Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

NASA Astrophysics Data System (ADS)

Wibowo, Wahyu; Wene, Chatrien; Budiantara, I. Nyoman; Permatasari, Erma Oktania

2017-03-01

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as nonparametric predictor and the others are parametric predictors. The result shows that the multiresponse semiparametric regression can be applied well as indicate by the high coefficient determination, 90 percent.

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…

Statistical methods and regression analysis of stratospheric ozone and meteorological variables in Isfahan

NASA Astrophysics Data System (ADS)

Hassanzadeh, S.; Hosseinibalam, F.; Omidvari, M.

2008-04-01

Data of seven meteorological variables (relative humidity, wet temperature, dry temperature, maximum temperature, minimum temperature, ground temperature and sun radiation time) and ozone values have been used for statistical analysis. Meteorological variables and ozone values were analyzed using both multiple linear regression and principal component methods. Data for the period 1999-2004 are analyzed jointly using both methods. For all periods, temperature dependent variables were highly correlated, but were all negatively correlated with relative humidity. Multiple regression analysis was used to fit the meteorological variables using the meteorological variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to obtain subsets of the predictor variables to be included in the linear regression model of the meteorological variables. In 1999, 2001 and 2002 one of the meteorological variables was weakly influenced predominantly by the ozone concentrations. However, the model did not predict that the meteorological variables for the year 2000 were not influenced predominantly by the ozone concentrations that point to variation in sun radiation. This could be due to other factors that were not explicitly considered in this study.

Modified Regression Correlation Coefficient for Poisson Regression Model

NASA Astrophysics Data System (ADS)

Kaengthong, Nattacha; Domthong, Uthumporn

2017-09-01

This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

Multiple regression for physiological data analysis: the problem of multicollinearity.

PubMed

Slinker, B K; Glantz, S A

1985-07-01

Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

NASA Astrophysics Data System (ADS)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Introduction to statistical modelling 2: categorical variables and interactions in linear regression.

PubMed

Lunt, Mark

2015-07-01

In the first article in this series we explored the use of linear regression to predict an outcome variable from a number of predictive factors. It assumed that the predictive factors were measured on an interval scale. However, this article shows how categorical variables can also be included in a linear regression model, enabling predictions to be made separately for different groups and allowing for testing the hypothesis that the outcome differs between groups. The use of interaction terms to measure whether the effect of a particular predictor variable differs between groups is also explained. An alternative approach to testing the difference between groups of the effect of a given predictor, which consists of measuring the effect in each group separately and seeing whether the statistical significance differs between the groups, is shown to be misleading. © The Author 2013. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Causal relationship model between variables using linear regression to improve professional commitment of lecturer

NASA Astrophysics Data System (ADS)

Setyaningsih, S.

2017-01-01

The main element to build a leading university requires lecturer commitment in a professional manner. Commitment is measured through willpower, loyalty, pride, loyalty, and integrity as a professional lecturer. A total of 135 from 337 university lecturers were sampled to collect data. Data were analyzed using validity and reliability test and multiple linear regression. Many studies have found a link on the commitment of lecturers, but the basic cause of the causal relationship is generally neglected. These results indicate that the professional commitment of lecturers affected by variables empowerment, academic culture, and trust. The relationship model between variables is composed of three substructures. The first substructure consists of endogenous variables professional commitment and exogenous three variables, namely the academic culture, empowerment and trust, as well as residue variable ɛ y . The second substructure consists of one endogenous variable that is trust and two exogenous variables, namely empowerment and academic culture and the residue variable ɛ 3. The third substructure consists of one endogenous variable, namely the academic culture and exogenous variables, namely empowerment as well as residue variable ɛ 2. Multiple linear regression was used in the path model for each substructure. The results showed that the hypothesis has been proved and these findings provide empirical evidence that increasing the variables will have an impact on increasing the professional commitment of the lecturers.

Post-processing through linear regression

NASA Astrophysics Data System (ADS)

van Schaeybroeck, B.; Vannitsem, S.

2011-03-01

Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

Regression Methods for Categorical Dependent Variables: Effects on a Model of Student College Choice

ERIC Educational Resources Information Center

Rapp, Kelly E.

2012-01-01

The use of categorical dependent variables with the classical linear regression model (CLRM) violates many of the model's assumptions and may result in biased estimates (Long, 1997; O'Connell, Goldstein, Rogers, & Peng, 2008). Many dependent variables of interest to educational researchers (e.g., professorial rank, educational attainment) are…

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

A Linear Regression Model Identifying the Primary Factors Contributing to Maintenance Man Hours for the C-17 Globemaster III in the Air National Guard

DTIC Science & Technology

2012-06-15

Maintenance AFSCs ................................................................................................. 14 2. Variation Inflation Factors...total variability in the data. It is an indication of how much of the   20    variation in the data can be accounted for in the regression model. In... Variation Inflation Factors for each independent variable (predictor) as regressed against all of the other independent variables in the model. The

Understanding logistic regression analysis.

PubMed

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

Independent contrasts and PGLS regression estimators are equivalent.

PubMed

Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

2012-05-01

We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

Identifying the Factors That Influence Change in SEBD Using Logistic Regression Analysis

ERIC Educational Resources Information Center

Camilleri, Liberato; Cefai, Carmel

2013-01-01

Multiple linear regression and ANOVA models are widely used in applications since they provide effective statistical tools for assessing the relationship between a continuous dependent variable and several predictors. However these models rely heavily on linearity and normality assumptions and they do not accommodate categorical dependent…

[Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

PubMed

Ling, Ru; Liu, Jiawang

2011-12-01

To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Ryazanov, S. S.; Kolcova, T. G.; Grigoryan, B. R.

2018-03-01

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.

Regression assumptions in clinical psychology research practice-a systematic review of common misconceptions.

PubMed

Ernst, Anja F; Albers, Casper J

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.

Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions

PubMed Central

Ernst, Anja F.

2017-01-01

Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking. PMID:28533971

Revisiting the Scale-Invariant, Two-Dimensional Linear Regression Method

ERIC Educational Resources Information Center

Patzer, A. Beate C.; Bauer, Hans; Chang, Christian; Bolte, Jan; Su¨lzle, Detlev

2018-01-01

The scale-invariant way to analyze two-dimensional experimental and theoretical data with statistical errors in both the independent and dependent variables is revisited by using what we call the triangular linear regression method. This is compared to the standard least-squares fit approach by applying it to typical simple sets of example data…

On the null distribution of Bayes factors in linear regression

USDA-ARS?s Scientific Manuscript database

We show that under the null, the 2 log (Bayes factor) is asymptotically distributed as a weighted sum of chi-squared random variables with a shifted mean. This claim holds for Bayesian multi-linear regression with a family of conjugate priors, namely, the normal-inverse-gamma prior, the g-prior, and...

Quantile regression models of animal habitat relationships

USGS Publications Warehouse

Cade, Brian S.

2003-01-01

Typically, all factors that limit an organism are not measured and included in statistical models used to investigate relationships with their environment. If important unmeasured variables interact multiplicatively with the measured variables, the statistical models often will have heterogeneous response distributions with unequal variances. Quantile regression is an approach for estimating the conditional quantiles of a response variable distribution in the linear model, providing a more complete view of possible causal relationships between variables in ecological processes. Chapter 1 introduces quantile regression and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of estimates for homogeneous and heterogeneous regression models. Chapter 2 evaluates performance of quantile rankscore tests used for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). A permutation F test maintained better Type I errors than the Chi-square T test for models with smaller n, greater number of parameters p, and more extreme quantiles τ. Both versions of the test required weighting to maintain correct Type I errors when there was heterogeneity under the alternative model. An example application related trout densities to stream channel width:depth. Chapter 3 evaluates a drop in dispersion, F-ratio like permutation test for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). Chapter 4 simulates from a large (N = 10,000) finite population representing grid areas on a landscape to demonstrate various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable (spatially and not spatially structured). Depending on whether interactions of the measured habitat and unmeasured variable were negative (interference interactions) or positive (facilitation interactions), either upper (τ > 0.5) or lower (τ < 0.5) quantile regression parameters were less biased than mean rate parameters. Sampling (n = 20 - 300) simulations demonstrated that confidence intervals constructed by inverting rankscore tests provided valid coverage of these biased parameters. Quantile regression was used to estimate effects of physical habitat resources on a bivalve mussel (Macomona liliana) in a New Zealand harbor by modeling the spatial trend surface as a cubic polynomial of location coordinates.

Transmission of linear regression patterns between time series: From relationship in time series to complex networks

NASA Astrophysics Data System (ADS)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

PubMed

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression

NASA Astrophysics Data System (ADS)

Martínez-Fernández, J.; Chuvieco, E.; Koutsias, N.

2013-02-01

Humans are responsible for most forest fires in Europe, but anthropogenic factors behind these events are still poorly understood. We tried to identify the driving factors of human-caused fire occurrence in Spain by applying two different statistical approaches. Firstly, assuming stationary processes for the whole country, we created models based on multiple linear regression and binary logistic regression to find factors associated with fire density and fire presence, respectively. Secondly, we used geographically weighted regression (GWR) to better understand and explore the local and regional variations of those factors behind human-caused fire occurrence. The number of human-caused fires occurring within a 25-yr period (1983-2007) was computed for each of the 7638 Spanish mainland municipalities, creating a binary variable (fire/no fire) to develop logistic models, and a continuous variable (fire density) to build standard linear regression models. A total of 383 657 fires were registered in the study dataset. The binary logistic model, which estimates the probability of having/not having a fire, successfully classified 76.4% of the total observations, while the ordinary least squares (OLS) regression model explained 53% of the variation of the fire density patterns (adjusted R2 = 0.53). Both approaches confirmed, in addition to forest and climatic variables, the importance of variables related with agrarian activities, land abandonment, rural population exodus and developmental processes as underlying factors of fire occurrence. For the GWR approach, the explanatory power of the GW linear model for fire density using an adaptive bandwidth increased from 53% to 67%, while for the GW logistic model the correctly classified observations improved only slightly, from 76.4% to 78.4%, but significantly according to the corrected Akaike Information Criterion (AICc), from 3451.19 to 3321.19. The results from GWR indicated a significant spatial variation in the local parameter estimates for all the variables and an important reduction of the autocorrelation in the residuals of the GW linear model. Despite the fitting improvement of local models, GW regression, more than an alternative to "global" or traditional regression modelling, seems to be a valuable complement to explore the non-stationary relationships between the response variable and the explanatory variables. The synergy of global and local modelling provides insights into fire management and policy and helps further our understanding of the fire problem over large areas while at the same time recognizing its local character.

Multiple regression technique for Pth degree polynominals with and without linear cross products

NASA Technical Reports Server (NTRS)

Davis, J. W.

1973-01-01

A multiple regression technique was developed by which the nonlinear behavior of specified independent variables can be related to a given dependent variable. The polynomial expression can be of Pth degree and can incorporate N independent variables. Two cases are treated such that mathematical models can be studied both with and without linear cross products. The resulting surface fits can be used to summarize trends for a given phenomenon and provide a mathematical relationship for subsequent analysis. To implement this technique, separate computer programs were developed for the case without linear cross products and for the case incorporating such cross products which evaluate the various constants in the model regression equation. In addition, the significance of the estimated regression equation is considered and the standard deviation, the F statistic, the maximum absolute percent error, and the average of the absolute values of the percent of error evaluated. The computer programs and their manner of utilization are described. Sample problems are included to illustrate the use and capability of the technique which show the output formats and typical plots comparing computer results to each set of input data.

Linear Modeling and Evaluation of Controls on Flow Response in Western Post-Fire Watersheds

NASA Astrophysics Data System (ADS)

Saxe, S.; Hogue, T. S.; Hay, L.

2015-12-01

This research investigates the impact of wildfires on watershed flow regimes throughout the western United States, specifically focusing on evaluation of fire events within specified subregions and determination of the impact of climate and geophysical variables in post-fire flow response. Fire events were collected through federal and state-level databases and streamflow data were collected from U.S. Geological Survey stream gages. 263 watersheds were identified with at least 10 years of continuous pre-fire daily streamflow records and 5 years of continuous post-fire daily flow records. For each watershed, percent changes in runoff ratio (RO), annual seven day low-flows (7Q2) and annual seven day high-flows (7Q10) were calculated from pre- to post-fire. Numerous independent variables were identified for each watershed and fire event, including topographic, land cover, climate, burn severity, and soils data. The national watersheds were divided into five regions through K-clustering and a lasso linear regression model, applying the Leave-One-Out calibration method, was calculated for each region. Nash-Sutcliffe Efficiency (NSE) was used to determine the accuracy of the resulting models. The regions encompassing the United States along and west of the Rocky Mountains, excluding the coastal watersheds, produced the most accurate linear models. The Pacific coast region models produced poor and inconsistent results, indicating that the regions need to be further subdivided. Presently, RO and HF response variables appear to be more easily modeled than LF. Results of linear regression modeling showed varying importance of watershed and fire event variables, with conflicting correlation between land cover types and soil types by region. The addition of further independent variables and constriction of current variables based on correlation indicators is ongoing and should allow for more accurate linear regression modeling.

Advanced Statistics for Exotic Animal Practitioners.

PubMed

Hodsoll, John; Hellier, Jennifer M; Ryan, Elizabeth G

2017-09-01

Correlation and regression assess the association between 2 or more variables. This article reviews the core knowledge needed to understand these analyses, moving from visual analysis in scatter plots through correlation, simple and multiple linear regression, and logistic regression. Correlation estimates the strength and direction of a relationship between 2 variables. Regression can be considered more general and quantifies the numerical relationships between an outcome and 1 or multiple variables in terms of a best-fit line, allowing predictions to be made. Each technique is discussed with examples and the statistical assumptions underlying their correct application. Copyright © 2017 Elsevier Inc. All rights reserved.

Estimation of streamflow, base flow, and nitrate-nitrogen loads in Iowa using multiple linear regression models

USGS Publications Warehouse

Schilling, K.E.; Wolter, C.F.

2005-01-01

Nineteen variables, including precipitation, soils and geology, land use, and basin morphologic characteristics, were evaluated to develop Iowa regression models to predict total streamflow (Q), base flow (Qb), storm flow (Qs) and base flow percentage (%Qb) in gauged and ungauged watersheds in the state. Discharge records from a set of 33 watersheds across the state for the 1980 to 2000 period were separated into Qb and Qs. Multiple linear regression found that 75.5 percent of long term average Q was explained by rainfall, sand content, and row crop percentage variables, whereas 88.5 percent of Qb was explained by these three variables plus permeability and floodplain area variables. Qs was explained by average rainfall and %Qb was a function of row crop percentage, permeability, and basin slope variables. Regional regression models developed for long term average Q and Qb were adapted to annual rainfall and showed good correlation between measured and predicted values. Combining the regression model for Q with an estimate of mean annual nitrate concentration, a map of potential nitrate loads in the state was produced. Results from this study have important implications for understanding geomorphic and land use controls on streamflow and base flow in Iowa watersheds and similar agriculture dominated watersheds in the glaciated Midwest. (JAWRA) (Copyright ?? 2005).

Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

Variables Associated with Communicative Participation in People with Multiple Sclerosis: A Regression Analysis

ERIC Educational Resources Information Center

Baylor, Carolyn; Yorkston, Kathryn; Bamer, Alyssa; Britton, Deanna; Amtmann, Dagmar

2010-01-01

Purpose: To explore variables associated with self-reported communicative participation in a sample (n = 498) of community-dwelling adults with multiple sclerosis (MS). Method: A battery of questionnaires was administered online or on paper per participant preference. Data were analyzed using multiple linear backward stepwise regression. The…

Logarithmic Transformations in Regression: Do You Transform Back Correctly?

ERIC Educational Resources Information Center

Dambolena, Ismael G.; Eriksen, Steven E.; Kopcso, David P.

2009-01-01

The logarithmic transformation is often used in regression analysis for a variety of purposes such as the linearization of a nonlinear relationship between two or more variables. We have noticed that when this transformation is applied to the response variable, the computation of the point estimate of the conditional mean of the original response…

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

NASA Technical Reports Server (NTRS)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

Explicit criteria for prioritization of cataract surgery

PubMed Central

Ma Quintana, José; Escobar, Antonio; Bilbao, Amaia

2006-01-01

Background Consensus techniques have been used previously to create explicit criteria to prioritize cataract extraction; however, the appropriateness of the intervention was not included explicitly in previous studies. We developed a prioritization tool for cataract extraction according to the RAND method. Methods Criteria were developed using a modified Delphi panel judgment process. A panel of 11 ophthalmologists was assembled. Ratings were analyzed regarding the level of agreement among panelists. We studied the effect of all variables on the final panel score using general linear and logistic regression models. Priority scoring systems were developed by means of optimal scaling and general linear models. The explicit criteria developed were summarized by means of regression tree analysis. Results Eight variables were considered to create the indications. Of the 310 indications that the panel evaluated, 22.6% were considered high priority, 52.3% intermediate priority, and 25.2% low priority. Agreement was reached for 31.9% of the indications and disagreement for 0.3%. Logistic regression and general linear models showed that the preoperative visual acuity of the cataractous eye, visual function, and anticipated visual acuity postoperatively were the most influential variables. Alternative and simple scoring systems were obtained by optimal scaling and general linear models where the previous variables were also the most important. The decision tree also shows the importance of the previous variables and the appropriateness of the intervention. Conclusion Our results showed acceptable validity as an evaluation and management tool for prioritizing cataract extraction. It also provides easy algorithms for use in clinical practice. PMID:16512893

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

PubMed

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

NASA Astrophysics Data System (ADS)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

A gentle introduction to quantile regression for ecologists

USGS Publications Warehouse

Cade, B.S.; Noon, B.R.

2003-01-01

Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a more complete view of possible causal relationships between variables in ecological processes. Typically, all the factors that affect ecological processes are not measured and included in the statistical models used to investigate relationships between variables associated with those processes. As a consequence, there may be a weak or no predictive relationship between the mean of the response variable (y) distribution and the measured predictive factors (X). Yet there may be stronger, useful predictive relationships with other parts of the response variable distribution. This primer relates quantile regression estimates to prediction intervals in parametric error distribution regression models (eg least squares), and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of the estimates for homogeneous and heterogeneous regression models.

Introduction to the use of regression models in epidemiology.

PubMed

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

Non-Linear Relationship between Economic Growth and CO2 Emissions in China: An Empirical Study Based on Panel Smooth Transition Regression Models

PubMed Central

Wang, Zheng-Xin; Hao, Peng; Yao, Pei-Yi

2017-01-01

The non-linear relationship between provincial economic growth and carbon emissions is investigated by using panel smooth transition regression (PSTR) models. The research indicates that, on the condition of separately taking Gross Domestic Product per capita (GDPpc), energy structure (Es), and urbanisation level (Ul) as transition variables, three models all reject the null hypothesis of a linear relationship, i.e., a non-linear relationship exists. The results show that the three models all contain only one transition function but different numbers of location parameters. The model taking GDPpc as the transition variable has two location parameters, while the other two models separately considering Es and Ul as the transition variables both contain one location parameter. The three models applied in the study all favourably describe the non-linear relationship between economic growth and CO2 emissions in China. It also can be seen that the conversion rate of the influence of Ul on per capita CO2 emissions is significantly higher than those of GDPpc and Es on per capita CO2 emissions. PMID:29236083

Non-Linear Relationship between Economic Growth and CO₂ Emissions in China: An Empirical Study Based on Panel Smooth Transition Regression Models.

PubMed

Wang, Zheng-Xin; Hao, Peng; Yao, Pei-Yi

2017-12-13

The non-linear relationship between provincial economic growth and carbon emissions is investigated by using panel smooth transition regression (PSTR) models. The research indicates that, on the condition of separately taking Gross Domestic Product per capita (GDPpc), energy structure (Es), and urbanisation level (Ul) as transition variables, three models all reject the null hypothesis of a linear relationship, i.e., a non-linear relationship exists. The results show that the three models all contain only one transition function but different numbers of location parameters. The model taking GDPpc as the transition variable has two location parameters, while the other two models separately considering Es and Ul as the transition variables both contain one location parameter. The three models applied in the study all favourably describe the non-linear relationship between economic growth and CO₂ emissions in China. It also can be seen that the conversion rate of the influence of Ul on per capita CO₂ emissions is significantly higher than those of GDPpc and Es on per capita CO₂ emissions.

Aspects of porosity prediction using multivariate linear regression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Byrnes, A.P.; Wilson, M.D.

1991-03-01

Highly accurate multiple linear regression models have been developed for sandstones of diverse compositions. Porosity reduction or enhancement processes are controlled by the fundamental variables, Pressure (P), Temperature (T), Time (t), and Composition (X), where composition includes mineralogy, size, sorting, fluid composition, etc. The multiple linear regression equation, of which all linear porosity prediction models are subsets, takes the generalized form: Porosity = C{sub 0} + C{sub 1}(P) + C{sub 2}(T) + C{sub 3}(X) + C{sub 4}(t) + C{sub 5}(PT) + C{sub 6}(PX) + C{sub 7}(Pt) + C{sub 8}(TX) + C{sub 9}(Tt) + C{sub 10}(Xt) + C{sub 11}(PTX) + C{submore » 12}(PXt) + C{sub 13}(PTt) + C{sub 14}(TXt) + C{sub 15}(PTXt). The first four primary variables are often interactive, thus requiring terms involving two or more primary variables (the form shown implies interaction and not necessarily multiplication). The final terms used may also involve simple mathematic transforms such as log X, e{sup T}, X{sup 2}, or more complex transformations such as the Time-Temperature Index (TTI). The X term in the equation above represents a suite of compositional variable and, therefore, a fully expanded equation may include a series of terms incorporating these variables. Numerous published bivariate porosity prediction models involving P (or depth) or Tt (TTI) are effective to a degree, largely because of the high degree of colinearity between p and TTI. However, all such bivariate models ignore the unique contributions of P and Tt, as well as various X terms. These simpler models become poor predictors in regions where colinear relations change, were important variables have been ignored, or where the database does not include a sufficient range or weight distribution for the critical variables.« less

Relationship between rice yield and climate variables in southwest Nigeria using multiple linear regression and support vector machine analysis

NASA Astrophysics Data System (ADS)

Oguntunde, Philip G.; Lischeid, Gunnar; Dietrich, Ottfried

2018-03-01

This study examines the variations of climate variables and rice yield and quantifies the relationships among them using multiple linear regression, principal component analysis, and support vector machine (SVM) analysis in southwest Nigeria. The climate and yield data used was for a period of 36 years between 1980 and 2015. Similar to the observed decrease ( P < 0.001) in rice yield, pan evaporation, solar radiation, and wind speed declined significantly. Eight principal components exhibited an eigenvalue > 1 and explained 83.1% of the total variance of predictor variables. The SVM regression function using the scores of the first principal component explained about 75% of the variance in rice yield data and linear regression about 64%. SVM regression between annual solar radiation values and yield explained 67% of the variance. Only the first component of the principal component analysis (PCA) exhibited a clear long-term trend and sometimes short-term variance similar to that of rice yield. Short-term fluctuations of the scores of the PC1 are closely coupled to those of rice yield during the 1986-1993 and the 2006-2013 periods thereby revealing the inter-annual sensitivity of rice production to climate variability. Solar radiation stands out as the climate variable of highest influence on rice yield, and the influence was especially strong during monsoon and post-monsoon periods, which correspond to the vegetative, booting, flowering, and grain filling stages in the study area. The outcome is expected to provide more in-depth regional-specific climate-rice linkage for screening of better cultivars that can positively respond to future climate fluctuations as well as providing information that may help optimized planting dates for improved radiation use efficiency in the study area.

Clustering performance comparison using K-means and expectation maximization algorithms.

PubMed

Jung, Yong Gyu; Kang, Min Soo; Heo, Jun

2014-11-14

Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K -means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K -means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.

Linear regression in astronomy. II

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

Are your covariates under control? How normalization can re-introduce covariate effects.

PubMed

Pain, Oliver; Dudbridge, Frank; Ronald, Angelica

2018-04-30

Many statistical tests rely on the assumption that the residuals of a model are normally distributed. Rank-based inverse normal transformation (INT) of the dependent variable is one of the most popular approaches to satisfy the normality assumption. When covariates are included in the analysis, a common approach is to first adjust for the covariates and then normalize the residuals. This study investigated the effect of regressing covariates against the dependent variable and then applying rank-based INT to the residuals. The correlation between the dependent variable and covariates at each stage of processing was assessed. An alternative approach was tested in which rank-based INT was applied to the dependent variable before regressing covariates. Analyses based on both simulated and real data examples demonstrated that applying rank-based INT to the dependent variable residuals after regressing out covariates re-introduces a linear correlation between the dependent variable and covariates, increasing type-I errors and reducing power. On the other hand, when rank-based INT was applied prior to controlling for covariate effects, residuals were normally distributed and linearly uncorrelated with covariates. This latter approach is therefore recommended in situations were normality of the dependent variable is required.

CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS

PubMed Central

Zhong, Wenxuan; Zhang, Tingting; Zhu, Yu; Liu, Jun S.

2012-01-01

In this article, a stepwise procedure, correlation pursuit (COP), is developed for variable selection under the sufficient dimension reduction framework, in which the response variable Y is influenced by the predictors X1, X2, …, Xp through an unknown function of a few linear combinations of them. Unlike linear stepwise regression, COP does not impose a special form of relationship (such as linear) between the response variable and the predictor variables. The COP procedure selects variables that attain the maximum correlation between the transformed response and the linear combination of the variables. Various asymptotic properties of the COP procedure are established, and in particular, its variable selection performance under diverging number of predictors and sample size has been investigated. The excellent empirical performance of the COP procedure in comparison with existing methods are demonstrated by both extensive simulation studies and a real example in functional genomics. PMID:23243388

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

Regression: The Apple Does Not Fall Far From the Tree.

PubMed

Vetter, Thomas R; Schober, Patrick

2018-05-15

Researchers and clinicians are frequently interested in either: (1) assessing whether there is a relationship or association between 2 or more variables and quantifying this association; or (2) determining whether 1 or more variables can predict another variable. The strength of such an association is mainly described by the correlation. However, regression analysis and regression models can be used not only to identify whether there is a significant relationship or association between variables but also to generate estimations of such a predictive relationship between variables. This basic statistical tutorial discusses the fundamental concepts and techniques related to the most common types of regression analysis and modeling, including simple linear regression, multiple regression, logistic regression, ordinal regression, and Poisson regression, as well as the common yet often underrecognized phenomenon of regression toward the mean. The various types of regression analysis are powerful statistical techniques, which when appropriately applied, can allow for the valid interpretation of complex, multifactorial data. Regression analysis and models can assess whether there is a relationship or association between 2 or more observed variables and estimate the strength of this association, as well as determine whether 1 or more variables can predict another variable. Regression is thus being applied more commonly in anesthesia, perioperative, critical care, and pain research. However, it is crucial to note that regression can identify plausible risk factors; it does not prove causation (a definitive cause and effect relationship). The results of a regression analysis instead identify independent (predictor) variable(s) associated with the dependent (outcome) variable. As with other statistical methods, applying regression requires that certain assumptions be met, which can be tested with specific diagnostics.

Non-Linear Approach in Kinesiology Should Be Preferred to the Linear--A Case of Basketball.

PubMed

Trninić, Marko; Jeličić, Mario; Papić, Vladan

2015-07-01

In kinesiology, medicine, biology and psychology, in which research focus is on dynamical self-organized systems, complex connections exist between variables. Non-linear nature of complex systems has been discussed and explained by the example of non-linear anthropometric predictors of performance in basketball. Previous studies interpreted relations between anthropometric features and measures of effectiveness in basketball by (a) using linear correlation models, and by (b) including all basketball athletes in the same sample of participants regardless of their playing position. In this paper the significance and character of linear and non-linear relations between simple anthropometric predictors (AP) and performance criteria consisting of situation-related measures of effectiveness (SE) in basketball were determined and evaluated. The sample of participants consisted of top-level junior basketball players divided in three groups according to their playing time (8 minutes and more per game) and playing position: guards (N = 42), forwards (N = 26) and centers (N = 40). Linear (general model) and non-linear (general model) regression models were calculated simultaneously and separately for each group. The conclusion is viable: non-linear regressions are frequently superior to linear correlations when interpreting actual association logic among research variables.

The discovery of indicator variables for QSAR using inductive logic programming

NASA Astrophysics Data System (ADS)

King, Ross D.; Srinivasan, Ashwin

1997-11-01

A central problem in forming accurate regression equations in QSAR studies isthe selection of appropriate descriptors for the compounds under study. Wedescribe a novel procedure for using inductive logic programming (ILP) todiscover new indicator variables (attributes) for QSAR problems, and show thatthese improve the accuracy of the derived regression equations. ILP techniqueshave previously been shown to work well on drug design problems where thereis a large structural component or where clear comprehensible rules arerequired. However, ILP techniques have had the disadvantage of only being ableto make qualitative predictions (e.g. active, inactive) and not to predictreal numbers (regression). We unify ILP and linear regression techniques togive a QSAR method that has the strength of ILP at describing stericstructure, with the familiarity and power of linear regression. We evaluatedthe utility of this new QSAR technique by examining the prediction ofbiological activity with and without the addition of new structural indicatorvariables formed by ILP. In three out of five datasets examined the additionof ILP variables produced statistically better results (P < 0.01) over theoriginal description. The new ILP variables did not increase the overallcomplexity of the derived QSAR equations and added insight into possiblemechanisms of action. We conclude that ILP can aid in the process of drugdesign.

Comprehensive ripeness-index for prediction of ripening level in mangoes by multivariate modelling of ripening behaviour

NASA Astrophysics Data System (ADS)

Eyarkai Nambi, Vijayaram; Thangavel, Kuladaisamy; Manickavasagan, Annamalai; Shahir, Sultan

2017-01-01

Prediction of ripeness level in climacteric fruits is essential for post-harvest handling. An index capable of predicting ripening level with minimum inputs would be highly beneficial to the handlers, processors and researchers in fruit industry. A study was conducted with Indian mango cultivars to develop a ripeness index and associated model. Changes in physicochemical, colour and textural properties were measured throughout the ripening period and the period was classified into five stages (unripe, early ripe, partially ripe, ripe and over ripe). Multivariate regression techniques like partial least square regression, principal component regression and multi linear regression were compared and evaluated for its prediction. Multi linear regression model with 12 parameters was found more suitable in ripening prediction. Scientific variable reduction method was adopted to simplify the developed model. Better prediction was achieved with either 2 or 3 variables (total soluble solids, colour and acidity). Cross validation was done to increase the robustness and it was found that proposed ripening index was more effective in prediction of ripening stages. Three-variable model would be suitable for commercial applications where reasonable accuracies are sufficient. However, 12-variable model can be used to obtain more precise results in research and development applications.

Regression dilution bias: tools for correction methods and sample size calculation.

PubMed

Berglund, Lars

2012-08-01

Random errors in measurement of a risk factor will introduce downward bias of an estimated association to a disease or a disease marker. This phenomenon is called regression dilution bias. A bias correction may be made with data from a validity study or a reliability study. In this article we give a non-technical description of designs of reliability studies with emphasis on selection of individuals for a repeated measurement, assumptions of measurement error models, and correction methods for the slope in a simple linear regression model where the dependent variable is a continuous variable. Also, we describe situations where correction for regression dilution bias is not appropriate. The methods are illustrated with the association between insulin sensitivity measured with the euglycaemic insulin clamp technique and fasting insulin, where measurement of the latter variable carries noticeable random error. We provide software tools for estimation of a corrected slope in a simple linear regression model assuming data for a continuous dependent variable and a continuous risk factor from a main study and an additional measurement of the risk factor in a reliability study. Also, we supply programs for estimation of the number of individuals needed in the reliability study and for choice of its design. Our conclusion is that correction for regression dilution bias is seldom applied in epidemiological studies. This may cause important effects of risk factors with large measurement errors to be neglected.

Modelling of binary logistic regression for obesity among secondary students in a rural area of Kedah

NASA Astrophysics Data System (ADS)

Kamaruddin, Ainur Amira; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

2014-07-01

Logistic regression analysis examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence. Logistic regression, also called a logit model, is a statistical procedure used to model dichotomous outcomes. In the logit model the log odds of the dichotomous outcome is modeled as a linear combination of the predictor variables. The log odds ratio in logistic regression provides a description of the probabilistic relationship of the variables and the outcome. In conducting logistic regression, selection procedures are used in selecting important predictor variables, diagnostics are used to check that assumptions are valid which include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers and a test statistic is calculated to determine the aptness of the model. This study used the binary logistic regression model to investigate overweight and obesity among rural secondary school students on the basis of their demographics profile, medical history, diet and lifestyle. The results indicate that overweight and obesity of students are influenced by obesity in family and the interaction between a student's ethnicity and routine meals intake. The odds of a student being overweight and obese are higher for a student having a family history of obesity and for a non-Malay student who frequently takes routine meals as compared to a Malay student.

Reliability of the Load-Velocity Relationship Obtained Through Linear and Polynomial Regression Models to Predict the One-Repetition Maximum Load.

PubMed

Pestaña-Melero, Francisco Luis; Haff, G Gregory; Rojas, Francisco Javier; Pérez-Castilla, Alejandro; García-Ramos, Amador

2017-12-18

This study aimed to compare the between-session reliability of the load-velocity relationship between (1) linear vs. polynomial regression models, (2) concentric-only vs. eccentric-concentric bench press variants, as well as (3) the within-participants vs. the between-participants variability of the velocity attained at each percentage of the one-repetition maximum (%1RM). The load-velocity relationship of 30 men (age: 21.2±3.8 y; height: 1.78±0.07 m, body mass: 72.3±7.3 kg; bench press 1RM: 78.8±13.2 kg) were evaluated by means of linear and polynomial regression models in the concentric-only and eccentric-concentric bench press variants in a Smith Machine. Two sessions were performed with each bench press variant. The main findings were: (1) first-order-polynomials (CV: 4.39%-4.70%) provided the load-velocity relationship with higher reliability than second-order-polynomials (CV: 4.68%-5.04%); (2) the reliability of the load-velocity relationship did not differ between the concentric-only and eccentric-concentric bench press variants; (3) the within-participants variability of the velocity attained at each %1RM was markedly lower than the between-participants variability. Taken together, these results highlight that, regardless of the bench press variant considered, the individual determination of the load-velocity relationship by a linear regression model could be recommended to monitor and prescribe the relative load in the Smith machine bench press exercise.

Reasons for Hierarchical Linear Modeling: A Reminder.

ERIC Educational Resources Information Center

Wang, Jianjun

1999-01-01

Uses examples of hierarchical linear modeling (HLM) at local and national levels to illustrate proper applications of HLM and dummy variable regression. Raises cautions about the circumstances under which hierarchical data do not need HLM. (SLD)

Cocaine Dependence Treatment Data: Methods for Measurement Error Problems With Predictors Derived From Stationary Stochastic Processes

PubMed Central

Guan, Yongtao; Li, Yehua; Sinha, Rajita

2011-01-01

In a cocaine dependence treatment study, we use linear and nonlinear regression models to model posttreatment cocaine craving scores and first cocaine relapse time. A subset of the covariates are summary statistics derived from baseline daily cocaine use trajectories, such as baseline cocaine use frequency and average daily use amount. These summary statistics are subject to estimation error and can therefore cause biased estimators for the regression coefficients. Unlike classical measurement error problems, the error we encounter here is heteroscedastic with an unknown distribution, and there are no replicates for the error-prone variables or instrumental variables. We propose two robust methods to correct for the bias: a computationally efficient method-of-moments-based method for linear regression models and a subsampling extrapolation method that is generally applicable to both linear and nonlinear regression models. Simulations and an application to the cocaine dependence treatment data are used to illustrate the efficacy of the proposed methods. Asymptotic theory and variance estimation for the proposed subsampling extrapolation method and some additional simulation results are described in the online supplementary material. PMID:21984854

Application of Multiregressive Linear Models, Dynamic Kriging Models and Neural Network Models to Predictive Maintenance of Hydroelectric Power Systems

NASA Astrophysics Data System (ADS)

Lucifredi, A.; Mazzieri, C.; Rossi, M.

2000-05-01

Since the operational conditions of a hydroelectric unit can vary within a wide range, the monitoring system must be able to distinguish between the variations of the monitored variable caused by variations of the operation conditions and those due to arising and progressing of failures and misoperations. The paper aims to identify the best technique to be adopted for the monitoring system. Three different methods have been implemented and compared. Two of them use statistical techniques: the first, the linear multiple regression, expresses the monitored variable as a linear function of the process parameters (independent variables), while the second, the dynamic kriging technique, is a modified technique of multiple linear regression representing the monitored variable as a linear combination of the process variables in such a way as to minimize the variance of the estimate error. The third is based on neural networks. Tests have shown that the monitoring system based on the kriging technique is not affected by some problems common to the other two models e.g. the requirement of a large amount of data for their tuning, both for training the neural network and defining the optimum plane for the multiple regression, not only in the system starting phase but also after a trivial operation of maintenance involving the substitution of machinery components having a direct impact on the observed variable. Or, in addition, the necessity of different models to describe in a satisfactory way the different ranges of operation of the plant. The monitoring system based on the kriging statistical technique overrides the previous difficulties: it does not require a large amount of data to be tuned and is immediately operational: given two points, the third can be immediately estimated; in addition the model follows the system without adapting itself to it. The results of the experimentation performed seem to indicate that a model based on a neural network or on a linear multiple regression is not optimal, and that a different approach is necessary to reduce the amount of work during the learning phase using, when available, all the information stored during the initial phase of the plant to build the reference baseline, elaborating, if it is the case, the raw information available. A mixed approach using the kriging statistical technique and neural network techniques could optimise the result.

Logic regression and its extensions.

PubMed

Schwender, Holger; Ruczinski, Ingo

2010-01-01

Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.

Early Parallel Activation of Semantics and Phonology in Picture Naming: Evidence from a Multiple Linear Regression MEG Study

PubMed Central

Miozzo, Michele; Pulvermüller, Friedemann; Hauk, Olaf

2015-01-01

The time course of brain activation during word production has become an area of increasingly intense investigation in cognitive neuroscience. The predominant view has been that semantic and phonological processes are activated sequentially, at about 150 and 200–400 ms after picture onset. Although evidence from prior studies has been interpreted as supporting this view, these studies were arguably not ideally suited to detect early brain activation of semantic and phonological processes. We here used a multiple linear regression approach to magnetoencephalography (MEG) analysis of picture naming in order to investigate early effects of variables specifically related to visual, semantic, and phonological processing. This was combined with distributed minimum-norm source estimation and region-of-interest analysis. Brain activation associated with visual image complexity appeared in occipital cortex at about 100 ms after picture presentation onset. At about 150 ms, semantic variables became physiologically manifest in left frontotemporal regions. In the same latency range, we found an effect of phonological variables in the left middle temporal gyrus. Our results demonstrate that multiple linear regression analysis is sensitive to early effects of multiple psycholinguistic variables in picture naming. Crucially, our results suggest that access to phonological information might begin in parallel with semantic processing around 150 ms after picture onset. PMID:25005037

Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.

ERIC Educational Resources Information Center

Vidal, Sherry

Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…

Variable Selection with Prior Information for Generalized Linear Models via the Prior LASSO Method.

PubMed

Jiang, Yuan; He, Yunxiao; Zhang, Heping

LASSO is a popular statistical tool often used in conjunction with generalized linear models that can simultaneously select variables and estimate parameters. When there are many variables of interest, as in current biological and biomedical studies, the power of LASSO can be limited. Fortunately, so much biological and biomedical data have been collected and they may contain useful information about the importance of certain variables. This paper proposes an extension of LASSO, namely, prior LASSO (pLASSO), to incorporate that prior information into penalized generalized linear models. The goal is achieved by adding in the LASSO criterion function an additional measure of the discrepancy between the prior information and the model. For linear regression, the whole solution path of the pLASSO estimator can be found with a procedure similar to the Least Angle Regression (LARS). Asymptotic theories and simulation results show that pLASSO provides significant improvement over LASSO when the prior information is relatively accurate. When the prior information is less reliable, pLASSO shows great robustness to the misspecification. We illustrate the application of pLASSO using a real data set from a genome-wide association study.

Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-04-29

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm(-1)) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic techniques application, such as Raman, ultraviolet-visible (UV-vis), or nuclear magnetic resonance (NMR) spectroscopies, can be greatly improved by an appropriate feature selection choice. Copyright © 2011 Elsevier B.V. All rights reserved.

Robust best linear estimator for Cox regression with instrumental variables in whole cohort and surrogates with additive measurement error in calibration sample

PubMed Central

Wang, Ching-Yun; Song, Xiao

2017-01-01

SUMMARY Biomedical researchers are often interested in estimating the effect of an environmental exposure in relation to a chronic disease endpoint. However, the exposure variable of interest may be measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies an additive measurement error model, but it may not have repeated measurements. The subset in which the surrogate variables are available is called a calibration sample. In addition to the surrogate variables that are available among the subjects in the calibration sample, we consider the situation when there is an instrumental variable available for all study subjects. An instrumental variable is correlated with the unobserved true exposure variable, and hence can be useful in the estimation of the regression coefficients. In this paper, we propose a nonparametric method for Cox regression using the observed data from the whole cohort. The nonparametric estimator is the best linear combination of a nonparametric correction estimator from the calibration sample and the difference of the naive estimators from the calibration sample and the whole cohort. The asymptotic distribution is derived, and the finite sample performance of the proposed estimator is examined via intensive simulation studies. The methods are applied to the Nutritional Biomarkers Study of the Women’s Health Initiative. PMID:27546625

Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting

NASA Astrophysics Data System (ADS)

Wu, Cheng; Zhen Yu, Jian

2018-03-01

Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS), Deming regression (DR), orthogonal distance regression (ODR), weighted ODR (WODR), and York regression (YR). We first introduce a new data generation scheme that employs the Mersenne twister (MT) pseudorandom number generator. The numerical simulations are also improved by (a) refining the parameterization of nonlinear measurement uncertainties, (b) inclusion of a linear measurement uncertainty, and (c) inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot) was developed to facilitate the implementation of error-in-variables regressions.

Estimating integrated variance in the presence of microstructure noise using linear regression

NASA Astrophysics Data System (ADS)

Holý, Vladimír

2017-07-01

Using financial high-frequency data for estimation of integrated variance of asset prices is beneficial but with increasing number of observations so-called microstructure noise occurs. This noise can significantly bias the realized variance estimator. We propose a method for estimation of the integrated variance robust to microstructure noise as well as for testing the presence of the noise. Our method utilizes linear regression in which realized variances estimated from different data subsamples act as dependent variable while the number of observations act as explanatory variable. We compare proposed estimator with other methods on simulated data for several microstructure noise structures.

A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression.

PubMed

Van Looy, Stijn; Verplancke, Thierry; Benoit, Dominique; Hoste, Eric; Van Maele, Georges; De Turck, Filip; Decruyenaere, Johan

2007-01-01

Tacrolimus is an important immunosuppressive drug for organ transplantation patients. It has a narrow therapeutic range, toxic side effects, and a blood concentration with wide intra- and interindividual variability. Hence, it is of the utmost importance to monitor tacrolimus blood concentration, thereby ensuring clinical effect and avoiding toxic side effects. Prediction models for tacrolimus blood concentration can improve clinical care by optimizing monitoring of these concentrations, especially in the initial phase after transplantation during intensive care unit (ICU) stay. This is the first study in the ICU in which support vector machines, as a new data modeling technique, are investigated and tested in their prediction capabilities of tacrolimus blood concentration. Linear support vector regression (SVR) and nonlinear radial basis function (RBF) SVR are compared with multiple linear regression (MLR). Tacrolimus blood concentrations, together with 35 other relevant variables from 50 liver transplantation patients, were extracted from our ICU database. This resulted in a dataset of 457 blood samples, on average between 9 and 10 samples per patient, finally resulting in a database of more than 16,000 data values. Nonlinear RBF SVR, linear SVR, and MLR were performed after selection of clinically relevant input variables and model parameters. Differences between observed and predicted tacrolimus blood concentrations were calculated. Prediction accuracy of the three methods was compared after fivefold cross-validation (Friedman test and Wilcoxon signed rank analysis). Linear SVR and nonlinear RBF SVR had mean absolute differences between observed and predicted tacrolimus blood concentrations of 2.31 ng/ml (standard deviation [SD] 2.47) and 2.38 ng/ml (SD 2.49), respectively. MLR had a mean absolute difference of 2.73 ng/ml (SD 3.79). The difference between linear SVR and MLR was statistically significant (p < 0.001). RBF SVR had the advantage of requiring only 2 input variables to perform this prediction in comparison to 15 and 16 variables needed by linear SVR and MLR, respectively. This is an indication of the superior prediction capability of nonlinear SVR. Prediction of tacrolimus blood concentration with linear and nonlinear SVR was excellent, and accuracy was superior in comparison with an MLR model.

Estimating Causal Effects with Ancestral Graph Markov Models

PubMed Central

Malinsky, Daniel; Spirtes, Peter

2017-01-01

We present an algorithm for estimating bounds on causal effects from observational data which combines graphical model search with simple linear regression. We assume that the underlying system can be represented by a linear structural equation model with no feedback, and we allow for the possibility of latent variables. Under assumptions standard in the causal search literature, we use conditional independence constraints to search for an equivalence class of ancestral graphs. Then, for each model in the equivalence class, we perform the appropriate regression (using causal structure information to determine which covariates to include in the regression) to estimate a set of possible causal effects. Our approach is based on the “IDA” procedure of Maathuis et al. (2009), which assumes that all relevant variables have been measured (i.e., no unmeasured confounders). We generalize their work by relaxing this assumption, which is often violated in applied contexts. We validate the performance of our algorithm on simulated data and demonstrate improved precision over IDA when latent variables are present. PMID:28217244

Some comparisons of complexity in dictionary-based and linear computational models.

PubMed

Gnecco, Giorgio; Kůrková, Věra; Sanguineti, Marcello

2011-03-01

Neural networks provide a more flexible approximation of functions than traditional linear regression. In the latter, one can only adjust the coefficients in linear combinations of fixed sets of functions, such as orthogonal polynomials or Hermite functions, while for neural networks, one may also adjust the parameters of the functions which are being combined. However, some useful properties of linear approximators (such as uniqueness, homogeneity, and continuity of best approximation operators) are not satisfied by neural networks. Moreover, optimization of parameters in neural networks becomes more difficult than in linear regression. Experimental results suggest that these drawbacks of neural networks are offset by substantially lower model complexity, allowing accuracy of approximation even in high-dimensional cases. We give some theoretical results comparing requirements on model complexity for two types of approximators, the traditional linear ones and so called variable-basis types, which include neural networks, radial, and kernel models. We compare upper bounds on worst-case errors in variable-basis approximation with lower bounds on such errors for any linear approximator. Using methods from nonlinear approximation and integral representations tailored to computational units, we describe some cases where neural networks outperform any linear approximator. Copyright © 2010 Elsevier Ltd. All rights reserved.

Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach

NASA Astrophysics Data System (ADS)

Bagirov, Adil M.; Mahmood, Arshad; Barton, Andrew

2017-05-01

This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889-2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations.

Analysis of the labor productivity of enterprises via quantile regression

NASA Astrophysics Data System (ADS)

Türkan, Semra

2017-07-01

In this study, we have analyzed the factors that affect the performance of Turkey's Top 500 Industrial Enterprises using quantile regression. The variable about labor productivity of enterprises is considered as dependent variable, the variableabout assets is considered as independent variable. The distribution of labor productivity of enterprises is right-skewed. If the dependent distribution is skewed, linear regression could not catch important aspects of the relationships between the dependent variable and its predictors due to modeling only the conditional mean. Hence, the quantile regression, which allows modelingany quantilesof the dependent distribution, including the median,appears to be useful. It examines whether relationships between dependent and independent variables are different for low, medium, and high percentiles. As a result of analyzing data, the effect of total assets is relatively constant over the entire distribution, except the upper tail. It hasa moderately stronger effect in the upper tail.

Simple, Efficient Estimators of Treatment Effects in Randomized Trials Using Generalized Linear Models to Leverage Baseline Variables

PubMed Central

Rosenblum, Michael; van der Laan, Mark J.

2010-01-01

Models, such as logistic regression and Poisson regression models, are often used to estimate treatment effects in randomized trials. These models leverage information in variables collected before randomization, in order to obtain more precise estimates of treatment effects. However, there is the danger that model misspecification will lead to bias. We show that certain easy to compute, model-based estimators are asymptotically unbiased even when the working model used is arbitrarily misspecified. Furthermore, these estimators are locally efficient. As a special case of our main result, we consider a simple Poisson working model containing only main terms; in this case, we prove the maximum likelihood estimate of the coefficient corresponding to the treatment variable is an asymptotically unbiased estimator of the marginal log rate ratio, even when the working model is arbitrarily misspecified. This is the log-linear analog of ANCOVA for linear models. Our results demonstrate one application of targeted maximum likelihood estimation. PMID:20628636

A multiple linear regression analysis of hot corrosion attack on a series of nickel base turbine alloys

NASA Technical Reports Server (NTRS)

Barrett, C. A.

1985-01-01

Multiple linear regression analysis was used to determine an equation for estimating hot corrosion attack for a series of Ni base cast turbine alloys. The U transform (i.e., 1/sin (% A/100) to the 1/2) was shown to give the best estimate of the dependent variable, y. A complete second degree equation is described for the centered" weight chemistries for the elements Cr, Al, Ti, Mo, W, Cb, Ta, and Co. In addition linear terms for the minor elements C, B, and Zr were added for a basic 47 term equation. The best reduced equation was determined by the stepwise selection method with essentially 13 terms. The Cr term was found to be the most important accounting for 60 percent of the explained variability hot corrosion attack.

A Bayesian Semiparametric Latent Variable Model for Mixed Responses

ERIC Educational Resources Information Center

Fahrmeir, Ludwig; Raach, Alexander

2007-01-01

In this paper we introduce a latent variable model (LVM) for mixed ordinal and continuous responses, where covariate effects on the continuous latent variables are modelled through a flexible semiparametric Gaussian regression model. We extend existing LVMs with the usual linear covariate effects by including nonparametric components for nonlinear…

Relationships of Measurement Error and Prediction Error in Observed-Score Regression

ERIC Educational Resources Information Center

Moses, Tim

2012-01-01

The focus of this paper is assessing the impact of measurement errors on the prediction error of an observed-score regression. Measures are presented and described for decomposing the linear regression's prediction error variance into parts attributable to the true score variance and the error variances of the dependent variable and the predictor…

Exhaustive Search for Sparse Variable Selection in Linear Regression

NASA Astrophysics Data System (ADS)

Igarashi, Yasuhiko; Takenaka, Hikaru; Nakanishi-Ohno, Yoshinori; Uemura, Makoto; Ikeda, Shiro; Okada, Masato

2018-04-01

We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.

Robust best linear estimator for Cox regression with instrumental variables in whole cohort and surrogates with additive measurement error in calibration sample.

PubMed

Wang, Ching-Yun; Song, Xiao

2016-11-01

Biomedical researchers are often interested in estimating the effect of an environmental exposure in relation to a chronic disease endpoint. However, the exposure variable of interest may be measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies an additive measurement error model, but it may not have repeated measurements. The subset in which the surrogate variables are available is called a calibration sample. In addition to the surrogate variables that are available among the subjects in the calibration sample, we consider the situation when there is an instrumental variable available for all study subjects. An instrumental variable is correlated with the unobserved true exposure variable, and hence can be useful in the estimation of the regression coefficients. In this paper, we propose a nonparametric method for Cox regression using the observed data from the whole cohort. The nonparametric estimator is the best linear combination of a nonparametric correction estimator from the calibration sample and the difference of the naive estimators from the calibration sample and the whole cohort. The asymptotic distribution is derived, and the finite sample performance of the proposed estimator is examined via intensive simulation studies. The methods are applied to the Nutritional Biomarkers Study of the Women's Health Initiative. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Temporal Synchronization Analysis for Improving Regression Modeling of Fecal Indicator Bacteria Levels

EPA Science Inventory

Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...

Statistical downscaling of precipitation using long short-term memory recurrent neural networks

NASA Astrophysics Data System (ADS)

Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

2017-11-01

Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.

Assessing risk factors for periodontitis using regression

NASA Astrophysics Data System (ADS)

Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa

2013-10-01

Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.

Efficacy of Social Media Adoption on Client Growth for Independent Management Consultants

DTIC Science & Technology

2017-02-01

design , a linear multiple regression with three predictor variables and one dependent variable per testing were used. Under those circumstances...regression test was used to compare the social media adoption of two groups on a single measure to determine if there was a statistical difference...number and types of social media platforms used and their influence on client growth was examined in this research design that used a descriptive

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models.

PubMed

Wiedermann, Wolfgang; Artner, Richard; von Eye, Alexander

2017-01-01

Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the homoscedasticity assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated assumptions is discussed.

Kendall-Theil Robust Line (KTRLine--version 1.0)-A Visual Basic Program for Calculating and Graphing Robust Nonparametric Estimates of Linear-Regression Coefficients Between Two Continuous Variables

USGS Publications Warehouse

Granato, Gregory E.

2006-01-01

The Kendall-Theil Robust Line software (KTRLine-version 1.0) is a Visual Basic program that may be used with the Microsoft Windows operating system to calculate parameters for robust, nonparametric estimates of linear-regression coefficients between two continuous variables. The KTRLine software was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration, for use in stochastic data modeling with local, regional, and national hydrologic data sets to develop planning-level estimates of potential effects of highway runoff on the quality of receiving waters. The Kendall-Theil robust line was selected because this robust nonparametric method is resistant to the effects of outliers and nonnormality in residuals that commonly characterize hydrologic data sets. The slope of the line is calculated as the median of all possible pairwise slopes between points. The intercept is calculated so that the line will run through the median of input data. A single-line model or a multisegment model may be specified. The program was developed to provide regression equations with an error component for stochastic data generation because nonparametric multisegment regression tools are not available with the software that is commonly used to develop regression models. The Kendall-Theil robust line is a median line and, therefore, may underestimate total mass, volume, or loads unless the error component or a bias correction factor is incorporated into the estimate. Regression statistics such as the median error, the median absolute deviation, the prediction error sum of squares, the root mean square error, the confidence interval for the slope, and the bias correction factor for median estimates are calculated by use of nonparametric methods. These statistics, however, may be used to formulate estimates of mass, volume, or total loads. The program is used to read a two- or three-column tab-delimited input file with variable names in the first row and data in subsequent rows. The user may choose the columns that contain the independent (X) and dependent (Y) variable. A third column, if present, may contain metadata such as the sample-collection location and date. The program screens the input files and plots the data. The KTRLine software is a graphical tool that facilitates development of regression models by use of graphs of the regression line with data, the regression residuals (with X or Y), and percentile plots of the cumulative frequency of the X variable, Y variable, and the regression residuals. The user may individually transform the independent and dependent variables to reduce heteroscedasticity and to linearize data. The program plots the data and the regression line. The program also prints model specifications and regression statistics to the screen. The user may save and print the regression results. The program can accept data sets that contain up to about 15,000 XY data points, but because the program must sort the array of all pairwise slopes, the program may be perceptibly slow with data sets that contain more than about 1,000 points.

A Demonstration of Regression False Positive Selection in Data Mining

ERIC Educational Resources Information Center

Pinder, Jonathan P.

2014-01-01

Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection ("type I errors") is…

An analysis of input errors in precipitation-runoff models using regression with errors in the independent variables

USGS Publications Warehouse

Troutman, Brent M.

1982-01-01

Errors in runoff prediction caused by input data errors are analyzed by treating precipitation-runoff models as regression (conditional expectation) models. Independent variables of the regression consist of precipitation and other input measurements; the dependent variable is runoff. In models using erroneous input data, prediction errors are inflated and estimates of expected storm runoff for given observed input variables are biased. This bias in expected runoff estimation results in biased parameter estimates if these parameter estimates are obtained by a least squares fit of predicted to observed runoff values. The problems of error inflation and bias are examined in detail for a simple linear regression of runoff on rainfall and for a nonlinear U.S. Geological Survey precipitation-runoff model. Some implications for flood frequency analysis are considered. A case study using a set of data from Turtle Creek near Dallas, Texas illustrates the problems of model input errors.

Variables Affecting Proficiency in English as a Second Language

ERIC Educational Resources Information Center

Santana, Josefina C.; García-Santillán, Arturo; Escalera-Chávez, Milka Elena

2017-01-01

This study explores different variables leading to proficiency in English as a second language. Level of English on a placement exam taken upon entering a private university in Mexico was correlated to several variables. Additionally, participants (N = 218) were asked their perception of their own proficiency. A linear regression and a one-factor…

Least Principal Components Analysis (LPCA): An Alternative to Regression Analysis.

ERIC Educational Resources Information Center

Olson, Jeffery E.

Often, all of the variables in a model are latent, random, or subject to measurement error, or there is not an obvious dependent variable. When any of these conditions exist, an appropriate method for estimating the linear relationships among the variables is Least Principal Components Analysis. Least Principal Components are robust, consistent,…

Daily commuting to work is not associated with variables of health.

PubMed

Mauss, Daniel; Jarczok, Marc N; Fischer, Joachim E

2016-01-01

Commuting to work is thought to have a negative impact on employee health. We tested the association of work commute and different variables of health in German industrial employees. Self-rated variables of an industrial cohort (n = 3805; 78.9 % male) including absenteeism, presenteeism and indices reflecting stress and well-being were assessed by a questionnaire. Fasting blood samples, heart-rate variability and anthropometric data were collected. Commuting was grouped into one of four categories: 0-19.9, 20-44.9, 45-59.9, ≥60 min travelling one way to work. Bivariate associations between commuting and all variables under study were calculated. Linear regression models tested this association further, controlling for potential confounders. Commuting was positively correlated with waist circumference and inversely with triglycerides. These associations did not remain statistically significant in linear regression models controlling for age, gender, marital status, and shiftwork. No other association with variables of physical, psychological, or mental health and well-being could be found. The results indicate that commuting to work has no significant impact on well-being and health of German industrial employees.

Improvement of Storm Forecasts Using Gridded Bayesian Linear Regression for Northeast United States

NASA Astrophysics Data System (ADS)

Yang, J.; Astitha, M.; Schwartz, C. S.

2017-12-01

Bayesian linear regression (BLR) is a post-processing technique in which regression coefficients are derived and used to correct raw forecasts based on pairs of observation-model values. This study presents the development and application of a gridded Bayesian linear regression (GBLR) as a new post-processing technique to improve numerical weather prediction (NWP) of rain and wind storm forecasts over northeast United States. Ten controlled variables produced from ten ensemble members of the National Center for Atmospheric Research (NCAR) real-time prediction system are used for a GBLR model. In the GBLR framework, leave-one-storm-out cross-validation is utilized to study the performances of the post-processing technique in a database composed of 92 storms. To estimate the regression coefficients of the GBLR, optimization procedures that minimize the systematic and random error of predicted atmospheric variables (wind speed, precipitation, etc.) are implemented for the modeled-observed pairs of training storms. The regression coefficients calculated for meteorological stations of the National Weather Service are interpolated back to the model domain. An analysis of forecast improvements based on error reductions during the storms will demonstrate the value of GBLR approach. This presentation will also illustrate how the variances are optimized for the training partition in GBLR and discuss the verification strategy for grid points where no observations are available. The new post-processing technique is successful in improving wind speed and precipitation storm forecasts using past event-based data and has the potential to be implemented in real-time.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

PubMed

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

A non-linear data mining parameter selection algorithm for continuous variables

PubMed Central

Razavi, Marianne; Brady, Sean

2017-01-01

In this article, we propose a new data mining algorithm, by which one can both capture the non-linearity in data and also find the best subset model. To produce an enhanced subset of the original variables, a preferred selection method should have the potential of adding a supplementary level of regression analysis that would capture complex relationships in the data via mathematical transformation of the predictors and exploration of synergistic effects of combined variables. The method that we present here has the potential to produce an optimal subset of variables, rendering the overall process of model selection more efficient. This algorithm introduces interpretable parameters by transforming the original inputs and also a faithful fit to the data. The core objective of this paper is to introduce a new estimation technique for the classical least square regression framework. This new automatic variable transformation and model selection method could offer an optimal and stable model that minimizes the mean square error and variability, while combining all possible subset selection methodology with the inclusion variable transformations and interactions. Moreover, this method controls multicollinearity, leading to an optimal set of explanatory variables. PMID:29131829

The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching.

PubMed

Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

2018-01-01

Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calculations, the quality of the control group is uncertain. In this paper, we present a statistics-based research in which we try to determine the relationship between the accuracy of the logistic regression model and the uncertainty of the dependent variable of the control group defined by propensity score matching. Our analyses show that there is a linear correlation between the fit of the logistic regression model and the uncertainty of the output variable. In certain cases, a latent binary explanatory variable can result in a relative error of up to 70% in the prediction of the outcome variable. The observed phenomenon calls the attention of analysts to an important point, which must be taken into account when deducting conclusions.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

Genetic Programming Transforms in Linear Regression Situations

NASA Astrophysics Data System (ADS)

Castillo, Flor; Kordon, Arthur; Villa, Carlos

The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape

PubMed Central

Coupé, Christophe

2018-01-01

As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for ‘difficult’ variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we assess a range of candidate distributions, including the Sichel, Delaporte, Box-Cox Green and Cole, and Box-Cox t distributions. We find that the Box-Cox t distribution, with appropriate modeling of its parameters, best fits the conditional distribution of phonemic inventory size. We finally discuss the specificities of phoneme counts, weak effects, and how GAMLSS should be considered for other linguistic variables. PMID:29713298

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape.

PubMed

Coupé, Christophe

2018-01-01

As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for 'difficult' variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we assess a range of candidate distributions, including the Sichel, Delaporte, Box-Cox Green and Cole, and Box-Cox t distributions. We find that the Box-Cox t distribution, with appropriate modeling of its parameters, best fits the conditional distribution of phonemic inventory size. We finally discuss the specificities of phoneme counts, weak effects, and how GAMLSS should be considered for other linguistic variables.

Food insecurity and CD4% Among HIV+ children in Gaborone, Botswana.

PubMed

Mendoza, Jason A; Matshaba, Mogomotsi; Makhanda, Jeremiah; Liu, Yan; Boitshwarelo, Matshwenyego; Anabwani, Gabriel M

2014-08-01

We investigated the association between household food insecurity (HFI) and CD4% among 2-6-year old HIV+ outpatients (n = 78) at the Botswana-Baylor Children's Clinical Center of Excellence in Gaborone, Botswana. HFI was assessed by a validated survey. CD4% data were abstracted from the medical record. We used multiple linear regression with CD4% (dependent variable), HFI (independent variable), and controlled for sociodemographic and clinical covariates. Multiple linear regression showed a significant main effect for HFI [beta = -0.6, 95% confidence interval (CI): -1.0 to -0.1] and child gender (beta = 5.6, 95% CI: 1.3 to 9.8). Alleviating food insecurity may improve pediatric HIV outcomes in Botswana and similar Sub-Saharan settings.

Evaluation of accuracy of linear regression models in predicting urban stormwater discharge characteristics.

PubMed

Madarang, Krish J; Kang, Joo-Hyon

2014-06-01

Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days (ADD), have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency's Storm Water Management Model (SWMM). The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid (TSS) discharge loads and event mean concentrations (EMC) were extracted. From these data, linear regression models were developed. R(2) and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Distributed Monitoring of the R(sup 2) Statistic for Linear Regression

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Das, Kamalika; Giannella, Chris R.

2011-01-01

The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and one or more dependent target variables. This problem becomes challenging for large scale data in a distributed computing environment when only a subset of instances is available at individual nodes and the local data changes frequently. Data centralization and periodic model recomputation can add high overhead to tasks like anomaly detection in such dynamic settings. Therefore, the goal is to develop techniques for monitoring and updating the model over the union of all nodes data in a communication-efficient fashion. Correctness guarantees on such techniques are also often highly desirable, especially in safety-critical application scenarios. In this paper we develop DReMo a distributed algorithm with very low resource overhead, for monitoring the quality of a regression model in terms of its coefficient of determination (R2 statistic). When the nodes collectively determine that R2 has dropped below a fixed threshold, the linear regression model is recomputed via a network-wide convergecast and the updated model is broadcast back to all nodes. We show empirically, using both synthetic and real data, that our proposed method is highly communication-efficient and scalable, and also provide theoretical guarantees on correctness.

Scale of association: hierarchical linear models and the measurement of ecological systems

Treesearch

Sean M. McMahon; Jeffrey M. Diez

2007-01-01

A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance-covariance parameters in hierarchically structured...

Influence of age on the correlations of hematological and biochemical variables with the stability of erythrocyte membrane in relation to sodium dodecyl sulfate.

PubMed

de Freitas, Mariana V; Marquez-Bernardes, Liandra F; de Arvelos, Letícia R; Paraíso, Lara F; Gonçalves E Oliveira, Ana Flávia M; Mascarenhas Netto, Rita de C; Neto, Morun Bernardino; Garrote-Filho, Mario S; de Souza, Paulo César A; Penha-Silva, Nilson

2014-10-01

To evaluate the influence of age on the relationships between biochemical and hematological variables and stability of erythrocyte membrane in relation to the sodium dodecyl sulfate (SDS) in population of 105 female volunteers between 20 and 90 years. The stability of RBC membrane was determined by non-linear regression of the dependency of the absorbance of hemoglobin released as a function of SDS concentration, represented by the half-transition point of the curve (D50) and the variation in the concentration of the detergent to promote lysis (dD). There was an age-dependent increase in the membrane stability in relation to SDS. Analyses by multiple linear regression showed that this stability increase is significantly related to the hematological variable red cell distribution width (RDW) and the biochemical variables blood albumin and cholesterol. The positive association between erythrocyte stability and RDW may reflect one possible mechanism involved in the clinical meaning of this hematological index.

Estimating basin lagtime and hydrograph-timing indexes used to characterize stormflows for runoff-quality analysis

USGS Publications Warehouse

Granato, Gregory E.

2012-01-01

A nationwide study to better define triangular-hydrograph statistics for use with runoff-quality and flood-flow studies was done by the U.S. Geological Survey (USGS) in cooperation with the Federal Highway Administration. Although the triangular hydrograph is a simple linear approximation, the cumulative distribution of stormflow with a triangular hydrograph is a curvilinear S-curve that closely approximates the cumulative distribution of stormflows from measured data. The temporal distribution of flow within a runoff event can be estimated using the basin lagtime, (which is the time from the centroid of rainfall excess to the centroid of the corresponding runoff hydrograph) and the hydrograph recession ratio (which is the ratio of the duration of the falling limb to the rising limb of the hydrograph). This report documents results of the study, methods used to estimate the variables, and electronic files that facilitate calculation of variables. Ten viable multiple-linear regression equations were developed to estimate basin lagtimes from readily determined drainage basin properties using data published in 37 stormflow studies. Regression equations using the basin lag factor (BLF, which is a variable calculated as the main-channel length, in miles, divided by the square root of the main-channel slope in feet per mile) and two variables describing development in the drainage basin were selected as the best candidates, because each equation explains about 70 percent of the variability in the data. The variables describing development are the USGS basin development factor (BDF, which is a function of the amount of channel modifications, storm sewers, and curb-and-gutter streets in a basin) and the total impervious area variable (IMPERV) in the basin. Two datasets were used to develop regression equations. The primary dataset included data from 493 sites that have values for the BLF, BDF, and IMPERV variables. This dataset was used to develop the best-fit regression equation using the BLF and BDF variables. The secondary dataset included data from 896 sites that have values for the BLF and IMPERV variables. This dataset was used to develop the best-fit regression equation using the BLF and IMPERV variables. Analysis of hydrograph recession ratios and basin characteristics for 41 sites indicated that recession ratios are random variables. Thus, recession ratios cannot be estimated quantitatively using multiple linear regression equations developed using the data available for these sites. The minimums of recession ratios for different streamgages are well characterized by a value of one. The most probable values and maximum values of recession ratios for different streamgages are, however, more variable than the minimums. The most probable values of recession ratios for the 41 streamgages analyzed ranged from 1.0 to 3.52 and had a median of 1.85. The maximum values ranged from 2.66 to 11.3 and had a median of 4.36.

The Use of Linear Instrumental Variables Methods in Health Services Research and Health Economics: A Cautionary Note

PubMed Central

Terza, Joseph V; Bradford, W David; Dismuke, Clara E

2008-01-01

Objective To investigate potential bias in the use of the conventional linear instrumental variables (IV) method for the estimation of causal effects in inherently nonlinear regression settings. Data Sources Smoking Supplement to the 1979 National Health Interview Survey, National Longitudinal Alcohol Epidemiologic Survey, and simulated data. Study Design Potential bias from the use of the linear IV method in nonlinear models is assessed via simulation studies and real world data analyses in two commonly encountered regression setting: (1) models with a nonnegative outcome (e.g., a count) and a continuous endogenous regressor; and (2) models with a binary outcome and a binary endogenous regressor. Principle Findings The simulation analyses show that substantial bias in the estimation of causal effects can result from applying the conventional IV method in inherently nonlinear regression settings. Moreover, the bias is not attenuated as the sample size increases. This point is further illustrated in the survey data analyses in which IV-based estimates of the relevant causal effects diverge substantially from those obtained with appropriate nonlinear estimation methods. Conclusions We offer this research as a cautionary note to those who would opt for the use of linear specifications in inherently nonlinear settings involving endogeneity. PMID:18546544

Secondary Students' Considerations of Variability in Measurement Activities Based on Authentic Practices

ERIC Educational Resources Information Center

Dierdorp, Adri; Bakker, Arthur; Ben-Zvi, Dani; Makar, Katie

2017-01-01

Measurement activities were designed in this study on the basis of authentic professional practices in which linear regression is used, to study considerations of variability by students in Grade 12 (aged 17-18). The question addressed in this article is: In what ways do secondary students consider variability within these measurement activities?…

The 11-year solar cycle in current reanalyses: a (non)linear attribution study of the middle atmosphere

NASA Astrophysics Data System (ADS)

Kuchar, A.; Sacha, P.; Miksovsky, J.; Pisoft, P.

2015-06-01

This study focusses on the variability of temperature, ozone and circulation characteristics in the stratosphere and lower mesosphere with regard to the influence of the 11-year solar cycle. It is based on attribution analysis using multiple nonlinear techniques (support vector regression, neural networks) besides the multiple linear regression approach. The analysis was applied to several current reanalysis data sets for the 1979-2013 period, including MERRA, ERA-Interim and JRA-55, with the aim to compare how these types of data resolve especially the double-peaked solar response in temperature and ozone variables and the consequent changes induced by these anomalies. Equatorial temperature signals in the tropical stratosphere were found to be in qualitative agreement with previous attribution studies, although the agreement with observational results was incomplete, especially for JRA-55. The analysis also pointed to the solar signal in the ozone data sets (i.e. MERRA and ERA-Interim) not being consistent with the observed double-peaked ozone anomaly extracted from satellite measurements. The results obtained by linear regression were confirmed by the nonlinear approach through all data sets, suggesting that linear regression is a relevant tool to sufficiently resolve the solar signal in the middle atmosphere. The seasonal evolution of the solar response was also discussed in terms of dynamical causalities in the winter hemispheres. The hypothetical mechanism of a weaker Brewer-Dobson circulation at solar maxima was reviewed together with a discussion of polar vortex behaviour.

[Variable selection methods combined with local linear embedding theory used for optimization of near infrared spectral quantitative models].

PubMed

Hao, Yong; Sun, Xu-Dong; Yang, Qiang

2012-12-01

Variables selection strategy combined with local linear embedding (LLE) was introduced for the analysis of complex samples by using near infrared spectroscopy (NIRS). Three methods include Monte Carlo uninformation variable elimination (MCUVE), successive projections algorithm (SPA) and MCUVE connected with SPA were used for eliminating redundancy spectral variables. Partial least squares regression (PLSR) and LLE-PLSR were used for modeling complex samples. The results shown that MCUVE can both extract effective informative variables and improve the precision of models. Compared with PLSR models, LLE-PLSR models can achieve more accurate analysis results. MCUVE combined with LLE-PLSR is an effective modeling method for NIRS quantitative analysis.

Simple and multiple linear regression: sample size considerations.

PubMed

Hanley, James A

2016-11-01

The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright © 2016 Elsevier Inc. All rights reserved.

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

PubMed Central

Song, Yong-Ze; Yang, Hong-Lei; Peng, Jun-Huan; Song, Yi-Rong; Sun, Qian; Li, Yuan

2015-01-01

Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5. PMID:26540446

Geographical variation of cerebrovascular disease in New York State: the correlation with income

PubMed Central

Han, Daikwon; Carrow, Shannon S; Rogerson, Peter A; Munschauer, Frederick E

2005-01-01

Background Income is known to be associated with cerebrovascular disease; however, little is known about the more detailed relationship between cerebrovascular disease and income. We examined the hypothesis that the geographical distribution of cerebrovascular disease in New York State may be predicted by a nonlinear model using income as a surrogate socioeconomic risk factor. Results We used spatial clustering methods to identify areas with high and low prevalence of cerebrovascular disease at the ZIP code level after smoothing rates and correcting for edge effects; geographic locations of high and low clusters of cerebrovascular disease in New York State were identified with and without income adjustment. To examine effects of income, we calculated the excess number of cases using a non-linear regression with cerebrovascular disease rates taken as the dependent variable and income and income squared taken as independent variables. The resulting regression equation was: excess rate = 32.075 - 1.22*10-4(income) + 8.068*10-10(income2), and both income and income squared variables were significant at the 0.01 level. When income was included as a covariate in the non-linear regression, the number and size of clusters of high cerebrovascular disease prevalence decreased. Some 87 ZIP codes exceeded the critical value of the local statistic yielding a relative risk of 1.2. The majority of low cerebrovascular disease prevalence geographic clusters disappeared when the non-linear income effect was included. For linear regression, the excess rate of cerebrovascular disease falls with income; each $10,000 increase in median income of each ZIP code resulted in an average reduction of 3.83 observed cases. The significant nonlinear effect indicates a lessening of this income effect with increasing income. Conclusion Income is a non-linear predictor of excess cerebrovascular disease rates, with both low and high observed cerebrovascular disease rate areas associated with higher income. Income alone explains a significant amount of the geographical variance in cerebrovascular disease across New York State since both high and low clusters of cerebrovascular disease dissipate or disappear with income adjustment. Geographical modeling, including non-linear effects of income, may allow for better identification of other non-traditional risk factors. PMID:16242043

Old and New Ideas for Data Screening and Assumption Testing for Exploratory and Confirmatory Factor Analysis

PubMed Central

Flora, David B.; LaBrish, Cathy; Chalmers, R. Philip

2011-01-01

We provide a basic review of the data screening and assumption testing issues relevant to exploratory and confirmatory factor analysis along with practical advice for conducting analyses that are sensitive to these concerns. Historically, factor analysis was developed for explaining the relationships among many continuous test scores, which led to the expression of the common factor model as a multivariate linear regression model with observed, continuous variables serving as dependent variables, and unobserved factors as the independent, explanatory variables. Thus, we begin our paper with a review of the assumptions for the common factor model and data screening issues as they pertain to the factor analysis of continuous observed variables. In particular, we describe how principles from regression diagnostics also apply to factor analysis. Next, because modern applications of factor analysis frequently involve the analysis of the individual items from a single test or questionnaire, an important focus of this paper is the factor analysis of items. Although the traditional linear factor model is well-suited to the analysis of continuously distributed variables, commonly used item types, including Likert-type items, almost always produce dichotomous or ordered categorical variables. We describe how relationships among such items are often not well described by product-moment correlations, which has clear ramifications for the traditional linear factor analysis. An alternative, non-linear factor analysis using polychoric correlations has become more readily available to applied researchers and thus more popular. Consequently, we also review the assumptions and data-screening issues involved in this method. Throughout the paper, we demonstrate these procedures using an historic data set of nine cognitive ability variables. PMID:22403561

Effect of Ankle Range of Motion (ROM) and Lower-Extremity Muscle Strength on Static Balance Control Ability in Young Adults: A Regression Analysis

PubMed Central

Kim, Seong-Gil

2018-01-01

Background The purpose of this study was to investigate the effect of ankle ROM and lower-extremity muscle strength on static balance control ability in young adults. Material/Methods This study was conducted with 65 young adults, but 10 young adults dropped out during the measurement, so 55 young adults (male: 19, female: 36) completed the study. Postural sway (length and velocity) was measured with eyes open and closed, and ankle ROM (AROM and PROM of dorsiflexion and plantarflexion) and lower-extremity muscle strength (flexor and extensor of hip, knee, and ankle joint) were measured. Pearson correlation coefficient was used to examine the correlation between variables and static balance ability. Simple linear regression analysis and multiple linear regression analysis were used to examine the effect of variables on static balance ability. Results In correlation analysis, plantarflexion ROM (AROM and PROM) and lower-extremity muscle strength (except hip extensor) were significantly correlated with postural sway (p<0.05). In simple correlation analysis, all variables that passed the correlation analysis procedure had significant influence (p<0.05). In multiple linear regression analysis, plantar flexion PROM with eyes open significantly influenced sway length (B=0.681) and sway velocity (B=0.011). Conclusions Lower-extremity muscle strength and ankle plantarflexion ROM influenced static balance control ability, with ankle plantarflexion PROM showing the greatest influence. Therefore, both contractile structures and non-contractile structures should be of interest when considering static balance control ability improvement. PMID:29760375

Effect of Ankle Range of Motion (ROM) and Lower-Extremity Muscle Strength on Static Balance Control Ability in Young Adults: A Regression Analysis.

PubMed

Kim, Seong-Gil; Kim, Wan-Soo

2018-05-15

BACKGROUND The purpose of this study was to investigate the effect of ankle ROM and lower-extremity muscle strength on static balance control ability in young adults. MATERIAL AND METHODS This study was conducted with 65 young adults, but 10 young adults dropped out during the measurement, so 55 young adults (male: 19, female: 36) completed the study. Postural sway (length and velocity) was measured with eyes open and closed, and ankle ROM (AROM and PROM of dorsiflexion and plantarflexion) and lower-extremity muscle strength (flexor and extensor of hip, knee, and ankle joint) were measured. Pearson correlation coefficient was used to examine the correlation between variables and static balance ability. Simple linear regression analysis and multiple linear regression analysis were used to examine the effect of variables on static balance ability. RESULTS In correlation analysis, plantarflexion ROM (AROM and PROM) and lower-extremity muscle strength (except hip extensor) were significantly correlated with postural sway (p<0.05). In simple correlation analysis, all variables that passed the correlation analysis procedure had significant influence (p<0.05). In multiple linear regression analysis, plantar flexion PROM with eyes open significantly influenced sway length (B=0.681) and sway velocity (B=0.011). CONCLUSIONS Lower-extremity muscle strength and ankle plantarflexion ROM influenced static balance control ability, with ankle plantarflexion PROM showing the greatest influence. Therefore, both contractile structures and non-contractile structures should be of interest when considering static balance control ability improvement.

Predicting location of recurrence using FDG, FLT, and Cu-ATSM PET in canine sinonasal tumors treated with radiotherapy

NASA Astrophysics Data System (ADS)

Bradshaw, Tyler; Fu, Rau; Bowen, Stephen; Zhu, Jun; Forrest, Lisa; Jeraj, Robert

2015-07-01

Dose painting relies on the ability of functional imaging to identify resistant tumor subvolumes to be targeted for additional boosting. This work assessed the ability of FDG, FLT, and Cu-ATSM PET imaging to predict the locations of residual FDG PET in canine tumors following radiotherapy. Nineteen canines with spontaneous sinonasal tumors underwent PET/CT imaging with radiotracers FDG, FLT, and Cu-ATSM prior to hypofractionated radiotherapy. Therapy consisted of 10 fractions of 4.2 Gy to the sinonasal cavity with or without an integrated boost of 0.8 Gy to the GTV. Patients had an additional FLT PET/CT scan after fraction 2, a Cu-ATSM PET/CT scan after fraction 3, and follow-up FDG PET/CT scans after radiotherapy. Following image registration, simple and multiple linear and logistic voxel regressions were performed to assess how well pre- and mid-treatment PET imaging predicted post-treatment FDG uptake. R2 and pseudo R2 were used to assess the goodness of fits. For simple linear regression models, regression coefficients for all pre- and mid-treatment PET images were significantly positive across the population (P < 0.05). However, there was large variability among patients in goodness of fits: R2 ranged from 0.00 to 0.85, with a median of 0.12. Results for logistic regression models were similar. Multiple linear regression models resulted in better fits (median R2 = 0.31), but there was still large variability between patients in R2. The R2 from regression models for different predictor variables were highly correlated across patients (R ≈ 0.8), indicating tumors that were poorly predicted with one tracer were also poorly predicted by other tracers. In conclusion, the high inter-patient variability in goodness of fits indicates that PET was able to predict locations of residual tumor in some patients, but not others. This suggests not all patients would be good candidates for dose painting based on a single biological target.

Predicting location of recurrence using FDG, FLT, and Cu-ATSM PET in canine sinonasal tumors treated with radiotherapy.

PubMed

Bradshaw, Tyler; Fu, Rau; Bowen, Stephen; Zhu, Jun; Forrest, Lisa; Jeraj, Robert

2015-07-07

Dose painting relies on the ability of functional imaging to identify resistant tumor subvolumes to be targeted for additional boosting. This work assessed the ability of FDG, FLT, and Cu-ATSM PET imaging to predict the locations of residual FDG PET in canine tumors following radiotherapy. Nineteen canines with spontaneous sinonasal tumors underwent PET/CT imaging with radiotracers FDG, FLT, and Cu-ATSM prior to hypofractionated radiotherapy. Therapy consisted of 10 fractions of 4.2 Gy to the sinonasal cavity with or without an integrated boost of 0.8 Gy to the GTV. Patients had an additional FLT PET/CT scan after fraction 2, a Cu-ATSM PET/CT scan after fraction 3, and follow-up FDG PET/CT scans after radiotherapy. Following image registration, simple and multiple linear and logistic voxel regressions were performed to assess how well pre- and mid-treatment PET imaging predicted post-treatment FDG uptake. R(2) and pseudo R(2) were used to assess the goodness of fits. For simple linear regression models, regression coefficients for all pre- and mid-treatment PET images were significantly positive across the population (P < 0.05). However, there was large variability among patients in goodness of fits: R(2) ranged from 0.00 to 0.85, with a median of 0.12. Results for logistic regression models were similar. Multiple linear regression models resulted in better fits (median R(2) = 0.31), but there was still large variability between patients in R(2). The R(2) from regression models for different predictor variables were highly correlated across patients (R ≈ 0.8), indicating tumors that were poorly predicted with one tracer were also poorly predicted by other tracers. In conclusion, the high inter-patient variability in goodness of fits indicates that PET was able to predict locations of residual tumor in some patients, but not others. This suggests not all patients would be good candidates for dose painting based on a single biological target.

A statistical model for Windstorm Variability over the British Isles based on Large-scale Atmospheric and Oceanic Mechanisms

NASA Astrophysics Data System (ADS)

Kirchner-Bossi, Nicolas; Befort, Daniel J.; Wild, Simon B.; Ulbrich, Uwe; Leckebusch, Gregor C.

2016-04-01

Time-clustered winter storms are responsible for a majority of the wind-induced losses in Europe. Over last years, different atmospheric and oceanic large-scale mechanisms as the North Atlantic Oscillation (NAO) or the Meridional Overturning Circulation (MOC) have been proven to drive some significant portion of the windstorm variability over Europe. In this work we systematically investigate the influence of different large-scale natural variability modes: more than 20 indices related to those mechanisms with proven or potential influence on the windstorm frequency variability over Europe - mostly SST- or pressure-based - are derived by means of ECMWF ERA-20C reanalysis during the last century (1902-2009), and compared to the windstorm variability for the European winter (DJF). Windstorms are defined and tracked as in Leckebusch et al. (2008). The derived indices are then employed to develop a statistical procedure including a stepwise Multiple Linear Regression (MLR) and an Artificial Neural Network (ANN), aiming to hindcast the inter-annual (DJF) regional windstorm frequency variability in a case study for the British Isles. This case study reveals 13 indices with a statistically significant coupling with seasonal windstorm counts. The Scandinavian Pattern (SCA) showed the strongest correlation (0.61), followed by the NAO (0.48) and the Polar/Eurasia Pattern (0.46). The obtained indices (standard-normalised) are selected as predictors for a windstorm variability hindcast model applied for the British Isles. First, a stepwise linear regression is performed, to identify which mechanisms can explain windstorm variability best. Finally, the indices retained by the stepwise regression are used to develop a multlayer perceptron-based ANN that hindcasted seasonal windstorm frequency and clustering. Eight indices (SCA, NAO, EA, PDO, W.NAtl.SST, AMO (unsmoothed), EA/WR and Trop.N.Atl SST) are retained by the stepwise regression. Among them, SCA showed the highest linear coefficient, followed by SST in western Atlantic, AMO and NAO. The explanatory regression model (considering all time steps) provided a Coefficient of Determination (R^2) of 0.75. A predictive version of the linear model applying a leave-one-out cross-validation (LOOCV) shows an R2 of 0.56 and a relative RMSE of 4.67 counts/season. An ANN-based nonlinear hindcast model for the seasonal windstorm frequency is developed with the aim to improve the stepwise hindcast ability and thus better predict a time-clustered season over the case study. A 7 node-hidden layer perceptron is set, and the LOOCV procedure reveals a R2 of 0.71. In comparison to the stepwise MLR the RMSE is reduced a 20%. This work shows that for the British Isles case study, most of the interannual variability can be explained by certain large-scale mechanisms, considering also nonlinear effects (ANN). This allows to discern a time-clustered season from a non-clustered one - a key issue for applications e.g., in the (re)insurance industry.

Growth and yield in Eucalyptus globulus

Treesearch

James A. Rinehart; Richard B. Standiford

1983-01-01

A study of the major Eucalyptus globulus stands throughout California conducted by Woodbridge Metcalf in 1924 provides a complete and accurate data set for generating variable site-density yield models. Two models were developed using linear regression techniques. Model I depicts a linear relationship between age and yield best used for stands between five and fifteen...

RELATION OF ENVIRONMENTAL CHARACTERISTICS TO FISH ASSEMBLAGES IN THE UPPER FRENCH BROAD RIVER BASIN, NORTH CAROLINA

EPA Science Inventory

Fish assemblages at 16 sites in the upper French Broad River basin, North Carolina were related to environmental variables using detrended correspondence analysis (DCA) and linear regression. This study was conducted at the landscape scale because regional variables are controlle...

Centering Effects in HLM Level-1 Predictor Variables.

ERIC Educational Resources Information Center

Schumacker, Randall E.; Bembry, Karen

Research has suggested that important research questions can be addressed with meaningful interpretations using hierarchical linear modeling (HLM). The proper interpretation of results, however, is invariably linked to the choice of centering for the Level-1 predictor variables that produce the outcome measure for the Level-2 regression analysis.…

Estimating severity of sideways fall using a generic multi linear regression model based on kinematic input variables.

PubMed

van der Zijden, A M; Groen, B E; Tanck, E; Nienhuis, B; Verdonschot, N; Weerdesteyn, V

2017-03-21

Many research groups have studied fall impact mechanics to understand how fall severity can be reduced to prevent hip fractures. Yet, direct impact force measurements with force plates are restricted to a very limited repertoire of experimental falls. The purpose of this study was to develop a generic model for estimating hip impact forces (i.e. fall severity) in in vivo sideways falls without the use of force plates. Twelve experienced judokas performed sideways Martial Arts (MA) and Block ('natural') falls on a force plate, both with and without a mat on top. Data were analyzed to determine the hip impact force and to derive 11 selected (subject-specific and kinematic) variables. Falls from kneeling height were used to perform a stepwise regression procedure to assess the effects of these input variables and build the model. The final model includes four input variables, involving one subject-specific measure and three kinematic variables: maximum upper body deceleration, body mass, shoulder angle at the instant of 'maximum impact' and maximum hip deceleration. The results showed that estimated and measured hip impact forces were linearly related (explained variances ranging from 46 to 63%). Hip impact forces of MA falls onto the mat from a standing position (3650±916N) estimated by the final model were comparable with measured values (3698±689N), even though these data were not used for training the model. In conclusion, a generic linear regression model was developed that enables the assessment of fall severity through kinematic measures of sideways falls, without using force plates. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modeling Fire Occurrence at the City Scale: A Comparison between Geographically Weighted Regression and Global Linear Regression.

PubMed

Song, Chao; Kwan, Mei-Po; Zhu, Jiping

2017-04-08

An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), which integrates spatial and temporal effects and global linear regression models (LM) for modeling fire risk at the city scale. The results show that the road density and the spatial distribution of enterprises have the strongest influences on fire risk, which implies that we should focus on areas where roads and enterprises are densely clustered. In addition, locations with a large number of enterprises have fewer fire ignition records, probably because of strict management and prevention measures. A changing number of significant variables across space indicate that heterogeneity mainly exists in the northern and eastern rural and suburban areas of Hefei city, where human-related facilities or road construction are only clustered in the city sub-centers. GTWR can capture small changes in the spatiotemporal heterogeneity of the variables while GWR and LM cannot. An approach that integrates space and time enables us to better understand the dynamic changes in fire risk. Thus governments can use the results to manage fire safety at the city scale.

TSS concentration in sewers estimated from turbidity measurements by means of linear regression accounting for uncertainties in both variables.

PubMed

Bertrand-Krajewski, J L

2004-01-01

In order to replace traditional sampling and analysis techniques, turbidimeters can be used to estimate TSS concentration in sewers, by means of sensor and site specific empirical equations established by linear regression of on-site turbidity Tvalues with TSS concentrations C measured in corresponding samples. As the ordinary least-squares method is not able to account for measurement uncertainties in both T and C variables, an appropriate regression method is used to solve this difficulty and to evaluate correctly the uncertainty in TSS concentrations estimated from measured turbidity. The regression method is described, including detailed calculations of variances and covariance in the regression parameters. An example of application is given for a calibrated turbidimeter used in a combined sewer system, with data collected during three dry weather days. In order to show how the established regression could be used, an independent 24 hours long dry weather turbidity data series recorded at 2 min time interval is used, transformed into estimated TSS concentrations, and compared to TSS concentrations measured in samples. The comparison appears as satisfactory and suggests that turbidity measurements could replace traditional samples. Further developments, including wet weather periods and other types of sensors, are suggested.

Modeling Fire Occurrence at the City Scale: A Comparison between Geographically Weighted Regression and Global Linear Regression

PubMed Central

Song, Chao; Kwan, Mei-Po; Zhu, Jiping

2017-01-01

An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), which integrates spatial and temporal effects and global linear regression models (LM) for modeling fire risk at the city scale. The results show that the road density and the spatial distribution of enterprises have the strongest influences on fire risk, which implies that we should focus on areas where roads and enterprises are densely clustered. In addition, locations with a large number of enterprises have fewer fire ignition records, probably because of strict management and prevention measures. A changing number of significant variables across space indicate that heterogeneity mainly exists in the northern and eastern rural and suburban areas of Hefei city, where human-related facilities or road construction are only clustered in the city sub-centers. GTWR can capture small changes in the spatiotemporal heterogeneity of the variables while GWR and LM cannot. An approach that integrates space and time enables us to better understand the dynamic changes in fire risk. Thus governments can use the results to manage fire safety at the city scale. PMID:28397745

Modification of the USLE K factor for soil erodibility assessment on calcareous soils in Iran

NASA Astrophysics Data System (ADS)

Ostovari, Yaser; Ghorbani-Dashtaki, Shoja; Bahrami, Hossein-Ali; Naderi, Mehdi; Dematte, Jose Alexandre M.; Kerry, Ruth

2016-11-01

The measurement of soil erodibility (K) in the field is tedious, time-consuming and expensive; therefore, its prediction through pedotransfer functions (PTFs) could be far less costly and time-consuming. The aim of this study was to develop new PTFs to estimate the K factor using multiple linear regression, Mamdani fuzzy inference systems, and artificial neural networks. For this purpose, K was measured in 40 erosion plots with natural rainfall. Various soil properties including the soil particle size distribution, calcium carbonate equivalent, organic matter, permeability, and wet-aggregate stability were measured. The results showed that the mean measured K was 0.014 t h MJ- 1 mm- 1 and 2.08 times less than the estimated mean K (0.030 t h MJ- 1 mm- 1) using the USLE model. Permeability, wet-aggregate stability, very fine sand, and calcium carbonate were selected as independent variables by forward stepwise regression in order to assess the ability of multiple linear regression, Mamdani fuzzy inference systems and artificial neural networks to predict K. The calcium carbonate equivalent, which is not accounted for in the USLE model, had a significant impact on K in multiple linear regression due to its strong influence on the stability of aggregates and soil permeability. Statistical indices in validation and calibration datasets determined that the artificial neural networks method with the highest R2, lowest RMSE, and lowest ME was the best model for estimating the K factor. A strong correlation (R2 = 0.81, n = 40, p < 0.05) between the estimated K from multiple linear regression and measured K indicates that the use of calcium carbonate equivalent as a predictor variable gives a better estimation of K in areas with calcareous soils.

Principal component regression analysis with SPSS.

PubMed

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Identification of International Classification of Functioning, Disability and Health categories for patients with peripheral arterial disease.

PubMed

Vyskocil, Erich; Gruther, Wolfgang; Steiner, Irene; Schuhfried, Othmar

2014-07-01

Disease-specific categories of the International Classification of Functioning, Disability and Health have not yet been described for patients with chronic peripheral arterial obstructive disease (PAD). The authors examined the relationship between the categories of the Brief Core Sets for ischemic heart diseases with the Peripheral Artery Questionnaire and the ankle-brachial index to determine which International Classification of Functioning, Disability and Health categories are most relevant for patients with PAD. This is a retrospective cohort study including 77 patients with verified PAD. Statistical analyses of the relationship between International Classification of Functioning, Disability and Health categories as independent variables and the endpoints Peripheral Artery Questionnaire or ankle-brachial index were carried out by simple and stepwise linear regression models adjusting for age, sex, and leg (left vs. right). The stepwise linear regression model with the ankle-brachial index as dependent variable revealed a significant effect of the variables blood vessel functions and muscle endurance functions. Calculating a stepwise linear regression model with the Peripheral Artery Questionnaire as dependent variable, a significant effect of age, emotional functions, energy and drive functions, carrying out daily routine, as well as walking could be observed. This study identifies International Classification of Functioning, Disability and Health categories in the Brief Core Sets for ischemic heart diseases that show a significant effect on the ankle-brachial index and the Peripheral Artery Questionnaire score in patients with PAD. These categories provide fundamental information on functioning of patients with PAD and patient-centered outcomes for rehabilitation interventions.

The comparison of robust partial least squares regression with robust principal component regression on a real

NASA Astrophysics Data System (ADS)

Polat, Esra; Gunay, Suleyman

2013-10-01

One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.

Modeling Pan Evaporation for Kuwait by Multiple Linear Regression

PubMed Central

Almedeij, Jaber

2012-01-01

Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values. PMID:23226984

Product unit neural network models for predicting the growth limits of Listeria monocytogenes.

PubMed

Valero, A; Hervás, C; García-Gimeno, R M; Zurera, G

2007-08-01

A new approach to predict the growth/no growth interface of Listeria monocytogenes as a function of storage temperature, pH, citric acid (CA) and ascorbic acid (AA) is presented. A linear logistic regression procedure was performed and a non-linear model was obtained by adding new variables by means of a Neural Network model based on Product Units (PUNN). The classification efficiency of the training data set and the generalization data of the new Logistic Regression PUNN model (LRPU) were compared with Linear Logistic Regression (LLR) and Polynomial Logistic Regression (PLR) models. 92% of the total cases from the LRPU model were correctly classified, an improvement on the percentage obtained using the PLR model (90%) and significantly higher than the results obtained with the LLR model, 80%. On the other hand predictions of LRPU were closer to data observed which permits to design proper formulations in minimally processed foods. This novel methodology can be applied to predictive microbiology for describing growth/no growth interface of food-borne microorganisms such as L. monocytogenes. The optimal balance is trying to find models with an acceptable interpretation capacity and with good ability to fit the data on the boundaries of variable range. The results obtained conclude that these kinds of models might well be very a valuable tool for mathematical modeling.

TiO2 dye sensitized solar cell (DSSC): linear relationship of maximum power point and anthocyanin concentration

NASA Astrophysics Data System (ADS)

Ahmadian, Radin

2010-09-01

This study investigated the relationship of anthocyanin concentration from different organic fruit species and output voltage and current in a TiO2 dye-sensitized solar cell (DSSC) and hypothesized that fruits with greater anthocyanin concentration produce higher maximum power point (MPP) which would lead to higher current and voltage. Anthocyanin dye solution was made with crushing of a group of fresh fruits with different anthocyanin content in 2 mL of de-ionized water and filtration. Using these test fruit dyes, multiple DSSCs were assembled such that light enters through the TiO2 side of the cell. The full current-voltage (I-V) co-variations were measured using a 500 Ω potentiometer as a variable load. Point-by point current and voltage data pairs were measured at various incremental resistance values. The maximum power point (MPP) generated by the solar cell was defined as a dependent variable and the anthocyanin concentration in the fruit used in the DSSC as the independent variable. A regression model was used to investigate the linear relationship between study variables. Regression analysis showed a significant linear relationship between MPP and anthocyanin concentration with a p-value of 0.007. Fruits like blueberry and black raspberry with the highest anthocyanin content generated higher MPP. In a DSSC, a linear model may predict MPP based on the anthocyanin concentration. This model is the first step to find organic anthocyanin sources in the nature with the highest dye concentration to generate energy.

Sparse partial least squares regression for simultaneous dimension reduction and variable selection

PubMed Central

Chun, Hyonho; Keleş, Sündüz

2010-01-01

Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data. PMID:20107611

Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Evaluation and prediction of shrub cover in coastal Oregon forests (USA)

Treesearch

Becky K. Kerns; Janet L. Ohmann

2004-01-01

We used data from regional forest inventories and research programs, coupled with mapped climatic and topographic information, to explore relationships and develop multiple linear regression (MLR) and regression tree models for total and deciduous shrub cover in the Oregon coastal province. Results from both types of models indicate that forest structure variables were...

Weighted linear regression using D2H and D2 as the independent variables

Treesearch

Hans T. Schreuder; Michael S. Williams

1998-01-01

Several error structures for weighted regression equations used for predicting volume were examined for 2 large data sets of felled and standing loblolly pine trees (Pinus taeda L.). The generally accepted model with variance of error proportional to the value of the covariate squared ( D2H = diameter squared times height or D...

Multiple linear regression analysis

NASA Technical Reports Server (NTRS)

Edwards, T. R.

1980-01-01

Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.

Mapping Regional Impervious Surface Distribution from Night Time Light: The Variability across Global Cities

NASA Astrophysics Data System (ADS)

Lin, M.; Yang, Z.; Park, H.; Qian, S.; Chen, J.; Fan, P.

2017-12-01

Impervious surface area (ISA) has become an important indicator for studying urban environments, but mapping ISA at the regional or global scale is still challenging due to the complexity of impervious surface features. The Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light data is (NTL) and Resolution Imaging Spectroradiometer (MODIS) are the major remote sensing data source for regional ISA mapping. A single regression relationship between fractional ISA and NTL or various index derived based on NTL and MODIS vegetation index (NDVI) data was established in many previous studies for regional ISA mapping. However, due to the varying geographical, climatic, and socio-economic characteristics of different cities, the same regression relationship may vary significantly across different cities in the same region in terms of both fitting performance (i.e. R2) and the rate of change (Slope). In this study, we examined the regression relationship between fractional ISA and Vegetation Adjusted Nighttime light Urban Index (VANUI) for 120 randomly selected cities around the world with a multilevel regression model. We found that indeed there is substantial variability of both the R2 (0.68±0.29) and slopes (0.64±0.40) among individual regressions, which suggests that multilevel/hierarchical models are needed for accuracy improvement of future regional ISA mapping .Further analysis also let us find the this substantial variability are affected by climate conditions, socio-economic status, and urban spatial structures. However, all these effects are nonlinear rather than linear, thus could not modeled explicitly in multilevel linear regression models.

Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images

NASA Astrophysics Data System (ADS)

Fernández-Manso, O.; Fernández-Manso, A.; Quintano, C.

2014-09-01

Aboveground biomass (AGB) estimation from optical satellite data is usually based on regression models of original or synthetic bands. To overcome the poor relation between AGB and spectral bands due to mixed-pixels when a medium spatial resolution sensor is considered, we propose to base the AGB estimation on fraction images from Linear Spectral Mixture Analysis (LSMA). Our study area is a managed Mediterranean pine woodland (Pinus pinaster Ait.) in central Spain. A total of 1033 circular field plots were used to estimate AGB from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) optical data. We applied Pearson correlation statistics and stepwise multiple regression to identify suitable predictors from the set of variables of original bands, fraction imagery, Normalized Difference Vegetation Index and Tasselled Cap components. Four linear models and one nonlinear model were tested. A linear combination of ASTER band 2 (red, 0.630-0.690 μm), band 8 (short wave infrared 5, 2.295-2.365 μm) and green vegetation fraction (from LSMA) was the best AGB predictor (Radj2=0.632, the root-mean-squared error of estimated AGB was 13.3 Mg ha-1 (or 37.7%), resulting from cross-validation), rather than other combinations of the above cited independent variables. Results indicated that using ASTER fraction images in regression models improves the AGB estimation in Mediterranean pine forests. The spatial distribution of the estimated AGB, based on a multiple linear regression model, may be used as baseline information for forest managers in future studies, such as quantifying the regional carbon budget, fuel accumulation or monitoring of management practices.

Effect of Contact Damage on the Strength of Ceramic Materials.

DTIC Science & Technology

1982-10-01

variables that are important to erosion, and a multivariate , linear regression analysis is used to fit the data to the dimensional analysis. The...of Equations 7 and 8 by a multivariable regression analysis (room tem- perature data) Exponent Regression Standard error Computed coefficient of...1980) 593. WEAVER, Proc. Brit. Ceram. Soc. 22 (1973) 125. 39. P. W. BRIDGMAN, "Dimensional Analaysis ", (Yale 18. R. W. RICE, S. W. FREIMAN and P. F

Impact of Preadmission Variables on USMLE Step 1 and Step 2 Performance

ERIC Educational Resources Information Center

Kleshinski, James; Khuder, Sadik A.; Shapiro, Joseph I.; Gold, Jeffrey P.

2009-01-01

Purpose: To examine the predictive ability of preadmission variables on United States Medical Licensing Examinations (USMLE) step 1 and step 2 performance, incorporating the use of a neural network model. Method: Preadmission data were collected on matriculants from 1998 to 2004. Linear regression analysis was first used to identify predictors of…

The Use of Structure Coefficients to Address Multicollinearity in Sport and Exercise Science

ERIC Educational Resources Information Center

Yeatts, Paul E.; Barton, Mitch; Henson, Robin K.; Martin, Scott B.

2017-01-01

A common practice in general linear model (GLM) analyses is to interpret regression coefficients (e.g., standardized ß weights) as indicators of variable importance. However, focusing solely on standardized beta weights may provide limited or erroneous information. For example, ß weights become increasingly unreliable when predictor variables are…

The Role of Schools, Families, and Psychological Variables on Math Achievement of Black High School Students

ERIC Educational Resources Information Center

Strayhorn, Terrell L.

2010-01-01

Using data from the National Education Longitudinal Study (NELS;1988/2000), the author conducted hierarchical linear regression analyses, with a nested design, to estimate the influence of affective variables--parent involvement, teacher perceptions, and school environments--on Black students' math achievement in grade 10. Drawing on…

Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Kong, Shish-Jeng; Chang, Chih-Hua

2018-03-01

The turbidity (TB) of a water body varies with time and space. Water quality is traditionally estimated via linear regression based on satellite images. However, estimating and mapping water quality require a spatio-temporal nonstationary model, while TB mapping necessitates the use of geographically and temporally weighted regression (GTWR) and geographically weighted regression (GWR) models, both of which are more precise than linear regression. Given the temporal nonstationary models for mapping water quality, GTWR offers the best option for estimating regional water quality. Compared with GWR, GTWR provides highly reliable information for water quality mapping, boasts a relatively high goodness of fit, improves the explanation of variance from 44% to 87%, and shows a sufficient space-time explanatory power. The seasonal patterns of TB and the main spatial patterns of TB variability can be identified using the estimated TB maps from GTWR and by conducting an empirical orthogonal function (EOF) analysis.

Firmness prediction in Prunus persica 'Calrico' peaches by visible/short-wave near infrared spectroscopy and acoustic measurements using optimised linear and non-linear chemometric models.

PubMed

Lafuente, Victoria; Herrera, Luis J; Pérez, María del Mar; Val, Jesús; Negueruela, Ignacio

2015-08-15

In this work, near infrared spectroscopy (NIR) and an acoustic measure (AWETA) (two non-destructive methods) were applied in Prunus persica fruit 'Calrico' (n = 260) to predict Magness-Taylor (MT) firmness. Separate and combined use of these measures was evaluated and compared using partial least squares (PLS) and least squares support vector machine (LS-SVM) regression methods. Also, a mutual-information-based variable selection method, seeking to find the most significant variables to produce optimal accuracy of the regression models, was applied to a joint set of variables (NIR wavelengths and AWETA measure). The newly proposed combined NIR-AWETA model gave good values of the determination coefficient (R(2)) for PLS and LS-SVM methods (0.77 and 0.78, respectively), improving the reliability of MT firmness prediction in comparison with separate NIR and AWETA predictions. The three variables selected by the variable selection method (AWETA measure plus NIR wavelengths 675 and 697 nm) achieved R(2) values 0.76 and 0.77, PLS and LS-SVM. These results indicated that the proposed mutual-information-based variable selection algorithm was a powerful tool for the selection of the most relevant variables. © 2014 Society of Chemical Industry.

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

USGS Publications Warehouse

Rasmussen, Patrick P.; Gray, John R.; Glysson, G. Douglas; Ziegler, Andrew C.

2009-01-01

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

Modeling Laterality of the Globus Pallidus Internus in Patients With Parkinson's Disease.

PubMed

Sharim, Justin; Yazdi, Daniel; Baohan, Amy; Behnke, Eric; Pouratian, Nader

2017-04-01

Neurosurgical interventions such as deep brain stimulation surgery of the globus pallidus internus (GPi) play an important role in the treatment of medically refractory Parkinson's disease (PD), and require high targeting accuracy. Variability in the laterality of the GPi across patients with PD has not been well characterized. The aim of this report is to identify factors that may contribute to differences in position of the motor region of GPi. The charts and operative reports of 101 PD patients following deep brain stimulation surgery (70 males, aged 11-78 years) representing 201 GPi were retrospectively reviewed. Data extracted for each subject include age, gender, anterior and posterior commissures (AC-PC) distance, and third ventricular width. Multiple linear regression, stepwise regression, and relative importance of regressors analysis were performed to assess the predictive ability of these variables on GPi laterality. Multiple linear regression for target vs. third ventricular width, gender, AC-PC distance, and age were significant for normalized linear regression coefficients of 0.333 (p < 0.0001), 0.206 (p = 0.00219), 0.168 (p = 0.0119), and 0.159 (p = 0.0136), respectively. Third ventricular width, gender, AC-PC distance, and age each account for 44.06% (21.38-65.69%, 95% CI), 20.82% (10.51-35.88%), 21.46% (8.28-37.05%), and 13.66% (2.62-28.64%) of the R 2 value, respectively. Effect size calculation was significant for a change in the GPi laterality of 0.19 mm per mm of ventricular width, 0.11 mm per mm of AC-PC distance, 0.017 mm per year in age, and 0.54 mm increase for male gender. This variability highlights the limitations of indirect targeting alone, and argues for the continued use of MRI as well as intraoperative physiological testing to account for such factors that contribute to patient-specific variability in GPi localization. © 2016 International Neuromodulation Society.

Linear Least Squares for Correlated Data

NASA Technical Reports Server (NTRS)

Dean, Edwin B.

1988-01-01

Throughout the literature authors have consistently discussed the suspicion that regression results were less than satisfactory when the independent variables were correlated. Camm, Gulledge, and Womer, and Womer and Marcotte provide excellent applied examples of these concerns. Many authors have obtained partial solutions for this problem as discussed by Womer and Marcotte and Wonnacott and Wonnacott, which result in generalized least squares algorithms to solve restrictive cases. This paper presents a simple but relatively general multivariate method for obtaining linear least squares coefficients which are free of the statistical distortion created by correlated independent variables.

Mixed geographically weighted regression (MGWR) model with weighted adaptive bi-square for case of dengue hemorrhagic fever (DHF) in Surakarta

NASA Astrophysics Data System (ADS)

Astuti, H. N.; Saputro, D. R. S.; Susanti, Y.

2017-06-01

MGWR model is combination of linear regression model and geographically weighted regression (GWR) model, therefore, MGWR model could produce parameter estimation that had global parameter estimation, and other parameter that had local parameter in accordance with its observation location. The linkage between locations of the observations expressed in specific weighting that is adaptive bi-square. In this research, we applied MGWR model with weighted adaptive bi-square for case of DHF in Surakarta based on 10 factors (variables) that is supposed to influence the number of people with DHF. The observation unit in the research is 51 urban villages and the variables are number of inhabitants, number of houses, house index, many public places, number of healthy homes, number of Posyandu, area width, level population density, welfare of the family, and high-region. Based on this research, we obtained 51 MGWR models. The MGWR model were divided into 4 groups with significant variable is house index as a global variable, an area width as a local variable and the remaining variables vary in each. Global variables are variables that significantly affect all locations, while local variables are variables that significantly affect a specific location.

Precision Interval Estimation of the Response Surface by Means of an Integrated Algorithm of Neural Network and Linear Regression

NASA Technical Reports Server (NTRS)

Lo, Ching F.

1999-01-01

The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.

Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear regression models.

PubMed

Zhang, Xin; Liu, Pan; Chen, Yuguang; Bai, Lu; Wang, Wei

2014-01-01

The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.

Determination of biodiesel content in biodiesel/diesel blends using NIR and visible spectroscopy with variable selection.

PubMed

Fernandes, David Douglas Sousa; Gomes, Adriano A; Costa, Gean Bezerra da; Silva, Gildo William B da; Véras, Germano

2011-12-15

This work is concerned of evaluate the use of visible and near-infrared (NIR) range, separately and combined, to determine the biodiesel content in biodiesel/diesel blends using Multiple Linear Regression (MLR) and variable selection by Successive Projections Algorithm (SPA). Full spectrum models employing Partial Least Squares (PLS) and variables selection by Stepwise (SW) regression coupled with Multiple Linear Regression (MLR) and PLS models also with variable selection by Jack-Knife (Jk) were compared the proposed methodology. Several preprocessing were evaluated, being chosen derivative Savitzky-Golay with second-order polynomial and 17-point window for NIR and visible-NIR range, with offset correction. A total of 100 blends with biodiesel content between 5 and 50% (v/v) prepared starting from ten sample of biodiesel. In the NIR and visible region the best model was the SPA-MLR using only two and eight wavelengths with RMSEP of 0.6439% (v/v) and 0.5741 respectively, while in the visible-NIR region the best model was the SW-MLR using five wavelengths and RMSEP of 0.9533% (v/v). Results indicate that both spectral ranges evaluated showed potential for developing a rapid and nondestructive method to quantify biodiesel in blends with mineral diesel. Finally, one can still mention that the improvement in terms of prediction error obtained with the procedure for variables selection was significant. Copyright © 2011 Elsevier B.V. All rights reserved.

Mental chronometry with simple linear regression.

PubMed

Chen, J Y

1997-10-01

Typically, mental chronometry is performed by means of introducing an independent variable postulated to affect selectively some stage of a presumed multistage process. However, the effect could be a global one that spreads proportionally over all stages of the process. Currently, there is no method to test this possibility although simple linear regression might serve the purpose. In the present study, the regression approach was tested with tasks (memory scanning and mental rotation) that involved a selective effect and with a task (word superiority effect) that involved a global effect, by the dominant theories. The results indicate (1) the manipulation of the size of a memory set or of angular disparity affects the intercept of the regression function that relates the times for memory scanning with different set sizes or for mental rotation with different angular disparities and (2) the manipulation of context affects the slope of the regression function that relates the times for detecting a target character under word and nonword conditions. These ratify the regression approach as a useful method for doing mental chronometry.

Reliable probabilities through statistical post-processing of ensemble predictions

NASA Astrophysics Data System (ADS)

Van Schaeybroeck, Bert; Vannitsem, Stéphane

2013-04-01

We develop post-processing or calibration approaches based on linear regression that make ensemble forecasts more reliable. We enforce climatological reliability in the sense that the total variability of the prediction is equal to the variability of the observations. Second, we impose ensemble reliability such that the spread around the ensemble mean of the observation coincides with the one of the ensemble members. In general the attractors of the model and reality are inhomogeneous. Therefore ensemble spread displays a variability not taken into account in standard post-processing methods. We overcome this by weighting the ensemble by a variable error. The approaches are tested in the context of the Lorenz 96 model (Lorenz 1996). The forecasts become more reliable at short lead times as reflected by a flatter rank histogram. Our best method turns out to be superior to well-established methods like EVMOS (Van Schaeybroeck and Vannitsem, 2011) and Nonhomogeneous Gaussian Regression (Gneiting et al., 2005). References [1] Gneiting, T., Raftery, A. E., Westveld, A., Goldman, T., 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Weather Rev. 133, 1098-1118. [2] Lorenz, E. N., 1996: Predictability - a problem partly solved. Proceedings, Seminar on Predictability ECMWF. 1, 1-18. [3] Van Schaeybroeck, B., and S. Vannitsem, 2011: Post-processing through linear regression, Nonlin. Processes Geophys., 18, 147.

Female Literacy Rate is a Better Predictor of Birth Rate and Infant Mortality Rate in India.

PubMed

Saurabh, Suman; Sarkar, Sonali; Pandey, Dhruv K

2013-01-01

Educated women are known to take informed reproductive and healthcare decisions. These result in population stabilization and better infant care reflected by lower birth rates and infant mortality rates (IMRs), respectively. Our objective was to study the relationship of male and female literacy rates with crude birth rates (CBRs) and IMRs of the states and union territories (UTs) of India. The data were analyzed using linear regression. CBR and IMR were taken as the dependent variables; while the overall literacy rates, male, and female literacy rates were the independent variables. CBRs were inversely related to literacy rates (slope parameter = -0.402, P < 0.001). On multiple linear regression with male and female literacy rates, a significant inverse relationship emerged between female literacy rate and CBR (slope = -0.363, P < 0.001), while male literacy rate was not significantly related to CBR (P = 0.674). IMR of the states were also inversely related to their literacy rates (slope = -1.254, P < 0.001). Multiple linear regression revealed a significant inverse relationship between IMR and female literacy (slope = -0.816, P = 0.031), whereas male literacy rate was not significantly related (P = 0.630). Female literacy is relatively highly important for both population stabilization and better infant health.

Building "e-rater"® Scoring Models Using Machine Learning Methods. Research Report. ETS RR-16-04

ERIC Educational Resources Information Center

Chen, Jing; Fife, James H.; Bejar, Isaac I.; Rupp, André A.

2016-01-01

The "e-rater"® automated scoring engine used at Educational Testing Service (ETS) scores the writing quality of essays. In the current practice, e-rater scores are generated via a multiple linear regression (MLR) model as a linear combination of various features evaluated for each essay and human scores as the outcome variable. This…

Estimating linear effects in ANOVA designs: the easy way.

PubMed

Pinhas, Michal; Tzelgov, Joseph; Ganor-Stern, Dana

2012-09-01

Research in cognitive science has documented numerous phenomena that are approximated by linear relationships. In the domain of numerical cognition, the use of linear regression for estimating linear effects (e.g., distance and SNARC effects) became common following Fias, Brysbaert, Geypens, and d'Ydewalle's (1996) study on the SNARC effect. While their work has become the model for analyzing linear effects in the field, it requires statistical analysis of individual participants and does not provide measures of the proportions of variability accounted for (cf. Lorch & Myers, 1990). In the present methodological note, using both the distance and SNARC effects as examples, we demonstrate how linear effects can be estimated in a simple way within the framework of repeated measures analysis of variance. This method allows for estimating effect sizes in terms of both slope and proportions of variability accounted for. Finally, we show that our method can easily be extended to estimate linear interaction effects, not just linear effects calculated as main effects.

Analysis of Student and School Level Variables Related to Mathematics Self-Efficacy Level Based on PISA 2012 Results for China-Shanghai, Turkey, and Greece

ERIC Educational Resources Information Center

Usta, H. Gonca

2016-01-01

This study aims to analyze the student and school level variables that affect students' self-efficacy levels in mathematics in China-Shanghai, Turkey, and Greece based on PISA 2012 results. In line with this purpose, the hierarchical linear regression model (HLM) was employed. The interschool variability is estimated at approximately 17% in…

Statistical structure of intrinsic climate variability under global warming

NASA Astrophysics Data System (ADS)

Zhu, Xiuhua; Bye, John; Fraedrich, Klaus

2017-04-01

Climate variability is often studied in terms of fluctuations with respect to the mean state, whereas the dependence between the mean and variability is rarely discussed. We propose a new climate metric to measure the relationship between means and standard deviations of annual surface temperature computed over non-overlapping 100-year segments. This metric is analyzed based on equilibrium simulations of the Max Planck Institute-Earth System Model (MPI-ESM): the last millennium climate (800-1799), the future climate projection following the A1B scenario (2100-2199), and the 3100-year unforced control simulation. A linear relationship is globally observed in the control simulation and thus termed intrinsic climate variability, which is most pronounced in the tropical region with negative regression slopes over the Pacific warm pool and positive slopes in the eastern tropical Pacific. It relates to asymmetric changes in temperature extremes and associates fluctuating climate means with increase or decrease in intensity and occurrence of both El Niño and La Niña events. In the future scenario period, the linear regression slopes largely retain their spatial structure with appreciable changes in intensity and geographical locations. Since intrinsic climate variability describes the internal rhythm of the climate system, it may serve as guidance for interpreting climate variability and climate change signals in the past and the future.

Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys

ERIC Educational Resources Information Center

Si, Yajuan; Reiter, Jerome P.

2013-01-01

In many surveys, the data comprise a large number of categorical variables that suffer from item nonresponse. Standard methods for multiple imputation, like log-linear models or sequential regression imputation, can fail to capture complex dependencies and can be difficult to implement effectively in high dimensions. We present a fully Bayesian,…

IN11B-1621: Quantifying How Climate Affects Vegetation in the Amazon Rainforest

NASA Technical Reports Server (NTRS)

Das, Kamalika; Kodali, Anuradha; Szubert, Marcin; Ganguly, Sangram; Bongard, Joshua

2016-01-01

Amazon droughts in 2005 and 2010 have raised serious concern about the future of the rainforest. Amazon forests are crucial because of their role as the largest carbon sink in the world which would effect the global warming phenomena with decreased photosynthesis activity. Especially, after a decline in plant growth in 1.68 million km2 forest area during the once-in-a-century severe drought in 2010, it is of primary importance to understand the relationship between different climatic variables and vegetation. In an earlier study, we have shown that non-linear models are better at capturing the relation dynamics of vegetation and climate variables such as temperature and precipitation, compared to linear models. In this research, we learn precise models between vegetation and climatic variables (temperature, precipitation) for normal conditions in the Amazon region using genetic programming based symbolic regression. This is done by removing high elevation and drought affected areas and also considering the slope of the region as one of the important factors while building the model. The model learned reveals new and interesting ways historical and current climate variables affect the vegetation at any location. MAIAC data has been used as a vegetation surrogate in our study. For temperature and precipitation, we have used TRMM and MODIS Land Surface Temperature data sets while learning the non-linear regression model. However, to generalize the model to make it independent of the data source, we perform transfer learning where we regress a regularized least squares to learn the parameters of the non-linear model using other data sources such as the precipitation and temperature from the Climatic Research Center (CRU). This new model is very similar in structure and performance compared to the original learned model and verifies the same claims about the nature of dependency between these climate variables and the vegetation in the Amazon region. As a result of this study, we are able to learn, for the very first time how exactly different climate factors influence vegetation at any location in the Amazon rainforests, independent of the specific sources from which the data has been obtained.

Quantifying How Climate Affects Vegetation in the Amazon Rainforest

NASA Astrophysics Data System (ADS)

Das, K.; Kodali, A.; Szubert, M.; Ganguly, S.; Bongard, J.

2016-12-01

Amazon droughts in 2005 and 2010 have raised serious concern about the future of the rainforest. Amazon forests are crucial because of their role as the largest carbon sink in the world which would effect the global warming phenomena with decreased photosynthesis activity. Especially, after a decline in plant growth in 1.68 million km2 forest area during the once-in-a-century severe drought in 2010, it is of primary importance to understand the relationship between different climatic variables and vegetation. In an earlier study, we have shown that non-linear models are better at capturing the relation dynamics of vegetation and climate variables such as temperature and precipitation, compared to linear models. In this research, we learn precise models between vegetation and climatic variables (temperature, precipitation) for normal conditions in the Amazon region using genetic programming based symbolic regression. This is done by removing high elevation and drought affected areas and also considering the slope of the region as one of the important factors while building the model. The model learned reveals new and interesting ways historical and current climate variables affect the vegetation at any location. MAIAC data has been used as a vegetation surrogate in our study. For temperature and precipitation, we have used TRMM and MODIS Land Surface Temperature data sets while learning the non-linear regression model. However, to generalize the model to make it independent of the data source, we perform transfer learning where we regress a regularized least squares to learn the parameters of the non-linear model using other data sources such as the precipitation and temperature from the Climatic Research Center (CRU). This new model is very similar in structure and performance compared to the original learned model and verifies the same claims about the nature of dependency between these climate variables and the vegetation in the Amazon region. As a result of this study, we are able to learn, for the very first time how exactly different climate factors influence vegetation at any location in the Amazon rainforests, independent of the specific sources from which the data has been obtained.

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after "controlling" for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

Prevalence of vitamin D deficiency and associated factors in women and newborns in the immediate postpartum period

PubMed Central

do Prado, Mara Rúbia Maciel Cardoso; Oliveira, Fabiana de Cássia Carvalho; Assis, Karine Franklin; Ribeiro, Sarah Aparecida Vieira; do Prado, Pedro Paulo; Sant'Ana, Luciana Ferreira da Rocha; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2015-01-01

Abstract Objective: To assess the prevalence of vitamin D deficiency and its associated factors in women and their newborns in the postpartum period. Methods: This cross-sectional study evaluated vitamin D deficiency/insufficiency in 226 women and their newborns in Viçosa (Minas Gerais, BR) between December 2011 and November 2012. Cord blood and venous maternal blood were collected to evaluate the following biochemical parameters: vitamin D, alkaline phosphatase, calcium, phosphorus and parathyroid hormone. Poisson regression analysis, with a confidence interval of 95%, was applied to assess vitamin D deficiency and its associated factors. Multiple linear regression analysis was performed to identify factors associated with 25(OH)D deficiency in the newborns and women from the study. The criteria for variable inclusion in the multiple linear regression model was the association with the dependent variable in the simple linear regression analysis, considering p<0.20. Significance level was α <5%. Results: From 226 women included, 200 (88.5%) were 20-44 years old; the median age was 28 years. Deficient/insufficient levels of vitamin D were found in 192 (85%) women and in 182 (80.5%) neonates. The maternal 25(OH)D and alkaline phosphatase levels were independently associated with vitamin D deficiency in infants. Conclusions: This study identified a high prevalence of vitamin D deficiency and insufficiency in women and newborns and the association between maternal nutritional status of vitamin D and their infants' vitamin D status. PMID:26100593

DOE Office of Scientific and Technical Information (OSTI.GOV)

Penna, M.L.; Duchiade, M.P.

The authors report the results of an investigation into the possible association between air pollution and infant mortality from pneumonia in the Rio de Janeiro Metropolitan Area. This investigation employed multiple linear regression analysis (stepwise method) for infant mortality from pneumonia in 1980, including the study population's areas of residence, incomes, and pollution exposure as independent variables. With the income variable included in the regression, a statistically significant association was observed between the average annual level of particulates and infant mortality from pneumonia. While this finding should be accepted with caution, it does suggest a biological association between these variables.more » The authors' conclusion is that air quality indicators should be included in studies of acute respiratory infections in developing countries.« less

The isoform A of reticulon-4 (Nogo-A) in cerebrospinal fluid of primary brain tumor patients: influencing factors.

PubMed

Koper, Olga Martyna; Kamińska, Joanna; Milewska, Anna; Sawicki, Karol; Mariak, Zenon; Kemona, Halina; Matowicka-Karna, Joanna

2018-05-18

The influence of isoform A of reticulon-4 (Nogo-A), also known as neurite outgrowth inhibitor, on primary brain tumor development was reported. Therefore the aim was the evaluation of Nogo-A concentrations in cerebrospinal fluid (CSF) and serum of brain tumor patients compared with non-tumoral individuals. All serum results, except for two cases, obtained both in brain tumors and non-tumoral individuals, were below the lower limit of ELISA detection. Cerebrospinal fluid Nogo-A concentrations were significantly lower in primary brain tumor patients compared to non-tumoral individuals. The univariate linear regression analysis found that if white blood cell count increases by 1 × 10 3 /μL, the mean cerebrospinal fluid Nogo-A concentration value decreases 1.12 times. In the model of multiple linear regression analysis predictor variables influencing cerebrospinal fluid Nogo-A concentrations included: diagnosis, sex, and sodium level. The mean cerebrospinal fluid Nogo-A concentration value was 1.9 times higher for women in comparison to men. In the astrocytic brain tumor group higher sodium level occurs with lower cerebrospinal fluid Nogo-A concentrations. We found the opposite situation in non-tumoral individuals. Univariate linear regression analysis revealed, that cerebrospinal fluid Nogo-A concentrations change in relation to white blood cell count. In the created model of multiple linear regression analysis we found, that within predictor variables influencing CSF Nogo-A concentrations were diagnosis, sex, and sodium level. Results may be relevant to the search for cerebrospinal fluid biomarkers and potential therapeutic targets in primary brain tumor patients. Nogo-A concentrations were tested by means of enzyme-linked immunosorbent assay (ELISA).

Addressing the identification problem in age-period-cohort analysis: a tutorial on the use of partial least squares and principal components analysis.

PubMed

Tu, Yu-Kang; Krämer, Nicole; Lee, Wen-Chung

2012-07-01

In the analysis of trends in health outcomes, an ongoing issue is how to separate and estimate the effects of age, period, and cohort. As these 3 variables are perfectly collinear by definition, regression coefficients in a general linear model are not unique. In this tutorial, we review why identification is a problem, and how this problem may be tackled using partial least squares and principal components regression analyses. Both methods produce regression coefficients that fulfill the same collinearity constraint as the variables age, period, and cohort. We show that, because the constraint imposed by partial least squares and principal components regression is inherent in the mathematical relation among the 3 variables, this leads to more interpretable results. We use one dataset from a Taiwanese health-screening program to illustrate how to use partial least squares regression to analyze the trends in body heights with 3 continuous variables for age, period, and cohort. We then use another dataset of hepatocellular carcinoma mortality rates for Taiwanese men to illustrate how to use partial least squares regression to analyze tables with aggregated data. We use the second dataset to show the relation between the intrinsic estimator, a recently proposed method for the age-period-cohort analysis, and partial least squares regression. We also show that the inclusion of all indicator variables provides a more consistent approach. R code for our analyses is provided in the eAppendix.

Effects of measurement errors on psychometric measurements in ergonomics studies: Implications for correlations, ANOVA, linear regression, factor analysis, and linear discriminant analysis.

PubMed

Liu, Yan; Salvendy, Gavriel

2009-05-01

This paper aims to demonstrate the effects of measurement errors on psychometric measurements in ergonomics studies. A variety of sources can cause random measurement errors in ergonomics studies and these errors can distort virtually every statistic computed and lead investigators to erroneous conclusions. The effects of measurement errors on five most widely used statistical analysis tools have been discussed and illustrated: correlation; ANOVA; linear regression; factor analysis; linear discriminant analysis. It has been shown that measurement errors can greatly attenuate correlations between variables, reduce statistical power of ANOVA, distort (overestimate, underestimate or even change the sign of) regression coefficients, underrate the explanation contributions of the most important factors in factor analysis and depreciate the significance of discriminant function and discrimination abilities of individual variables in discrimination analysis. The discussions will be restricted to subjective scales and survey methods and their reliability estimates. Other methods applied in ergonomics research, such as physical and electrophysiological measurements and chemical and biomedical analysis methods, also have issues of measurement errors, but they are beyond the scope of this paper. As there has been increasing interest in the development and testing of theories in ergonomics research, it has become very important for ergonomics researchers to understand the effects of measurement errors on their experiment results, which the authors believe is very critical to research progress in theory development and cumulative knowledge in the ergonomics field.

Solar cycle in current reanalyses: (non)linear attribution study

NASA Astrophysics Data System (ADS)

Kuchar, A.; Sacha, P.; Miksovsky, J.; Pisoft, P.

2014-12-01

This study focusses on the variability of temperature, ozone and circulation characteristics in the stratosphere and lower mesosphere with regard to the influence of the 11 year solar cycle. It is based on attribution analysis using multiple nonlinear techniques (Support Vector Regression, Neural Networks) besides the traditional linear approach. The analysis was applied to several current reanalysis datasets for the 1979-2013 period, including MERRA, ERA-Interim and JRA-55, with the aim to compare how this type of data resolves especially the double-peaked solar response in temperature and ozone variables and the consequent changes induced by these anomalies. Equatorial temperature signals in the lower and upper stratosphere were found to be sufficiently robust and in qualitative agreement with previous observational studies. The analysis also pointed to the solar signal in the ozone datasets (i.e. MERRA and ERA-Interim) not being consistent with the observed double-peaked ozone anomaly extracted from satellite measurements. Consequently the results obtained by linear regression were confirmed by the nonlinear approach through all datasets, suggesting that linear regression is a relevant tool to sufficiently resolve the solar signal in the middle atmosphere. Furthermore, the seasonal dependence of the solar response was also discussed, mainly as a source of dynamical causalities in the wave propagation characteristics in the zonal wind and the induced meridional circulation in the winter hemispheres. The hypothetical mechanism of a weaker Brewer Dobson circulation was reviewed together with discussion of polar vortex stability.

Normalization Approaches for Removing Systematic Biases Associated with Mass Spectrometry and Label-Free Proteomics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Callister, Stephen J.; Barry, Richard C.; Adkins, Joshua N.

2006-02-01

Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample setmore » were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias, assigned ranks among the techniques revealed significant trends. For most LC-FTICR MS analyses, linear regression normalization ranked either first or second among the four techniques, suggesting that this technique was more generally suitable for reducing systematic biases.« less

Simultaneous multiple non-crossing quantile regression estimation using kernel constraints

PubMed Central

Liu, Yufeng; Wu, Yichao

2011-01-01

Quantile regression (QR) is a very useful statistical tool for learning the relationship between the response variable and covariates. For many applications, one often needs to estimate multiple conditional quantile functions of the response variable given covariates. Although one can estimate multiple quantiles separately, it is of great interest to estimate them simultaneously. One advantage of simultaneous estimation is that multiple quantiles can share strength among them to gain better estimation accuracy than individually estimated quantile functions. Another important advantage of joint estimation is the feasibility of incorporating simultaneous non-crossing constraints of QR functions. In this paper, we propose a new kernel-based multiple QR estimation technique, namely simultaneous non-crossing quantile regression (SNQR). We use kernel representations for QR functions and apply constraints on the kernel coefficients to avoid crossing. Both unregularised and regularised SNQR techniques are considered. Asymptotic properties such as asymptotic normality of linear SNQR and oracle properties of the sparse linear SNQR are developed. Our numerical results demonstrate the competitive performance of our SNQR over the original individual QR estimation. PMID:22190842

Mapping the spatial pattern of temperate forest above ground biomass by integrating airborne lidar with Radarsat-2 imagery via geostatistical models

NASA Astrophysics Data System (ADS)

Li, Wang; Niu, Zheng; Gao, Shuai; Wang, Cheng

2014-11-01

Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar (SAR) are two competitive active remote sensing techniques in forest above ground biomass estimation, which is important for forest management and global climate change study. This study aims to further explore their capabilities in temperate forest above ground biomass (AGB) estimation by emphasizing the spatial auto-correlation of variables obtained from these two remote sensing tools, which is a usually overlooked aspect in remote sensing applications to vegetation studies. Remote sensing variables including airborne LiDAR metrics, backscattering coefficient for different SAR polarizations and their ratio variables for Radarsat-2 imagery were calculated. First, simple linear regression models (SLR) was established between the field-estimated above ground biomass and the remote sensing variables. Pearson's correlation coefficient (R2) was used to find which LiDAR metric showed the most significant correlation with the regression residuals and could be selected as co-variable in regression co-kriging (RCoKrig). Second, regression co-kriging was conducted by choosing the regression residuals as dependent variable and the LiDAR metric (Hmean) with highest R2 as co-variable. Third, above ground biomass over the study area was estimated using SLR model and RCoKrig model, respectively. The results for these two models were validated using the same ground points. Results showed that both of these two methods achieved satisfactory prediction accuracy, while regression co-kriging showed the lower estimation error. It is proved that regression co-kriging model is feasible and effective in mapping the spatial pattern of AGB in the temperate forest using Radarsat-2 data calibrated by airborne LiDAR metrics.

Statistical experiments using the multiple regression research for prediction of proper hardness in areas of phosphorus cast-iron brake shoes manufacturing

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Ratiu, S. A.; Rackov, M.; Penčić, M.

2018-01-01

Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. This article focuses on expressing the multiple linear regression model related to the hardness assurance by the chemical composition of the phosphorous cast irons destined to the brake shoes, having in view that the regression coefficients will illustrate the unrelated contributions of each independent variable towards predicting the dependent variable. In order to settle the multiple correlations between the hardness of the cast-iron brake shoes, and their chemical compositions several regression equations has been proposed. Is searched a mathematical solution which can determine the optimum chemical composition for the hardness desirable values. Starting from the above-mentioned affirmations two new statistical experiments are effectuated related to the values of Phosphorus [P], Manganese [Mn] and Silicon [Si]. Therefore, the regression equations, which describe the mathematical dependency between the above-mentioned elements and the hardness, are determined. As result, several correlation charts will be revealed.

Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral data

Treesearch

Andrew T. Hudak; Nicholas L. Crookston; Jeffrey S. Evans; Michael K. Falkowski; Alistair M. S. Smith; Paul E. Gessler; Penelope Morgan

2006-01-01

We compared the utility of discrete-return light detection and ranging (lidar) data and multispectral satellite imagery, and their integration, for modeling and mapping basal area and tree density across two diverse coniferous forest landscapes in north-central Idaho. We applied multiple linear regression models subset from a suite of 26 predictor variables derived...

Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs. NBER Working Paper No. 20405

ERIC Educational Resources Information Center

Gelman, Andrew; Imbens, Guido

2014-01-01

It is common in regression discontinuity analysis to control for high order (third, fourth, or higher) polynomials of the forcing variable. We argue that estimators for causal effects based on such methods can be misleading, and we recommend researchers do not use them, and instead use estimators based on local linear or quadratic polynomials or…

Properties of added variable plots in Cox's regression model.

PubMed

Lindkvist, M

2000-03-01

The added variable plot is useful for examining the effect of a covariate in regression models. The plot provides information regarding the inclusion of a covariate, and is useful in identifying influential observations on the parameter estimates. Hall et al. (1996) proposed a plot for Cox's proportional hazards model derived by regarding the Cox model as a generalized linear model. This paper proves and discusses properties of this plot. These properties make the plot a valuable tool in model evaluation. Quantities considered include parameter estimates, residuals, leverage, case influence measures and correspondence to previously proposed residuals and diagnostics.

Effects of categorization method, regression type, and variable distribution on the inflation of Type-I error rate when categorizing a confounding variable.

PubMed

Barnwell-Ménard, Jean-Louis; Li, Qing; Cohen, Alan A

2015-03-15

The loss of signal associated with categorizing a continuous variable is well known, and previous studies have demonstrated that this can lead to an inflation of Type-I error when the categorized variable is a confounder in a regression analysis estimating the effect of an exposure on an outcome. However, it is not known how the Type-I error may vary under different circumstances, including logistic versus linear regression, different distributions of the confounder, and different categorization methods. Here, we analytically quantified the effect of categorization and then performed a series of 9600 Monte Carlo simulations to estimate the Type-I error inflation associated with categorization of a confounder under different regression scenarios. We show that Type-I error is unacceptably high (>10% in most scenarios and often 100%). The only exception was when the variable categorized was a continuous mixture proxy for a genuinely dichotomous latent variable, where both the continuous proxy and the categorized variable are error-ridden proxies for the dichotomous latent variable. As expected, error inflation was also higher with larger sample size, fewer categories, and stronger associations between the confounder and the exposure or outcome. We provide online tools that can help researchers estimate the potential error inflation and understand how serious a problem this is. Copyright © 2014 John Wiley & Sons, Ltd.

An Integrated Method to Analyze Farm Vulnerability to Climatic and Economic Variability According to Farm Configurations and Farmers' Adaptations.

PubMed

Martin, Guillaume; Magne, Marie-Angélina; Cristobal, Magali San

2017-01-01

The need to adapt to decrease farm vulnerability to adverse contextual events has been extensively discussed on a theoretical basis. We developed an integrated and operational method to assess farm vulnerability to multiple and interacting contextual changes and explain how this vulnerability can best be reduced according to farm configurations and farmers' technical adaptations over time. Our method considers farm vulnerability as a function of the raw measurements of vulnerability variables (e.g., economic efficiency of production), the slope of the linear regression of these measurements over time, and the residuals of this linear regression. The last two are extracted from linear mixed models considering a random regression coefficient (an intercept common to all farms), a global trend (a slope common to all farms), a random deviation from the general mean for each farm, and a random deviation from the general trend for each farm. Among all possible combinations, the lowest farm vulnerability is obtained through a combination of high values of measurements, a stable or increasing trend and low variability for all vulnerability variables considered. Our method enables relating the measurements, trends and residuals of vulnerability variables to explanatory variables that illustrate farm exposure to climatic and economic variability, initial farm configurations and farmers' technical adaptations over time. We applied our method to 19 cattle (beef, dairy, and mixed) farms over the period 2008-2013. Selected vulnerability variables, i.e., farm productivity and economic efficiency, varied greatly among cattle farms and across years, with means ranging from 43.0 to 270.0 kg protein/ha and 29.4-66.0% efficiency, respectively. No farm had a high level, stable or increasing trend and low residuals for both farm productivity and economic efficiency of production. Thus, the least vulnerable farms represented a compromise among measurement value, trend, and variability of both performances. No specific combination of farmers' practices emerged for reducing cattle farm vulnerability to climatic and economic variability. In the least vulnerable farms, the practices implemented (stocking rate, input use…) were more consistent with the objective of developing the properties targeted (efficiency, robustness…). Our method can be used to support farmers with sector-specific and local insights about most promising farm adaptations.

An Integrated Method to Analyze Farm Vulnerability to Climatic and Economic Variability According to Farm Configurations and Farmers’ Adaptations

PubMed Central

Martin, Guillaume; Magne, Marie-Angélina; Cristobal, Magali San

2017-01-01

The need to adapt to decrease farm vulnerability to adverse contextual events has been extensively discussed on a theoretical basis. We developed an integrated and operational method to assess farm vulnerability to multiple and interacting contextual changes and explain how this vulnerability can best be reduced according to farm configurations and farmers’ technical adaptations over time. Our method considers farm vulnerability as a function of the raw measurements of vulnerability variables (e.g., economic efficiency of production), the slope of the linear regression of these measurements over time, and the residuals of this linear regression. The last two are extracted from linear mixed models considering a random regression coefficient (an intercept common to all farms), a global trend (a slope common to all farms), a random deviation from the general mean for each farm, and a random deviation from the general trend for each farm. Among all possible combinations, the lowest farm vulnerability is obtained through a combination of high values of measurements, a stable or increasing trend and low variability for all vulnerability variables considered. Our method enables relating the measurements, trends and residuals of vulnerability variables to explanatory variables that illustrate farm exposure to climatic and economic variability, initial farm configurations and farmers’ technical adaptations over time. We applied our method to 19 cattle (beef, dairy, and mixed) farms over the period 2008–2013. Selected vulnerability variables, i.e., farm productivity and economic efficiency, varied greatly among cattle farms and across years, with means ranging from 43.0 to 270.0 kg protein/ha and 29.4–66.0% efficiency, respectively. No farm had a high level, stable or increasing trend and low residuals for both farm productivity and economic efficiency of production. Thus, the least vulnerable farms represented a compromise among measurement value, trend, and variability of both performances. No specific combination of farmers’ practices emerged for reducing cattle farm vulnerability to climatic and economic variability. In the least vulnerable farms, the practices implemented (stocking rate, input use…) were more consistent with the objective of developing the properties targeted (efficiency, robustness…). Our method can be used to support farmers with sector-specific and local insights about most promising farm adaptations. PMID:28900435

Explanation of the variance in quality of life and activity capacity of patients with heart failure by laboratory data.

PubMed

Athanasopoulos, Leonidas V; Dritsas, Athanasios; Doll, Helen A; Cokkinos, Dennis V

2010-08-01

This study was conducted to explain the variance in quality of life (QoL) and activity capacity of patients with congestive heart failure from pathophysiological changes as estimated by laboratory data. Peak oxygen consumption (peak VO2) and ventilation (VE)/carbon dioxide output (VCO2) slope derived from cardiopulmonary exercise testing, plasma N-terminal prohormone of B-type natriuretic peptide (NT-proBNP), and echocardiographic markers [left atrium (LA), left ventricular ejection fraction (LVEF)] were measured in 62 patients with congestive heart failure, who also completed the Minnesota Living with Heart Failure Questionnaire and the Specific Activity Questionnaire. All regression models were adjusted for age and sex. On linear regression analysis, peak VO2 with P value less than 0.001, VE/VCO2 slope with P value less than 0.01, LVEF with P value less than 0.001, LA with P=0.001, and logNT-proBNP with P value less than 0.01 were found to be associated with QoL. On stepwise multiple linear regression, peak VO2 and LVEF continued to be predictive, accounting for 40% of the variability in Minnesota Living with Heart Failure Questionnaire score. On linear regression analysis, peak VO2 with P value less than 0.001, VE/VCO2 slope with P value less than 0.001, LVEF with P value less than 0.05, LA with P value less than 0.001, and logNT-proBNP with P value less than 0.001 were found to be associated with activity capacity. On stepwise multiple linear regression, peak VO2 and LA continued to be predictive, accounting for 53% of the variability in Specific Activity Questionnaire score. Peak VO2 is independently associated both with QoL and activity capacity. In addition to peak VO2, LVEF is independently associated with QoL, and LA with activity capacity.

Isolating the cow-specific part of residual energy intake in lactating dairy cows using random regressions.

PubMed

Fischer, A; Friggens, N C; Berry, D P; Faverdin, P

2018-07-01

The ability to properly assess and accurately phenotype true differences in feed efficiency among dairy cows is key to the development of breeding programs for improving feed efficiency. The variability among individuals in feed efficiency is commonly characterised by the residual intake approach. Residual feed intake is represented by the residuals of a linear regression of intake on the corresponding quantities of the biological functions that consume (or release) energy. However, the residuals include both, model fitting and measurement errors as well as any variability in cow efficiency. The objective of this study was to isolate the individual animal variability in feed efficiency from the residual component. Two separate models were fitted, in one the standard residual energy intake (REI) was calculated as the residual of a multiple linear regression of lactation average net energy intake (NEI) on lactation average milk energy output, average metabolic BW, as well as lactation loss and gain of body condition score. In the other, a linear mixed model was used to simultaneously fit fixed linear regressions and random cow levels on the biological traits and intercept using fortnight repeated measures for the variables. This method split the predicted NEI in two parts: one quantifying the population mean intercept and coefficients, and one quantifying cow-specific deviations in the intercept and coefficients. The cow-specific part of predicted NEI was assumed to isolate true differences in feed efficiency among cows. NEI and associated energy expenditure phenotypes were available for the first 17 fortnights of lactation from 119 Holstein cows; all fed a constant energy-rich diet. Mixed models fitting cow-specific intercept and coefficients to different combinations of the aforementioned energy expenditure traits, calculated on a fortnightly basis, were compared. The variance of REI estimated with the lactation average model represented only 8% of the variance of measured NEI. Among all compared mixed models, the variance of the cow-specific part of predicted NEI represented between 53% and 59% of the variance of REI estimated from the lactation average model or between 4% and 5% of the variance of measured NEI. The remaining 41% to 47% of the variance of REI estimated with the lactation average model may therefore reflect model fitting errors or measurement errors. In conclusion, the use of a mixed model framework with cow-specific random regressions seems to be a promising method to isolate the cow-specific component of REI in dairy cows.

Comparison of Linear and Non-linear Regression Analysis to Determine Pulmonary Pressure in Hyperthyroidism.

PubMed

Scarneciu, Camelia C; Sangeorzan, Livia; Rus, Horatiu; Scarneciu, Vlad D; Varciu, Mihai S; Andreescu, Oana; Scarneciu, Ioan

2017-01-01

This study aimed at assessing the incidence of pulmonary hypertension (PH) at newly diagnosed hyperthyroid patients and at finding a simple model showing the complex functional relation between pulmonary hypertension in hyperthyroidism and the factors causing it. The 53 hyperthyroid patients (H-group) were evaluated mainly by using an echocardiographical method and compared with 35 euthyroid (E-group) and 25 healthy people (C-group). In order to identify the factors causing pulmonary hypertension the statistical method of comparing the values of arithmetical means is used. The functional relation between the two random variables (PAPs and each of the factors determining it within our research study) can be expressed by linear or non-linear function. By applying the linear regression method described by a first-degree equation the line of regression (linear model) has been determined; by applying the non-linear regression method described by a second degree equation, a parabola-type curve of regression (non-linear or polynomial model) has been determined. We made the comparison and the validation of these two models by calculating the determination coefficient (criterion 1), the comparison of residuals (criterion 2), application of AIC criterion (criterion 3) and use of F-test (criterion 4). From the H-group, 47% have pulmonary hypertension completely reversible when obtaining euthyroidism. The factors causing pulmonary hypertension were identified: previously known- level of free thyroxin, pulmonary vascular resistance, cardiac output; new factors identified in this study- pretreatment period, age, systolic blood pressure. According to the four criteria and to the clinical judgment, we consider that the polynomial model (graphically parabola- type) is better than the linear one. The better model showing the functional relation between the pulmonary hypertension in hyperthyroidism and the factors identified in this study is given by a polynomial equation of second degree where the parabola is its graphical representation.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

USGS Publications Warehouse

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-01-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

NASA Astrophysics Data System (ADS)

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-12-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Methodological and Epistemological Issues on Linear Regression Applied to Psychometric Variables in Problem Solving: Rethinking Variance

ERIC Educational Resources Information Center

Stamovlasis, Dimitrios

2010-01-01

The aim of the present paper is two-fold. First, it attempts to support previous findings on the role of some psychometric variables, such as, M-capacity, the degree of field dependence-independence, logical thinking and the mobility-fixity dimension, on students' achievement in chemistry problem solving. Second, the paper aims to raise some…

Social determinants of childhood asthma symptoms: an ecological study in urban Latin America.

PubMed

Fattore, Gisel L; Santos, Carlos A T; Barreto, Mauricio L

2014-04-01

Asthma is an important public health problem in urban Latin America. This study aimed to analyze the role of socioeconomic and environmental factors as potential determinants of asthma symptoms prevalence in children from Latin American (LA) urban centers. We selected 31 LA urban centers with complete data, and an ecological analysis was performed. According to our theoretical framework, the explanatory variables were classified in three levels: distal, intermediate, and proximate. The association between variables in the three levels and prevalence of asthma symptoms was examined by bivariate and multivariate linear regression analysis weighed by sample size. In a second stage, we fitted several linear regression models introducing sequentially the variables according to the predefined hierarchy. In the final hierarchical model Gini Index, crowding, sanitation, variation in infant mortality rates and homicide rates, explained great part of the variance in asthma prevalence between centers (R(2) = 75.0 %). We found a strong association between socioeconomic and environmental variables and prevalence of asthma symptoms in LA urban children, and according to our hierarchical framework and the results found we suggest that social inequalities (measured by the Gini Index) is a central determinant to explain high prevalence of asthma in LA.

Confounding Problems in Multifactor AOV When Using Several Organismic Variables of Limited Reliability

ERIC Educational Resources Information Center

Games, Paul A.

1975-01-01

A brief introduction is presented on how multiple regression and linear model techniques can handle data analysis situations that most educators and psychologists think of as appropriate for analysis of variance. (Author/BJG)

Modeling Effects of Temperature, Soil, Moisture, Nutrition and Variety As Determinants of Severity of Pythium Damping-Off and Root Disease in Subterranean Clover

PubMed Central

You, Ming P.; Rensing, Kelly; Renton, Michael; Barbetti, Martin J.

2017-01-01

Subterranean clover (Trifolium subterraneum) is a critical pasture legume in Mediterranean regions of southern Australia and elsewhere, including Mediterranean-type climatic regions in Africa, Asia, Australia, Europe, North America, and South America. Pythium damping-off and root disease caused by Pythium irregulare is a significant threat to subterranean clover in Australia and a study was conducted to define how environmental factors (viz. temperature, soil type, moisture and nutrition) as well as variety, influence the extent of damping-off and root disease as well as subterranean clover productivity under challenge by this pathogen. Relationships were statistically modeled using linear and generalized linear models and boosted regression trees. Modeling found complex relationships between explanatory variables and the extent of Pythium damping-off and root rot. Linear modeling identified high-level (4 or 5-way) significant interactions for each dependent variable (dry shoot and root weight, emergence, tap and lateral root disease index). Furthermore, all explanatory variables (temperature, soil, moisture, nutrition, variety) were found significant as part of some interaction within these models. A significant five-way interaction between all explanatory variables was found for both dry shoot and root dry weights, and a four way interaction between temperature, soil, moisture, and nutrition was found for both tap and lateral root disease index. A second approach to modeling using boosted regression trees provided support for and helped clarify the complex nature of the relationships found in linear models. All explanatory variables showed at least 5% relative influence on each of the five dependent variables. All models indicated differences due to soil type, with the sand-based soil having either higher weights, greater emergence, or lower disease indices; while lowest weights and less emergence, as well as higher disease indices, were found for loam soil and low temperature. There was more severe tap and lateral root rot disease in higher moisture situations. PMID:29184544

Energy expenditure estimation during daily military routine with body-fixed sensors.

PubMed

Wyss, Thomas; Mäder, Urs

2011-05-01

The purpose of this study was to develop and validate an algorithm for estimating energy expenditure during the daily military routine on the basis of data collected using body-fixed sensors. First, 8 volunteers completed isolated physical activities according to an established protocol, and the resulting data were used to develop activity-class-specific multiple linear regressions for physical activity energy expenditure on the basis of hip acceleration, heart rate, and body mass as independent variables. Second, the validity of these linear regressions was tested during the daily military routine using indirect calorimetry (n = 12). Volunteers' mean estimated energy expenditure did not significantly differ from the energy expenditure measured with indirect calorimetry (p = 0.898, 95% confidence interval = -1.97 to 1.75 kJ/min). We conclude that the developed activity-class-specific multiple linear regressions applied to the acceleration and heart rate data allow estimation of energy expenditure in 1-minute intervals during daily military routine, with accuracy equal to indirect calorimetry.

Mixed effect Poisson log-linear models for clinical and epidemiological sleep hypnogram data

PubMed Central

Swihart, Bruce J.; Caffo, Brian S.; Crainiceanu, Ciprian; Punjabi, Naresh M.

2013-01-01

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased to non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis. PMID:22241689

Nonparametric regression applied to quantitative structure-activity relationships

PubMed

Constans; Hirst

2000-03-01

Several nonparametric regressors have been applied to modeling quantitative structure-activity relationship (QSAR) data. The simplest regressor, the Nadaraya-Watson, was assessed in a genuine multivariate setting. Other regressors, the local linear and the shifted Nadaraya-Watson, were implemented within additive models--a computationally more expedient approach, better suited for low-density designs. Performances were benchmarked against the nonlinear method of smoothing splines. A linear reference point was provided by multilinear regression (MLR). Variable selection was explored using systematic combinations of different variables and combinations of principal components. For the data set examined, 47 inhibitors of dopamine beta-hydroxylase, the additive nonparametric regressors have greater predictive accuracy (as measured by the mean absolute error of the predictions or the Pearson correlation in cross-validation trails) than MLR. The use of principal components did not improve the performance of the nonparametric regressors over use of the original descriptors, since the original descriptors are not strongly correlated. It remains to be seen if the nonparametric regressors can be successfully coupled with better variable selection and dimensionality reduction in the context of high-dimensional QSARs.

A comparison of methods to handle skew distributed cost variables in the analysis of the resource consumption in schizophrenia treatment.

PubMed

Kilian, Reinhold; Matschinger, Herbert; Löeffler, Walter; Roick, Christiane; Angermeyer, Matthias C

2002-03-01

Transformation of the dependent cost variable is often used to solve the problems of heteroscedasticity and skewness in linear ordinary least square regression of health service cost data. However, transformation may cause difficulties in the interpretation of regression coefficients and the retransformation of predicted values. The study compares the advantages and disadvantages of different methods to estimate regression based cost functions using data on the annual costs of schizophrenia treatment. Annual costs of psychiatric service use and clinical and socio-demographic characteristics of the patients were assessed for a sample of 254 patients with a diagnosis of schizophrenia (ICD-10 F 20.0) living in Leipzig. The clinical characteristics of the participants were assessed by means of the BPRS 4.0, the GAF, and the CAN for service needs. Quality of life was measured by WHOQOL-BREF. A linear OLS regression model with non-parametric standard errors, a log-transformed OLS model and a generalized linear model with a log-link and a gamma distribution were used to estimate service costs. For the estimation of robust non-parametric standard errors, the variance estimator by White and a bootstrap estimator based on 2000 replications were employed. Models were evaluated by the comparison of the R2 and the root mean squared error (RMSE). RMSE of the log-transformed OLS model was computed with three different methods of bias-correction. The 95% confidence intervals for the differences between the RMSE were computed by means of bootstrapping. A split-sample-cross-validation procedure was used to forecast the costs for the one half of the sample on the basis of a regression equation computed for the other half of the sample. All three methods showed significant positive influences of psychiatric symptoms and met psychiatric service needs on service costs. Only the log- transformed OLS model showed a significant negative impact of age, and only the GLM shows a significant negative influences of employment status and partnership on costs. All three models provided a R2 of about.31. The Residuals of the linear OLS model revealed significant deviances from normality and homoscedasticity. The residuals of the log-transformed model are normally distributed but still heteroscedastic. The linear OLS model provided the lowest prediction error and the best forecast of the dependent cost variable. The log-transformed model provided the lowest RMSE if the heteroscedastic bias correction was used. The RMSE of the GLM with a log link and a gamma distribution was higher than those of the linear OLS model and the log-transformed OLS model. The difference between the RMSE of the linear OLS model and that of the log-transformed OLS model without bias correction was significant at the 95% level. As result of the cross-validation procedure, the linear OLS model provided the lowest RMSE followed by the log-transformed OLS model with a heteroscedastic bias correction. The GLM showed the weakest model fit again. None of the differences between the RMSE resulting form the cross- validation procedure were found to be significant. The comparison of the fit indices of the different regression models revealed that the linear OLS model provided a better fit than the log-transformed model and the GLM, but the differences between the models RMSE were not significant. Due to the small number of cases in the study the lack of significance does not sufficiently proof that the differences between the RSME for the different models are zero and the superiority of the linear OLS model can not be generalized. The lack of significant differences among the alternative estimators may reflect a lack of sample size adequate to detect important differences among the estimators employed. Further studies with larger case number are necessary to confirm the results. Specification of an adequate regression models requires a careful examination of the characteristics of the data. Estimation of standard errors and confidence intervals by nonparametric methods which are robust against deviations from the normal distribution and the homoscedasticity of residuals are suitable alternatives to the transformation of the skew distributed dependent variable. Further studies with more adequate case numbers are needed to confirm the results.

Using ridge regression in systematic pointing error corrections

NASA Technical Reports Server (NTRS)

Guiar, C. N.

1988-01-01

A pointing error model is used in the antenna calibration process. Data from spacecraft or radio star observations are used to determine the parameters in the model. However, the regression variables are not truly independent, displaying a condition known as multicollinearity. Ridge regression, a biased estimation technique, is used to combat the multicollinearity problem. Two data sets pertaining to Voyager 1 spacecraft tracking (days 105 and 106 of 1987) were analyzed using both linear least squares and ridge regression methods. The advantages and limitations of employing the technique are presented. The problem is not yet fully resolved.

Classification of sodium MRI data of cartilage using machine learning.

PubMed

Madelin, Guillaume; Poidevin, Frederick; Makrymallis, Antonios; Regatte, Ravinder R

2015-11-01

To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: Support vector machine, k-nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested. Sodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification. Either Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes. Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data. © 2014 Wiley Periodicals, Inc.

Quantifying spatial and temporal variabilities of microwave brightness temperature over the U.S. Southern Great Plains

NASA Technical Reports Server (NTRS)

Choudhury, B. J.; Owe, M.; Ormsby, J. P.; Chang, A. T. C.; Wang, J. R.; Goward, S. N.; Golus, R. E.

1987-01-01

Spatial and temporal variabilities of microwave brightness temperature over the U.S. Southern Great Plains are quantified in terms of vegetation and soil wetness. The brightness temperatures (TB) are the daytime observations from April to October for five years (1979 to 1983) obtained by the Nimbus-7 Scanning Multichannel Microwave Radiometer at 6.6 GHz frequency, horizontal polarization. The spatial and temporal variabilities of vegetation are assessed using visible and near-infrared observations by the NOAA-7 Advanced Very High Resolution Radiometer (AVHRR), while an Antecedent Precipitation Index (API) model is used for soil wetness. The API model was able to account for more than 50 percent of the observed variability in TB, although linear correlations between TB and API were generally significant at the 1 percent level. The slope of the linear regression between TB and API is found to correlate linearly with an index for vegetation density derived from AVHRR data.

Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000-2015 using quantile and multiple line regression models

NASA Astrophysics Data System (ADS)

Zhao, Wei; Fan, Shaojia; Guo, Hai; Gao, Bo; Sun, Jiaren; Chen, Laiguo

2016-11-01

The quantile regression (QR) method has been increasingly introduced to atmospheric environmental studies to explore the non-linear relationship between local meteorological conditions and ozone mixing ratios. In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000-2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.

Normality of Residuals Is a Continuous Variable, and Does Seem to Influence the Trustworthiness of Confidence Intervals: A Response to, and Appreciation of, Williams, Grajales, and Kurkiewicz (2013)

ERIC Educational Resources Information Center

Osborne, Jason W.

2013-01-01

Osborne and Waters (2002) focused on checking some of the assumptions of multiple linear regression. In a critique of that paper, Williams, Grajales, and Kurkiewicz correctly clarify that regression models estimated using ordinary least squares require the assumption of normally distributed errors, but not the assumption of normally distributed…

Comparison of stream invertebrate response models for bioassessment metric

USGS Publications Warehouse

Waite, Ian R.; Kennen, Jonathan G.; May, Jason T.; Brown, Larry R.; Cuffney, Thomas F.; Jones, Kimberly A.; Orlando, James L.

2012-01-01

We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R2 from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R2 values than RICHTOL for the two regions tested. Modeled O/E R2 values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.

Race-ethnicity is a strong correlate of circulating fat-soluble nutrient concentrations in a representative sample of the U.S. population.

PubMed

Schleicher, Rosemary L; Sternberg, Maya R; Pfeiffer, Christine M

2013-06-01

Sociodemographic and lifestyle factors exert important influences on nutritional status; however, information on their association with biomarkers of fat-soluble nutrients is limited, particularly in a representative sample of adults. Serum or plasma concentrations of vitamin A, vitamin E, carotenes, xanthophylls, 25-hydroxyvitamin D [25(OH)D], SFAs, MUFAs, PUFAs, and total fatty acids (tFAs) were measured in adults (aged ≥ 20 y) during all or part of NHANES 2003-2006. Simple and multiple linear regression models were used to assess 5 sociodemographic variables (age, sex, race-ethnicity, education, and income) and 5 lifestyle behaviors (smoking, alcohol consumption, BMI, physical activity, and supplement use) and their relation to biomarker concentrations. Adjustment for total serum cholesterol and lipid-altering drug use was added to the full regression model. Adjustment for latitude and season was added to the full model for 25(OH)D. Based on simple linear regression, race-ethnicity, BMI, and supplement use were significantly related to all fat-soluble biomarkers. Sociodemographic variables as a group explained 5-17% of biomarker variability, whereas together, sociodemographic and lifestyle variables explained 22-23% [25(OH)D, vitamin E, xanthophylls], 17% (vitamin A), 15% (MUFAs), 10-11% (SFAs, carotenes, tFAs), and 6% (PUFAs) of biomarker variability. Although lipid adjustment explained additional variability for all biomarkers except for 25(OH)D, it appeared to be largely independent of sociodemographic and lifestyle variables. After adjusting for sociodemographic, lifestyle, and lipid-related variables, major differences in biomarkers were associated with race-ethnicity (from -44 to 57%), smoking (up to -25%), supplement use (up to 21%), and BMI (up to -15%). Latitude and season attenuated some race-ethnicity differences. Of the sociodemographic and lifestyle variables examined, with or without lipid adjustment, most fat-soluble nutrient biomarkers were significantly associated with race-ethnicity.

Multiple Linear Regression Analysis of Factors Affecting Real Property Price Index From Case Study Research In Istanbul/Turkey

NASA Astrophysics Data System (ADS)

Denli, H. H.; Koc, Z.

2015-12-01

Estimation of real properties depending on standards is difficult to apply in time and location. Regression analysis construct mathematical models which describe or explain relationships that may exist between variables. The problem of identifying price differences of properties to obtain a price index can be converted into a regression problem, and standard techniques of regression analysis can be used to estimate the index. Considering regression analysis for real estate valuation, which are presented in real marketing process with its current characteristics and quantifiers, the method will help us to find the effective factors or variables in the formation of the value. In this study, prices of housing for sale in Zeytinburnu, a district in Istanbul, are associated with its characteristics to find a price index, based on information received from a real estate web page. The associated variables used for the analysis are age, size in m2, number of floors having the house, floor number of the estate and number of rooms. The price of the estate represents the dependent variable, whereas the rest are independent variables. Prices from 60 real estates have been used for the analysis. Same price valued locations have been found and plotted on the map and equivalence curves have been drawn identifying the same valued zones as lines.

Female Literacy Rate is a Better Predictor of Birth Rate and Infant Mortality Rate in India

PubMed Central

Saurabh, Suman; Sarkar, Sonali; Pandey, Dhruv K.

2013-01-01

Background: Educated women are known to take informed reproductive and healthcare decisions. These result in population stabilization and better infant care reflected by lower birth rates and infant mortality rates (IMRs), respectively. Materials and Methods: Our objective was to study the relationship of male and female literacy rates with crude birth rates (CBRs) and IMRs of the states and union territories (UTs) of India. The data were analyzed using linear regression. CBR and IMR were taken as the dependent variables; while the overall literacy rates, male, and female literacy rates were the independent variables. Results: CBRs were inversely related to literacy rates (slope parameter = −0.402, P < 0.001). On multiple linear regression with male and female literacy rates, a significant inverse relationship emerged between female literacy rate and CBR (slope = −0.363, P < 0.001), while male literacy rate was not significantly related to CBR (P = 0.674). IMR of the states were also inversely related to their literacy rates (slope = −1.254, P < 0.001). Multiple linear regression revealed a significant inverse relationship between IMR and female literacy (slope = −0.816, P = 0.031), whereas male literacy rate was not significantly related (P = 0.630). Conclusion: Female literacy is relatively highly important for both population stabilization and better infant health. PMID:26664840

Modeling relationships between catchment attributes and river water quality in southern catchments of the Caspian Sea.

PubMed

Hasani Sangani, Mohammad; Jabbarian Amiri, Bahman; Alizadeh Shabani, Afshin; Sakieh, Yousef; Ashrafi, Sohrab

2015-04-01

Increasing land utilization through diverse forms of human activities, such as agriculture, forestry, urban growth, and industrial development, has led to negative impacts on the water quality of rivers. To find out how catchment attributes, such as land use, hydrologic soil groups, and lithology, can affect water quality variables (Ca(2+), Mg(2+), Na(+), Cl(-), HCO 3 (-) , pH, TDS, EC, SAR), a spatio-statistical approach was applied to 23 catchments in southern basins of the Caspian Sea. All input data layers (digital maps of land use, soil, and lithology) were prepared using geographic information system (GIS) and spatial analysis. Relationships between water quality variables and catchment attributes were then examined by Spearman rank correlation tests and multiple linear regression. Stepwise approach-based multiple linear regressions were developed to examine the relationship between catchment attributes and water quality variables. The areas (%) of marl, tuff, or diorite, as well as those of good-quality rangeland and bare land had negative effects on all water quality variables, while those of basalt, forest land cover were found to contribute to improved river water quality. Moreover, lithological variables showed the greatest most potential for predicting the mean concentration values of water quality variables, and noting that measure of EC and TDS have inversely associated with area (%) of urban land use.

The prediction of intelligence in preschool children using alternative models to regression.

PubMed

Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E

2011-12-01

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.

[Prevalence of vitamin D deficiency and associated factors in women and newborns in the immediate postpartum period].

PubMed

do Prado, Mara Rúbia Maciel Cardoso; Oliveira, Fabiana de Cássia Carvalho; Assis, Karine Franklin; Ribeiro, Sarah Aparecida Vieira; do Prado Junior, Pedro Paulo; Sant'Ana, Luciana Ferreira da Rocha; Priore, Silvia Eloiza; Franceschini, Sylvia do Carmo Castro

2015-01-01

To assess the prevalence of vitamin D deficiency and its associated factors in women and their newborns in the postpartum period. This cross-sectional study evaluated vitamin D deficiency/insufficiency in 226 women and their newborns in Viçosa (Minas Gerais, BR) between December 2011 and November 2012. Cord blood and venous maternal blood were collected to evaluate the following biochemical parameters: vitamin D, alkaline phosphatase, calcium, phosphorus and parathyroid hormone. Poisson regression analysis, with a confidence interval of 95% was applied to assess vitamin D deficiency and its associated factors. Multiple linear regression analysis was performed to identify factors associated with 25(OH)D deficiency in the newborns and women from the study. The criteria for variable inclusion in the multiple linear regression model was the association with the dependent variable in the simple linear regression analysis, considering p<0.20. Significance level was α<5%. From 226 women included, 200 (88.5%) were 20 to 44 years old; the median age was 28 years. Deficient/insufficient levels of vitamin D were found in 192 (85%) women and in 182 (80.5%) neonates. The maternal 25(OH)D and alkaline phosphatase levels were independently associated with vitamin D deficiency in infants. This study identified a high prevalence of vitamin D deficiency and insufficiency in women and newborns and the association between maternal nutritional status of vitamin D and their infants' vitamin D status. Copyright © 2015 Sociedade de Pediatria de São Paulo. Publicado por Elsevier Editora Ltda. All rights reserved.

Advanced microwave soil moisture studies. [Big Sioux River Basin, Iowa

NASA Technical Reports Server (NTRS)

Dalsted, K. J.; Harlan, J. C.

1983-01-01

Comparisons of low level L-band brightness temperature (TB) and thermal infrared (TIR) data as well as the following data sets: soil map and land cover data; direct soil moisture measurement; and a computer generated contour map were statistically evaluated using regression analysis and linear discriminant analysis. Regression analysis of footprint data shows that statistical groupings of ground variables (soil features and land cover) hold promise for qualitative assessment of soil moisture and for reducing variance within the sampling space. Dry conditions appear to be more conductive to producing meaningful statistics than wet conditions. Regression analysis using field averaged TB and TIR data did not approach the higher sq R values obtained using within-field variations. The linear discriminant analysis indicates some capacity to distinguish categories with the results being somewhat better on a field basis than a footprint basis.

High school science enrollment of black students

NASA Astrophysics Data System (ADS)

Goggins, Ellen O.; Lindbeck, Joy S.

How can the high school science enrollment of black students be increased? School and home counseling and classroom procedures could benefit from variables identified as predictors of science enrollment. The problem in this study was to identify a set of variables which characterize science course enrollment by black secondary students. The population consisted of a subsample of 3963 black high school seniors from The High School and Beyond 1980 Base-Year Survey. Using multiple linear regression, backward regression, and correlation analyses, the US Census regions and grades mostly As and Bs in English were found to be significant predictors of the number of science courses scheduled by black seniors.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa.

PubMed

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-06-14

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

PubMed Central

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-01-01

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of KnowledgeSM databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably. PMID:27314369

Race-ethnicity is a strong correlate of circulating fat-soluble nutrient concentrations in a representative sample of the US population1,2,3

PubMed Central

Schleicher, Rosemary L; Sternberg, Maya R; Pfeiffer, Christine M

2016-01-01

Sociodemographic and lifestyle factors exert important influences on nutritional status; however, information on their association with biomarkers of fat-soluble nutrients is limited, particularly in a representative sample of adults. Serum or plasma concentrations of vitamin A (VIA), vitamin E (VIE), carotenes (CAR), xanthophylls (XAN), 25-hydroxyvitamin D (25OHD), saturated- (SFA), monounsaturated- (MUFA), polyunsaturated- (PUFA) and total fatty acids (tFA) were measured in adults (≥20 y) during all or part of NHANES 2003–2006. Simple and multiple linear regression were used to assess 5 sociodemographic variables (age, sex, race-ethnicity, education, income) and 5 lifestyle behaviors (smoking, alcohol consumption, BMI, physical activity, supplement use) and their relation to biomarker concentrations. Adjustment for total serum cholesterol and lipid-altering drug use was added to the full regression model. Adjustment for latitude and season was added to the full model for 25OHD. Based on simple linear regression, race-ethnicity, BMI and supplement use were significantly related to all fat-soluble biomarkers. Sociodemographic variables as a groupexplained 5–17% of biomarker variability, whereas together, sociodemographic and lifestyle variables explained 22–23% (25OHD, VIE, XAN), 17% (VIA), 15% (MUFA), 10–11% (SFA, CAR, tFA) and 6% (PUFA). Although lipid adjustment explained additional variability for all biomarkers except 25OHD, it appeared to be largely independent of sociodemographic and lifestyle variables. After adjusting for sociodemographic, lifestyle and lipid-related variables, major differences in biomarkers were associated with race-ethnicity (from −44% to 57%); smoking (up to −25%); supplement use (up to 21%); and BMI (up to −15%). Latitude and season attenuated some race-ethnic differences. Of the sociodemographic and lifestyle variables examined, with or without lipid-adjustment, most fat-soluble nutrient biomarkers were significantly associated with race-ethnicity. PMID:23596163

Partitioning sources of variation in vertebrate species richness

USGS Publications Warehouse

Boone, R.B.; Krohn, W.B.

2000-01-01

Aim: To explore biogeographic patterns of terrestrial vertebrates in Maine, USA using techniques that would describe local and spatial correlations with the environment. Location: Maine, USA. Methods: We delineated the ranges within Maine (86,156 km2) of 275 species using literature and expert review. Ranges were combined into species richness maps, and compared to geomorphology, climate, and woody plant distributions. Methods were adapted that compared richness of all vertebrate classes to each environmental correlate, rather than assessing a single explanatory theory. We partitioned variation in species richness into components using tree and multiple linear regression. Methods were used that allowed for useful comparisons between tree and linear regression results. For both methods we partitioned variation into broad-scale (spatially autocorrelated) and fine-scale (spatially uncorrelated) explained and unexplained components. By partitioning variance, and using both tree and linear regression in analyses, we explored the degree of variation in species richness for each vertebrate group that Could be explained by the relative contribution of each environmental variable. Results: In tree regression, climate variation explained richness better (92% of mean deviance explained for all species) than woody plant variation (87%) and geomorphology (86%). Reptiles were highly correlated with environmental variation (93%), followed by mammals, amphibians, and birds (each with 84-82% deviance explained). In multiple linear regression, climate was most closely associated with total vertebrate richness (78%), followed by woody plants (67%) and geomorphology (56%). Again, reptiles were closely correlated with the environment (95%), followed by mammals (73%), amphibians (63%) and birds (57%). Main conclusions: Comparing variation explained using tree and multiple linear regression quantified the importance of nonlinear relationships and local interactions between species richness and environmental variation, identifying the importance of linear relationships between reptiles and the environment, and nonlinear relationships between birds and woody plants, for example. Conservation planners should capture climatic variation in broad-scale designs; temperatures may shift during climate change, but the underlying correlations between the environment and species richness will presumably remain.

Study of process variables associated with manufacturing hermetically-sealed nickel-cadmium cells

NASA Technical Reports Server (NTRS)

Miller, L.; Doan, D. J.; Carr, E. S.

1971-01-01

A program to determine and study the critical process variables associated with the manufacture of aerospace, hermetically-sealed, nickel-cadmium cells is described. The determination and study of the process variables associated with the positive and negative plaque impregnation/polarization process are emphasized. The experimental data resulting from the implementation of fractional factorial design experiments are analyzed by means of a linear multiple regression analysis technique. This analysis permits the selection of preferred levels for certain process variables to achieve desirable impregnated plaque characteristics.

Uncertainty of streamwater solute fluxes in five contrasting headwater catchments including model uncertainty and natural variability (Invited)

NASA Astrophysics Data System (ADS)

Aulenbach, B. T.; Burns, D. A.; Shanley, J. B.; Yanai, R. D.; Bae, K.; Wild, A.; Yang, Y.; Dong, Y.

2013-12-01

There are many sources of uncertainty in estimates of streamwater solute flux. Flux is the product of discharge and concentration (summed over time), each of which has measurement uncertainty of its own. Discharge can be measured almost continuously, but concentrations are usually determined from discrete samples, which increases uncertainty dependent on sampling frequency and how concentrations are assigned for the periods between samples. Gaps between samples can be estimated by linear interpolation or by models that that use the relations between concentration and continuously measured or known variables such as discharge, season, temperature, and time. For this project, developed in cooperation with QUEST (Quantifying Uncertainty in Ecosystem Studies), we evaluated uncertainty for three flux estimation methods and three different sampling frequencies (monthly, weekly, and weekly plus event). The constituents investigated were dissolved NO3, Si, SO4, and dissolved organic carbon (DOC), solutes whose concentration dynamics exhibit strongly contrasting behavior. The evaluation was completed for a 10-year period at five small, forested watersheds in Georgia, New Hampshire, New York, Puerto Rico, and Vermont. Concentration regression models were developed for each solute at each of the three sampling frequencies for all five watersheds. Fluxes were then calculated using (1) a linear interpolation approach, (2) a regression-model method, and (3) the composite method - which combines the regression-model method for estimating concentrations and the linear interpolation method for correcting model residuals to the observed sample concentrations. We considered the best estimates of flux to be derived using the composite method at the highest sampling frequencies. We also evaluated the importance of sampling frequency and estimation method on flux estimate uncertainty; flux uncertainty was dependent on the variability characteristics of each solute and varied for different reporting periods (e.g. 10-year, study period vs. annually vs. monthly). The usefulness of the two regression model based flux estimation approaches was dependent upon the amount of variance in concentrations the regression models could explain. Our results can guide the development of optimal sampling strategies by weighing sampling frequency with improvements in uncertainty in stream flux estimates for solutes with particular characteristics of variability. The appropriate flux estimation method is dependent on a combination of sampling frequency and the strength of concentration regression models. Sites: Biscuit Brook (Frost Valley, NY), Hubbard Brook Experimental Forest and LTER (West Thornton, NH), Luquillo Experimental Forest and LTER (Luquillo, Puerto Rico), Panola Mountain (Stockbridge, GA), Sleepers River Research Watershed (Danville, VT)

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

PubMed

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

A New SEYHAN's Approach in Case of Heterogeneity of Regression Slopes in ANCOVA.

PubMed

Ankarali, Handan; Cangur, Sengul; Ankarali, Seyit

2018-06-01

In this study, when the assumptions of linearity and homogeneity of regression slopes of conventional ANCOVA are not met, a new approach named as SEYHAN has been suggested to use conventional ANCOVA instead of robust or nonlinear ANCOVA. The proposed SEYHAN's approach involves transformation of continuous covariate into categorical structure when the relationship between covariate and dependent variable is nonlinear and the regression slopes are not homogenous. A simulated data set was used to explain SEYHAN's approach. In this approach, we performed conventional ANCOVA in each subgroup which is constituted according to knot values and analysis of variance with two-factor model after MARS method was used for categorization of covariate. The first model is a simpler model than the second model that includes interaction term. Since the model with interaction effect has more subjects, the power of test also increases and the existing significant difference is revealed better. We can say that linearity and homogeneity of regression slopes are not problem for data analysis by conventional linear ANCOVA model by helping this approach. It can be used fast and efficiently for the presence of one or more covariates.

Forecasting daily patient volumes in the emergency department.

PubMed

Jones, Spencer S; Thomas, Alun; Evans, R Scott; Welch, Shari J; Haug, Peter J; Snow, Gregory L

2008-02-01

Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.

Does Nonlinear Modeling Play a Role in Plasmid Bioprocess Monitoring Using Fourier Transform Infrared Spectra?

PubMed

Lopes, Marta B; Calado, Cecília R C; Figueiredo, Mário A T; Bioucas-Dias, José M

2017-06-01

The monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid. Despite being successful in the presence of small nonlinearities, linear methods may fail in the presence of strong nonlinearities. This paper studies the potential usefulness of nonlinear regression methods for predicting, from in situ near-infrared (NIR) and mid-infrared (MIR) spectra acquired in high-throughput mode, biomass and plasmid concentrations in Escherichia coli DH5-α cultures producing the plasmid model pVAX-LacZ. The linear methods PLS and ridge regression (RR) are compared with their kernel (nonlinear) versions, kPLS and kRR, as well as with the (also nonlinear) relevance vector machine (RVM) and Gaussian process regression (GPR). For the systems studied, RR provided better predictive performances compared to the remaining methods. Moreover, the results point to further investigation based on larger data sets whenever differences in predictive accuracy between a linear method and its kernelized version could not be found. The use of nonlinear methods, however, shall be judged regarding the additional computational cost required to tune their additional parameters, especially when the less computationally demanding linear methods herein studied are able to successfully monitor the variables under study.

Psychosocial factors and financial literacy.

PubMed

Murphy, John L

2013-01-01

This study uses data from the Health and Retirement Study (HRS) to analyze the psychological and social variables associated with financial literacy. The HRS is a nationally representative longitudinal survey of individuals older than age 50 and their spouses. An ordinary least squares linear regression analysis explores the relationship between financial literacy and several economic and psychosocial variables. After controlling for earnings, level of education, and other socioeconomic variables in this exploratory study, I find that financial satisfaction and religiosity are correlated with financial literacy.

Simple agrometeorological models for estimating Guineagrass yield in Southeast Brazil.

PubMed

Pezzopane, José Ricardo Macedo; da Cruz, Pedro Gomes; Santos, Patricia Menezes; Bosi, Cristiam; de Araujo, Leandro Coelho

2014-09-01

The objective of this work was to develop and evaluate agrometeorological models to simulate the production of Guineagrass. For this purpose, we used forage yield from 54 growing periods between December 2004-January 2007 and April 2010-March 2012 in irrigated and non-irrigated pastures in São Carlos, São Paulo state, Brazil (latitude 21°57'42″ S, longitude 47°50'28″ W and altitude 860 m). Initially we performed linear regressions between the agrometeorological variables and the average dry matter accumulation rate for irrigated conditions. Then we determined the effect of soil water availability on the relative forage yield considering irrigated and non-irrigated pastures, by means of segmented linear regression among water balance and relative production variables (dry matter accumulation rates with and without irrigation). The models generated were evaluated with independent data related to 21 growing periods without irrigation in the same location, from eight growing periods in 2000 and 13 growing periods between December 2004-January 2007 and April 2010-March 2012. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, minimum temperature and potential evapotranspiration or degreedays) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on minimum temperature corrected by relative soil water storage, determined by the ratio between the actual soil water storage and the soil water holding capacity.irrigation in the same location, in 2000, 2010 and 2011. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, potential evapotranspiration or degree-days) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on degree-days corrected by the water deficit factor.

Combining fixed effects and instrumental variable approaches for estimating the effect of psychosocial job quality on mental health: evidence from 13 waves of a nationally representative cohort study.

PubMed

Milner, Allison; Aitken, Zoe; Kavanagh, Anne; LaMontagne, Anthony D; Pega, Frank; Petrie, Dennis

2017-06-23

Previous studies suggest that poor psychosocial job quality is a risk factor for mental health problems, but they use conventional regression analytic methods that cannot rule out reverse causation, unmeasured time-invariant confounding and reporting bias. This study combines two quasi-experimental approaches to improve causal inference by better accounting for these biases: (i) linear fixed effects regression analysis and (ii) linear instrumental variable analysis. We extract 13 annual waves of national cohort data including 13 260 working-age (18-64 years) employees. The exposure variable is self-reported level of psychosocial job quality. The instruments used are two common workplace entitlements. The outcome variable is the Mental Health Inventory (MHI-5). We adjust for measured time-varying confounders. In the fixed effects regression analysis adjusted for time-varying confounders, a 1-point increase in psychosocial job quality is associated with a 1.28-point improvement in mental health on the MHI-5 scale (95% CI: 1.17, 1.40; P < 0.001). When the fixed effects was combined with the instrumental variable analysis, a 1-point increase psychosocial job quality is related to 1.62-point improvement on the MHI-5 scale (95% CI: -0.24, 3.48; P = 0.088). Our quasi-experimental results provide evidence to confirm job stressors as risk factors for mental ill health using methods that improve causal inference. © The Author 2017. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Ozone and sulfur dioxide effects on three tall fescue cultivars

DOE Office of Scientific and Technical Information (OSTI.GOV)

Flagler, R.B.; Youngner, V.B.

Although many reports have been published concerning differential susceptibility of various crops and/or cultivars to air pollutants, most have used foliar injury instead of the marketable yield as the factor that determined susceptibility for the crop. In an examination of screening in terms of marketable yield, three cultivars of tall fescue (Festuca arundinacea Schreb.), 'Alta,' 'Fawn,' and 'Kentucky 31,' were exposed to 0-0.40 ppm O/sub 3/ or 0-0.50 ppm SO/sub 2/ 6 h/d, once a week, for 7 and 9 weeks, respectively. Experimental design was a randomized complete block with three replications. Statistical analysis was by standard analysis of variancemore » and regression techniques. Three variables were analyzed: top dry weight (yield), tiller number, and weight per tiller. Ozone had a significant effect on all three variables. Significant linear decreases in yield and weight per tiller occurred with increasing O/sub 3/ concentrations. Linear regressions of these variables on O/sub 3/ concentration produced significantly different regression coefficients. The coefficient for Kentucky 31 was significantly greater than Alta or Fawn, which did not differ from each other. This indicated that Kentucky 31 was more susceptible to O/sub 3/ than either of the other cultivars. Percent reductions in dry weight for the three cultivars at highest O/sub 3/ level were 35, 44, and 53%, respectively, for Fawn, Alta, and Kentucky 31. For weight per tiller, Kentucky 31 had a higher percent reduction than the other cultivars (59 vs. 46 and 44%). Tiller number was generally increased by O/sub 3/, but this variable was not useful for determining differential susceptibility to the pollutant. Sulfur dioxide treatments produced no significant effects on any of the variables analyzed.« less

Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations.

PubMed

Hayes, Andrew F; Matthes, Jörg

2009-08-01

Researchers often hypothesize moderated effects, in which the effect of an independent variable on an outcome variable depends on the value of a moderator variable. Such an effect reveals itself statistically as an interaction between the independent and moderator variables in a model of the outcome variable. When an interaction is found, it is important to probe the interaction, for theories and hypotheses often predict not just interaction but a specific pattern of effects of the focal independent variable as a function of the moderator. This article describes the familiar pick-a-point approach and the much less familiar Johnson-Neyman technique for probing interactions in linear models and introduces macros for SPSS and SAS to simplify the computations and facilitate the probing of interactions in ordinary least squares and logistic regression. A script version of the SPSS macro is also available for users who prefer a point-and-click user interface rather than command syntax.

Meteorological Modes of Variability for Fine Particulate Matter (PM2.5) Air Quality in the United States: Implications for PM2.5 Sensitivity to Climate Change

EPA Science Inventory

We applied a multiple linear regression model to understand the relationships of PM_2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM_2.5 to climate change. We used 2004-2008 PM_2.5 observations fro...

Relationship between Gender Roles and Sexual Assertiveness in Married Women.

PubMed

Azmoude, Elham; Firoozi, Mahbobe; Sadeghi Sahebzad, Elahe; Asgharipour, Neghar

2016-10-01

Evidence indicates that sexual assertiveness is one of the important factors affecting sexual satisfaction. According to some studies, traditional gender norms conflict with women's capability in expressing sexual desires. This study examined the relationship between gender roles and sexual assertiveness in married women in Mashhad, Iran. This cross-sectional study was conducted on 120 women who referred to Mashhad health centers through convenient sampling in 2014-15. Data were collected using Bem Sex Role Inventory (BSRI) and Hulbert index of sexual assertiveness. Data were analyzed using SPSS 16 by Pearson and Spearman's correlation tests and linear Regression Analysis. The mean scores of sexual assertiveness was 54.93±13.20. According to the findings, there was non-significant correlation between Femininity and masculinity score with sexual assertiveness (P=0.069 and P=0.080 respectively). Linear regression analysis indicated that among the predictor variables, only Sexual function satisfaction was identified as the sexual assertiveness summary predictor variables (P=0.001). Based on the results, sexual assertiveness in married women does not comply with gender role, but it is related to Sexual function satisfaction. So, counseling psychologists need to consider this variable when designing intervention programs for modifying sexual assertiveness and find other variables that affect sexual assertiveness.

Hourly predictive Levenberg-Marquardt ANN and multi linear regression models for predicting of dew point temperature

NASA Astrophysics Data System (ADS)

Zounemat-Kermani, Mohammad

2012-08-01

In this study, the ability of two models of multi linear regression (MLR) and Levenberg-Marquardt (LM) feed-forward neural network was examined to estimate the hourly dew point temperature. Dew point temperature is the temperature at which water vapor in the air condenses into liquid. This temperature can be useful in estimating meteorological variables such as fog, rain, snow, dew, and evapotranspiration and in investigating agronomical issues as stomatal closure in plants. The availability of hourly records of climatic data (air temperature, relative humidity and pressure) which could be used to predict dew point temperature initiated the practice of modeling. Additionally, the wind vector (wind speed magnitude and direction) and conceptual input of weather condition were employed as other input variables. The three quantitative standard statistical performance evaluation measures, i.e. the root mean squared error, mean absolute error, and absolute logarithmic Nash-Sutcliffe efficiency coefficient ( {| {{{Log}}({{NS}})} |} ) were employed to evaluate the performances of the developed models. The results showed that applying wind vector and weather condition as input vectors along with meteorological variables could slightly increase the ANN and MLR predictive accuracy. The results also revealed that LM-NN was superior to MLR model and the best performance was obtained by considering all potential input variables in terms of different evaluation criteria.

Relationship between Gender Roles and Sexual Assertiveness in Married Women

PubMed Central

Azmoude, Elham; Firoozi, Mahbobe; Sadeghi Sahebzad, Elahe; Asgharipour, Neghar

2016-01-01

ABSTRACT Background: Evidence indicates that sexual assertiveness is one of the important factors affecting sexual satisfaction. According to some studies, traditional gender norms conflict with women’s capability in expressing sexual desires. This study examined the relationship between gender roles and sexual assertiveness in married women in Mashhad, Iran. Methods: This cross-sectional study was conducted on 120 women who referred to Mashhad health centers through convenient sampling in 2014-15. Data were collected using Bem Sex Role Inventory (BSRI) and Hulbert index of sexual assertiveness. Data were analyzed using SPSS 16 by Pearson and Spearman’s correlation tests and linear Regression Analysis. Results: The mean scores of sexual assertiveness was 54.93±13.20. According to the findings, there was non-significant correlation between Femininity and masculinity score with sexual assertiveness (P=0.069 and P=0.080 respectively). Linear regression analysis indicated that among the predictor variables, only Sexual function satisfaction was identified as the sexual assertiveness summary predictor variables (P=0.001). Conclusion: Based on the results, sexual assertiveness in married women does not comply with gender role, but it is related to Sexual function satisfaction. So, counseling psychologists need to consider this variable when designing intervention programs for modifying sexual assertiveness and find other variables that affect sexual assertiveness. PMID:27713899

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

NASA Astrophysics Data System (ADS)

Soares dos Santos, T.; Mendes, D.; Rodrigues Torres, R.

2016-01-01

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

NASA Astrophysics Data System (ADS)

dos Santos, T. S.; Mendes, D.; Torres, R. R.

2015-08-01

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANN) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon, Northeastern Brazil and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model out- put and observed monthly precipitation. We used GCMs experiments for the 20th century (RCP Historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANN significantly outperforms the MLR downscaling of monthly precipitation variability.

Comparative analysis of used car price evaluation models

NASA Astrophysics Data System (ADS)

Chen, Chuancan; Hao, Lulu; Xu, Cong

2017-05-01

An accurate used car price evaluation is a catalyst for the healthy development of used car market. Data mining has been applied to predict used car price in several articles. However, little is studied on the comparison of using different algorithms in used car price estimation. This paper collects more than 100,000 used car dealing records throughout China to do empirical analysis on a thorough comparison of two algorithms: linear regression and random forest. These two algorithms are used to predict used car price in three different models: model for a certain car make, model for a certain car series and universal model. Results show that random forest has a stable but not ideal effect in price evaluation model for a certain car make, but it shows great advantage in the universal model compared with linear regression. This indicates that random forest is an optimal algorithm when handling complex models with a large number of variables and samples, yet it shows no obvious advantage when coping with simple models with less variables.

Statistically extracted fundamental watershed variables for estimating the loads of total nitrogen in small streams

USGS Publications Warehouse

Kronholm, Scott C.; Capel, Paul D.; Terziotti, Silvia

2016-01-01

Accurate estimation of total nitrogen loads is essential for evaluating conditions in the aquatic environment. Extrapolation of estimates beyond measured streams will greatly expand our understanding of total nitrogen loading to streams. Recursive partitioning and random forest regression were used to assess 85 geospatial, environmental, and watershed variables across 636 small (<585 km2) watersheds to determine which variables are fundamentally important to the estimation of annual loads of total nitrogen. Initial analysis led to the splitting of watersheds into three groups based on predominant land use (agricultural, developed, and undeveloped). Nitrogen application, agricultural and developed land area, and impervious or developed land in the 100-m stream buffer were commonly extracted variables by both recursive partitioning and random forest regression. A series of multiple linear regression equations utilizing the extracted variables were created and applied to the watersheds. As few as three variables explained as much as 76 % of the variability in total nitrogen loads for watersheds with predominantly agricultural land use. Catchment-scale national maps were generated to visualize the total nitrogen loads and yields across the USA. The estimates provided by these models can inform water managers and help identify areas where more in-depth monitoring may be beneficial.

Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions.

PubMed

Drouard, Vincent; Horaud, Radu; Deleforge, Antoine; Ba, Sileye; Evangelidis, Georgios

2017-03-01

Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging, because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We propose to use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena. We describe in detail the mapping method that combines the merits of unsupervised manifold learning techniques and of mixtures of regressions. We validate our method with three publicly available data sets and we thoroughly benchmark four variants of the proposed algorithm with several state-of-the-art head-pose estimation methods.

Variables associated with health-related quality of life in a Brazilian sample of patients from a tertiary outpatient clinic for depression and anxiety disorders.

PubMed

Schwab, Bianca; Daniel, Heloisa Silveira; Lutkemeyer, Carine; Neves, João Arthur Lange Lins; Zilli, Louise Nassif; Guarnieri, Ricardo; Diaz, Alexandre Paim; Michels, Ana Maria Maykot Prates

2015-01-01

Health-related quality of life (HRQOL) assessment tools have been broadly used in the medical context. These tools are used to measure the subjective impact of the disease on patients. The objective of this study was to evaluate the variables associated with HRQOL in a Brazilian sample of patients followed up in a tertiary outpatient clinic for depression and anxiety disorders. Cross-sectional study. Independent variables were those included in a sociodemographic questionnaire and the Hospital Anxiety and Depression Scale (HADS) scores. Dependent variables were those included in the short version of the World Health Organization Quality of Life (WHOQOL-BREF) and the scores for its subdomains (overall quality of life and general health, physical health, psychological health, social relationships, and environment). A multiple linear regression analysis was used to find the variables independently associated with each outcome. Seventy-five adult patients were evaluated. After multiple linear regression analysis, the HADS scores were associated with all outcomes, except social relationships (p = 0.08). Female gender was associated with poor total scores, as well as psychological health and environment. Unemployment was associated with poor physical health. Identifying the factors associated with HRQOL and recognizing that depression and anxiety are major factors are essential to improve the care of patients.

Modelling the breeding of Aedes Albopictus species in an urban area in Pulau Pinang using polynomial regression

NASA Astrophysics Data System (ADS)

Salleh, Nur Hanim Mohd; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Saad, Ahmad Ramli; Sulaiman, Husna Mahirah; Ahmad, Wan Muhamad Amir W.

2014-07-01

Polynomial regression is used to model a curvilinear relationship between a response variable and one or more predictor variables. It is a form of a least squares linear regression model that predicts a single response variable by decomposing the predictor variables into an nth order polynomial. In a curvilinear relationship, each curve has a number of extreme points equal to the highest order term in the polynomial. A quadratic model will have either a single maximum or minimum, whereas a cubic model has both a relative maximum and a minimum. This study used quadratic modeling techniques to analyze the effects of environmental factors: temperature, relative humidity, and rainfall distribution on the breeding of Aedes albopictus, a type of Aedes mosquito. Data were collected at an urban area in south-west Penang from September 2010 until January 2011. The results indicated that the breeding of Aedes albopictus in the urban area is influenced by all three environmental characteristics. The number of mosquito eggs is estimated to reach a maximum value at a medium temperature, a medium relative humidity and a high rainfall distribution.

Modeling and forecasting US presidential election using learning algorithms

NASA Astrophysics Data System (ADS)

Zolghadr, Mohammad; Niaki, Seyed Armin Akhavan; Niaki, S. T. A.

2017-09-01

The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six independent variables such as GDP, unemployment rate, the president's approval rate, and others are considered in a stepwise regression to identify significant variables. The president's approval rate is identified as the most significant variable, based on which eight other variables are identified and considered in the model development. Preprocessing methods are applied to prepare the data for the learning algorithms. The proposed procedure significantly increases the accuracy of the model by 50%. The learning algorithms (ANN and SVR) proved to be superior to linear regression based on each method's calculated performance measures. The SVR model is identified as the most accurate model among the other models as this model successfully predicted the outcome of the election in the last three elections (2004, 2008, and 2012). The proposed approach significantly increases the accuracy of the forecast.

Covariate Selection for Multilevel Models with Missing Data

PubMed Central

Marino, Miguel; Buxton, Orfeu M.; Li, Yi

2017-01-01

Missing covariate data hampers variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-selection methods which are problematic. Moreover, most variable selection methods are developed for independent linear regression models and do not accommodate multilevel mixed effects regression models with incomplete covariate data. We develop a novel methodology that is able to perform covariate selection across multiply-imputed data for multilevel random effects models when missing data is present. Specifically, we propose to stack the multiply-imputed data sets from a multiple imputation procedure and to apply a group variable selection procedure through group lasso regularization to assess the overall impact of each predictor on the outcome across the imputed data sets. Simulations confirm the advantageous performance of the proposed method compared with the competing methods. We applied the method to reanalyze the Healthy Directions-Small Business cancer prevention study, which evaluated a behavioral intervention program targeting multiple risk-related behaviors in a working-class, multi-ethnic population. PMID:28239457

Modeling workplace bullying using catastrophe theory.

PubMed

Escartin, J; Ceja, L; Navarro, J; Zapf, D

2013-10-01

Workplace bullying is defined as negative behaviors directed at organizational members or their work context that occur regularly and repeatedly over a period of time. Employees' perceptions of psychosocial safety climate, workplace bullying victimization, and workplace bullying perpetration were assessed within a sample of nearly 5,000 workers. Linear and nonlinear approaches were applied in order to model both continuous and sudden changes in workplace bullying. More specifically, the present study examines whether a nonlinear dynamical systems model (i.e., a cusp catastrophe model) is superior to the linear combination of variables for predicting the effect of psychosocial safety climate and workplace bullying victimization on workplace bullying perpetration. According to the AICc, and BIC indices, the linear regression model fits the data better than the cusp catastrophe model. The study concludes that some phenomena, especially unhealthy behaviors at work (like workplace bullying), may be better studied using linear approaches as opposed to nonlinear dynamical systems models. This can be explained through the healthy variability hypothesis, which argues that positive organizational behavior is likely to present nonlinear behavior, while a decrease in such variability may indicate the occurrence of negative behaviors at work.

Beyond logistic regression: structural equations modelling for binary variables and its application to investigating unobserved confounders.

PubMed

Kupek, Emil

2006-03-15

Structural equation modelling (SEM) has been increasingly used in medical statistics for solving a system of related regression equations. However, a great obstacle for its wider use has been its difficulty in handling categorical variables within the framework of generalised linear models. A large data set with a known structure among two related outcomes and three independent variables was generated to investigate the use of Yule's transformation of odds ratio (OR) into Q-metric by (OR-1)/(OR+1) to approximate Pearson's correlation coefficients between binary variables whose covariance structure can be further analysed by SEM. Percent of correctly classified events and non-events was compared with the classification obtained by logistic regression. The performance of SEM based on Q-metric was also checked on a small (N = 100) random sample of the data generated and on a real data set. SEM successfully recovered the generated model structure. SEM of real data suggested a significant influence of a latent confounding variable which would have not been detectable by standard logistic regression. SEM classification performance was broadly similar to that of the logistic regression. The analysis of binary data can be greatly enhanced by Yule's transformation of odds ratios into estimated correlation matrix that can be further analysed by SEM. The interpretation of results is aided by expressing them as odds ratios which are the most frequently used measure of effect in medical statistics.

Multi-linear regression of sea level in the south west Pacific as a first step towards local sea level projections

NASA Astrophysics Data System (ADS)

Kumar, Vandhna; Meyssignac, Benoit; Melet, Angélique; Ganachaud, Alexandre

2017-04-01

Rising sea levels are a critical concern in small island nations. The problem is especially serious in the western south Pacific, where the total sea level rise over the last 60 years is up to 3 times the global average. In this study, we attempt to reconstruct sea levels at selected sites in the region (Suva, Lautoka, Noumea - Fiji and New Caledonia) as a mutiple-linear regression of atmospheric and oceanic variables. We focus on interannual-to-decadal scale variability, and lower (including the global mean sea level rise) over the 1979-2014 period. Sea levels are taken from tide gauge records and the ORAS4 reanalysis dataset, and are expressed as a sum of steric and mass changes as a preliminary step. The key development in our methodology is using leading wind stress curl as a proxy for the thermosteric component. This is based on the knowledge that wind stress curl anomalies can modulate the thermocline depth and resultant sea levels via Rossby wave propagation. The analysis is primarily based on correlation between local sea level and selected predictors, the dominant one being wind stress curl. In the first step, proxy boxes for wind stress curl are determined via regions of highest correlation. The proportion of sea level explained via linear regression is then removed, leaving a residual. This residual is then correlated with other locally acting potential predictors: halosteric sea level, the zonal and meridional wind stress components, and sea surface temperature. The statistically significant predictors are used in a multi-linear regression function to simulate the observed sea level. The method is able to reproduce between 40 to 80% of the variance in observed sea level. Based on the skill of the model, it has high potential in sea level projection and downscaling studies.

A FORTRAN program for multivariate survival analysis on the personal computer.

PubMed

Mulder, P G

1988-01-01

In this paper a FORTRAN program is presented for multivariate survival or life table regression analysis in a competing risks' situation. The relevant failure rate (for example, a particular disease or mortality rate) is modelled as a log-linear function of a vector of (possibly time-dependent) explanatory variables. The explanatory variables may also include the variable time itself, which is useful for parameterizing piecewise exponential time-to-failure distributions in a Gompertz-like or Weibull-like way as a more efficient alternative to Cox's proportional hazards model. Maximum likelihood estimates of the coefficients of the log-linear relationship are obtained from the iterative Newton-Raphson method. The program runs on a personal computer under DOS; running time is quite acceptable, even for large samples.

Understanding Child Stunting in India: A Comprehensive Analysis of Socio-Economic, Nutritional and Environmental Determinants Using Additive Quantile Regression

PubMed Central

Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A.

2013-01-01

Background Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. Objective We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Design Using cross-sectional data for children aged 0–24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. Results At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Conclusions Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role. PMID:24223839

Understanding child stunting in India: a comprehensive analysis of socio-economic, nutritional and environmental determinants using additive quantile regression.

PubMed

Fenske, Nora; Burns, Jacob; Hothorn, Torsten; Rehfuess, Eva A

2013-01-01

Most attempts to address undernutrition, responsible for one third of global child deaths, have fallen behind expectations. This suggests that the assumptions underlying current modelling and intervention practices should be revisited. We undertook a comprehensive analysis of the determinants of child stunting in India, and explored whether the established focus on linear effects of single risks is appropriate. Using cross-sectional data for children aged 0-24 months from the Indian National Family Health Survey for 2005/2006, we populated an evidence-based diagram of immediate, intermediate and underlying determinants of stunting. We modelled linear, non-linear, spatial and age-varying effects of these determinants using additive quantile regression for four quantiles of the Z-score of standardized height-for-age and logistic regression for stunting and severe stunting. At least one variable within each of eleven groups of determinants was significantly associated with height-for-age in the 35% Z-score quantile regression. The non-modifiable risk factors child age and sex, and the protective factors household wealth, maternal education and BMI showed the largest effects. Being a twin or multiple birth was associated with dramatically decreased height-for-age. Maternal age, maternal BMI, birth order and number of antenatal visits influenced child stunting in non-linear ways. Findings across the four quantile and two logistic regression models were largely comparable. Our analysis confirms the multifactorial nature of child stunting. It emphasizes the need to pursue a systems-based approach and to consider non-linear effects, and suggests that differential effects across the height-for-age distribution do not play a major role.

An hourly PM10 diagnosis model for the Bilbao metropolitan area using a linear regression methodology.

PubMed

González-Aparicio, I; Hidalgo, J; Baklanov, A; Padró, A; Santa-Coloma, O

2013-07-01

There is extensive evidence of the negative impacts on health linked to the rise of the regional background of particulate matter (PM) 10 levels. These levels are often increased over urban areas becoming one of the main air pollution concerns. This is the case on the Bilbao metropolitan area, Spain. This study describes a data-driven model to diagnose PM10 levels in Bilbao at hourly intervals. The model is built with a training period of 7-year historical data covering different urban environments (inland, city centre and coastal sites). The explanatory variables are quantitative-log [NO2], temperature, short-wave incoming radiation, wind speed and direction, specific humidity, hour and vehicle intensity-and qualitative-working days/weekends, season (winter/summer), the hour (from 00 to 23 UTC) and precipitation/no precipitation. Three different linear regression models are compared: simple linear regression; linear regression with interaction terms (INT); and linear regression with interaction terms following the Sawa's Bayesian Information Criteria (INT-BIC). Each type of model is calculated selecting two different periods: the training (it consists of 6 years) and the testing dataset (it consists of 1 year). The results of each type of model show that the INT-BIC-based model (R(2) = 0.42) is the best. Results were R of 0.65, 0.63 and 0.60 for the city centre, inland and coastal sites, respectively, a level of confidence similar to the state-of-the art methodology. The related error calculated for longer time intervals (monthly or seasonal means) diminished significantly (R of 0.75-0.80 for monthly means and R of 0.80 to 0.98 at seasonally means) with respect to shorter periods.

Statistical and Biophysical Models for Predicting Total and Outdoor Water Use in Los Angeles

NASA Astrophysics Data System (ADS)

Mini, C.; Hogue, T. S.; Pincetl, S.

2012-04-01

Modeling water demand is a complex exercise in the choice of the functional form, techniques and variables to integrate in the model. The goal of the current research is to identify the determinants that control total and outdoor residential water use in semi-arid cities and to utilize that information in the development of statistical and biophysical models that can forecast spatial and temporal urban water use. The City of Los Angeles is unique in its highly diverse socio-demographic, economic and cultural characteristics across neighborhoods, which introduces significant challenges in modeling water use. Increasing climate variability also contributes to uncertainties in water use predictions in urban areas. Monthly individual water use records were acquired from the Los Angeles Department of Water and Power (LADWP) for the 2000 to 2010 period. Study predictors of residential water use include socio-demographic, economic, climate and landscaping variables at the zip code level collected from US Census database. Climate variables are estimated from ground-based observations and calculated at the centroid of each zip code by inverse-distance weighting method. Remotely-sensed products of vegetation biomass and landscape land cover are also utilized. Two linear regression models were developed based on the panel data and variables described: a pooled-OLS regression model and a linear mixed effects model. Both models show income per capita and the percentage of landscape areas in each zip code as being statistically significant predictors. The pooled-OLS model tends to over-estimate higher water use zip codes and both models provide similar RMSE values.Outdoor water use was estimated at the census tract level as the residual between total water use and indoor use. This residual is being compared with the output from a biophysical model including tree and grass cover areas, climate variables and estimates of evapotranspiration at very high spatial resolution. A genetic algorithm based model (Shuffled Complex Evolution-UA; SCE-UA) is also being developed to provide estimates of the predictions and parameters uncertainties and to compare against the linear regression models. Ultimately, models will be selected to undertake predictions for a range of climate change and landscape scenarios. Finally, project results will contribute to a better understanding of water demand to help predict future water use and implement targeted landscaping conservation programs to maintain sustainable water needs for a growing population under uncertain climate variability.

[Multivariate Adaptive Regression Splines (MARS), an alternative for the analysis of time series].

PubMed

Vanegas, Jairo; Vásquez, Fabián

Multivariate Adaptive Regression Splines (MARS) is a non-parametric modelling method that extends the linear model, incorporating nonlinearities and interactions between variables. It is a flexible tool that automates the construction of predictive models: selecting relevant variables, transforming the predictor variables, processing missing values and preventing overshooting using a self-test. It is also able to predict, taking into account structural factors that might influence the outcome variable, thereby generating hypothetical models. The end result could identify relevant cut-off points in data series. It is rarely used in health, so it is proposed as a tool for the evaluation of relevant public health indicators. For demonstrative purposes, data series regarding the mortality of children under 5 years of age in Costa Rica were used, comprising the period 1978-2008. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

PubMed

Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

2016-01-01

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation. Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models.

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data.

PubMed

Holsclaw, Tracy; Hallgren, Kevin A; Steyvers, Mark; Smyth, Padhraic; Atkins, David C

2015-12-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials. (c) 2016 APA, all rights reserved).

Measurement error and outcome distributions: Methodological issues in regression analyses of behavioral coding data

PubMed Central

Holsclaw, Tracy; Hallgren, Kevin A.; Steyvers, Mark; Smyth, Padhraic; Atkins, David C.

2015-01-01

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non-normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased type-I and type-II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally-technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in supplementary materials. PMID:26098126

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

NASA Astrophysics Data System (ADS)

Verrelst, Jochem; Malenovský, Zbyněk; Van der Tol, Christiaan; Camps-Valls, Gustau; Gastellu-Etchegorry, Jean-Philippe; Lewis, Philip; North, Peter; Moreno, Jose

2018-06-01

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given.

Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression

PubMed Central

Shimizu, Yu; Yoshimoto, Junichiro; Takamura, Masahiro; Okada, Go; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS) regression to resting-state functional magnetic resonance imaging (rs-fMRI) data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression. Subsequent classification using predicted clinical scores distinguished depressed patients from healthy controls with 80% accuracy. Moreover, loading vectors for latent variables enabled us to identify brain regions relevant to depression, including the default mode network, the right superior frontal gyrus, and the superior motor area. PMID:28700672

Social support for women of reproductive age and its predictors: a population-based study

PubMed Central

2012-01-01

Background Social support is an exchange of resources between at least two individuals perceived by the provider or recipient to be intended to promote the health of the recipient. Social support is a major determinant of health. The objective of this study was to determine the perceived social support and its associated sociodemographic factors among women of reproductive age. Methods This was a population-based cross-sectional study with multistage random cluster sampling of 1359 women of reproductive age. Data were collected using questionnaires on sociodemographic factors and perceived social support (PRQ85-Part 2). The relationship between the dependent variable (perceived social support) and the independent variables (sociodemographic characteristics) was analyzed using the multivariable linear regression model. Results The mean score of social support was 134.3 ± 17.9. Women scored highest in the “worth” dimension and lowest in the “social integration” dimension. Multivariable linear regression analysis indicated that the variables of education, spouse’s occupation, Sufficiency of income for expenses and primary support source were significantly related to the perceived social support. Conclusion Sociodemographic factors affect social support and could be considered in planning interventions to improve social support for Iranian women. PMID:22988834

Modeling of bromate formation by ozonation of surface waters in drinking water treatment.

PubMed

Legube, Bernard; Parinet, Bernard; Gelinet, Karine; Berne, Florence; Croue, Jean-Philippe

2004-04-01

The main objective of this paper is to try to develop statistically and chemically rational models for bromate formation by ozonation of clarified surface waters. The results presented here show that bromate formation by ozonation of natural waters in drinking water treatment is directly proportional to the "Ct" value ("Ctau" in this study). Moreover, this proportionality strongly depends on many parameters: increasing of pH, temperature and bromide level leading to an increase of bromate formation; ammonia and dissolved organic carbon concentrations causing a reverse effect. Taking into account limitation of theoretical modeling, we proposed to predict bromate formation by stochastic simulations (multi-linear regression and artificial neural networks methods) from 40 experiments (BrO(3)(-) vs. "Ctau") carried out with three sand filtered waters sampled on three different waterworks. With seven selected variables we used a simple architecture of neural networks, optimized by "neural connection" of SPSS Inc./Recognition Inc. The bromate modeling by artificial neural networks gives better result than multi-linear regression. The artificial neural networks model allowed us classifying variables by decreasing order of influence (for the studied cases in our variables scale): "Ctau", [N-NH(4)(+)], [Br(-)], pH, temperature, DOC, alkalinity.

Forecasting on the total volumes of Malaysia's imports and exports by multiple linear regression

NASA Astrophysics Data System (ADS)

Beh, W. L.; Yong, M. K. Au

2017-04-01

This study is to give an insight on the doubt of the important of macroeconomic variables that affecting the total volumes of Malaysia's imports and exports by using multiple linear regression (MLR) analysis. The time frame for this study will be determined by using quarterly data of the total volumes of Malaysia's imports and exports covering the period between 2000-2015. The macroeconomic variables will be limited to eleven variables which are the exchange rate of US Dollar with Malaysia Ringgit (USD-MYR), exchange rate of China Yuan with Malaysia Ringgit (RMB-MYR), exchange rate of European Euro with Malaysia Ringgit (EUR-MYR), exchange rate of Singapore Dollar with Malaysia Ringgit (SGD-MYR), crude oil prices, gold prices, producer price index (PPI), interest rate, consumer price index (CPI), industrial production index (IPI) and gross domestic product (GDP). This study has applied the Johansen Co-integration test to investigate the relationship among the total volumes to Malaysia's imports and exports. The result shows that crude oil prices, RMB-MYR, EUR-MYR and IPI play important roles in the total volumes of Malaysia's imports. Meanwhile crude oil price, USD-MYR and GDP play important roles in the total volumes of Malaysia's exports.

pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms.

PubMed

Molnos, Sophie; Baumbach, Clemens; Wahl, Simone; Müller-Nurasyid, Martina; Strauch, Konstantin; Wang-Sattler, Rui; Waldenberger, Melanie; Meitinger, Thomas; Adamski, Jerzy; Kastenmüller, Gabi; Suhre, Karsten; Peters, Annette; Grallert, Harald; Theis, Fabian J; Gieger, Christian

2017-09-29

Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different "omics" layers. Existing tools only consider single-nucleotide polymorphism (SNP)-SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables. We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Comparisons based on simulated data showed that pulver is much faster than the existing tools. This is achieved by using the correlation coefficient to test the null-hypothesis, which avoids the costly computation of inversions. Additional tricks are a rearrangement of the order, when iterating through the different "omics" layers, and implementing this algorithm in the fast programming language C++. Furthermore, we applied our algorithm to data from the German KORA study to investigate a real-world problem involving the interplay among DNA methylation, genetic variants, and metabolite levels. The pulver package is a convenient and rapid tool for screening huge numbers of linear regression models for significant interaction terms in arbitrary pairs of quantitative variables. pulver is written in R and C++, and can be downloaded freely from CRAN at https://cran.r-project.org/web/packages/pulver/ .

Machine learning approaches to the social determinants of health in the health and retirement study.

PubMed

Seligman, Benjamin; Tuljapurkar, Shripad; Rehkopf, David

2018-04-01

Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data ( R 2 between 0.4-0.6, versus <0.3 for all others). Across machine learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender. Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.

Predicting daily use of urban forest recreation sites

Treesearch

John F. Dwyer

1988-01-01

A multiple linear regression model explains 90% of the variance in daily use of an urban recreation site. Explanatory variables include season, day of the week, and weather. The results offer guides for recreation site planning and management as well as suggestions for improving the model.

Comparative analysis on the probability of being a good payer

NASA Astrophysics Data System (ADS)

Mihova, V.; Pavlov, V.

2017-10-01

Credit risk assessment is crucial for the bank industry. The current practice uses various approaches for the calculation of credit risk. The core of these approaches is the use of multiple regression models, applied in order to assess the risk associated with the approval of people applying for certain products (loans, credit cards, etc.). Based on data from the past, these models try to predict what will happen in the future. Different data requires different type of models. This work studies the causal link between the conduct of an applicant upon payment of the loan and the data that he completed at the time of application. A database of 100 borrowers from a commercial bank is used for the purposes of the study. The available data includes information from the time of application and credit history while paying off the loan. Customers are divided into two groups, based on the credit history: Good and Bad payers. Linear and logistic regression are applied in parallel to the data in order to estimate the probability of being good for new borrowers. A variable, which contains value of 1 for Good borrowers and value of 0 for Bad candidates, is modeled as a dependent variable. To decide which of the variables listed in the database should be used in the modelling process (as independent variables), a correlation analysis is made. Due to the results of it, several combinations of independent variables are tested as initial models - both with linear and logistic regression. The best linear and logistic models are obtained after initial transformation of the data and following a set of standard and robust statistical criteria. A comparative analysis between the two final models is made and scorecards are obtained from both models to assess new customers at the time of application. A cut-off level of points, bellow which to reject the applications and above it - to accept them, has been suggested for both the models, applying the strategy to keep the same Accept Rate as in the current data.

Soil Cd, Cr, Cu, Ni, Pb and Zn sorption and retention models using SVM: Variable selection and competitive model.

PubMed

González Costa, J J; Reigosa, M J; Matías, J M; Covelo, E F

2017-09-01

The aim of this study was to model the sorption and retention of Cd, Cu, Ni, Pb and Zn in soils. To that extent, the sorption and retention of these metals were studied and the soil characterization was performed separately. Multiple stepwise regression was used to produce multivariate models with linear techniques and with support vector machines, all of which included 15 explanatory variables characterizing soils. When the R-squared values are represented, two different groups are noticed. Cr, Cu and Pb sorption and retention show a higher R-squared; the most explanatory variables being humified organic matter, Al oxides and, in some cases, cation-exchange capacity (CEC). The other group of metals (Cd, Ni and Zn) shows a lower R-squared, and clays are the most explanatory variables, including a percentage of vermiculite and slime. In some cases, quartz, plagioclase or hematite percentages also show some explanatory capacity. Support Vector Machine (SVM) regression shows that the different models are not as regular as in multiple regression in terms of number of variables, the regression for nickel adsorption being the one with the highest number of variables in its optimal model. On the other hand, there are cases where the most explanatory variables are the same for two metals, as it happens with Cd and Cr adsorption. A similar adsorption mechanism is thus postulated. These patterns of the introduction of variables in the model allow us to create explainability sequences. Those which are the most similar to the selectivity sequences obtained by Covelo (2005) are Mn oxides in multiple regression and change capacity in SVM. Among all the variables, the only one that is explanatory for all the metals after applying the maximum parsimony principle is the percentage of sand in the retention process. In the competitive model arising from the aforementioned sequences, the most intense competitiveness for the adsorption and retention of different metals appears between Cr and Cd, Cu and Zn in multiple regression; and between Cr and Cd in SVM regression. Copyright © 2017 Elsevier B.V. All rights reserved.

Approximate Probabilistic Methods for Survivability/Vulnerability Analysis of Strategic Structures.

DTIC Science & Technology

1978-07-15

weapon yield, in kilotons; K = energy coupling factor; C = coefficient determined from linear regression; a, b = exponents determined from linear...hn(l + .582 00 = 0.54 In the case of the applied pressure, according to Perret and Bass (1975), the variabilities in the exponents a and b of Eq. 32...ATTN: WESSF, L. Ingram ATTN: ATC-T ATTN: Library ATTN: F. Brown BMD Systems Command ATTN: J. Strange Deoartment of the Army ATTN: BMDSC-H, N. Hurst

Partial Least Squares Regression Models for the Analysis of Kinase Signaling.

PubMed

Bourgeois, Danielle L; Kreeger, Pamela K

2017-01-01

Partial least squares regression (PLSR) is a data-driven modeling approach that can be used to analyze multivariate relationships between kinase networks and cellular decisions or patient outcomes. In PLSR, a linear model relating an X matrix of dependent variables and a Y matrix of independent variables is generated by extracting the factors with the strongest covariation. While the identified relationship is correlative, PLSR models can be used to generate quantitative predictions for new conditions or perturbations to the network, allowing for mechanisms to be identified. This chapter will provide a brief explanation of PLSR and provide an instructive example to demonstrate the use of PLSR to analyze kinase signaling.

Bivariate least squares linear regression: Towards a unified analytic formalism. I. Functional models

NASA Astrophysics Data System (ADS)

Caimmi, R.

2011-08-01

Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts ( York, 1966, 1969) is reviewed using a new formalism in terms of deviation (matrix) traces which, for unweighted data, reduce to usual quantities leaving aside an unessential (but dimensional) multiplicative factor. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. The classes of linear models considered are regression lines in the general case of correlated errors in X and in Y for weighted data, and in the opposite limiting situations of (i) uncorrelated errors in X and in Y, and (ii) completely correlated errors in X and in Y. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases, namely: (Y) errors in X negligible (ideally null) with respect to errors in Y; (X) errors in Y negligible (ideally null) with respect to errors in X; (O) genuine orthogonal regression; (R) reduced major-axis regression. In the limit of unweighted data, the results determined for functional models are compared with their counterparts related to extreme structural models i.e. the instrumental scatter is negligible (ideally null) with respect to the intrinsic scatter ( Isobe et al., 1990; Feigelson and Babu, 1992). While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with the exception of Y models. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors (∓ σ) for both heteroscedastic and homoscedastic data. Conversely, samples related to different methods produce discrepant results, due to the presence of (still undetected) systematic errors, which implies no definitive statement can be made at present. A comparison is also made between different expressions of regression line slope and intercept variance estimators, where fractional discrepancies are found to be not exceeding a few percent, which grows up to about 20% in the presence of large dispersion data. An extension of the formalism to structural models is left to a forthcoming paper.

Flood quantile estimation at ungauged sites by Bayesian networks

NASA Astrophysics Data System (ADS)

Mediero, L.; Santillán, D.; Garrote, L.

2012-04-01

Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a stochastic generator of synthetic data was developed. Synthetic basin characteristics were randomised, keeping the statistical properties of observed physical and climatic variables in the homogeneous region. The synthetic flood quantiles were stochastically generated taking the regression equation as basis. The learnt Bayesian network was validated by the reliability diagram, the Brier Score and the ROC diagram, which are common measures used in the validation of probabilistic forecasts. Summarising, the flood quantile estimations through Bayesian networks supply information about the prediction uncertainty as a probability distribution function of discharges is given as result. Therefore, the Bayesian network model has application as a decision support for water resources and planning management.

Cooperation without culture? The null effect of generalized trust on intentional homicide: a cross-national panel analysis, 1995-2009.

PubMed

Robbins, Blaine

2013-01-01

Sociologists, political scientists, and economists all suggest that culture plays a pivotal role in the development of large-scale cooperation. In this study, I used generalized trust as a measure of culture to explore if and how culture impacts intentional homicide, my operationalization of cooperation. I compiled multiple cross-national data sets and used pooled time-series linear regression, single-equation instrumental-variables linear regression, and fixed- and random-effects estimation techniques on an unbalanced panel of 118 countries and 232 observations spread over a 15-year time period. Results suggest that culture and large-scale cooperation form a tenuous relationship, while economic factors such as development, inequality, and geopolitics appear to drive large-scale cooperation.

A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection

PubMed Central

Sabourin, Jeremy A; Valdar, William; Nobel, Andrew B

2015-01-01

Summary We describe a simple, computationally effcient, permutation-based procedure for selecting the penalty parameter in LASSO penalized regression. The procedure, permutation selection, is intended for applications where variable selection is the primary focus, and can be applied in a variety of structural settings, including that of generalized linear models. We briefly discuss connections between permutation selection and existing theory for the LASSO. In addition, we present a simulation study and an analysis of real biomedical data sets in which permutation selection is compared with selection based on the following: cross-validation (CV), the Bayesian information criterion (BIC), Scaled Sparse Linear Regression, and a selection method based on recently developed testing procedures for the LASSO. PMID:26243050

Key Results of Interaction Models with Centering

ERIC Educational Resources Information Center

Afshartous, David; Preston, Richard A.

2011-01-01

We consider the effect on estimation of simultaneous variable centering and interaction effects in linear regression. We technically define, review, and amplify many of the statistical issues for interaction models with centering in order to create a useful and compact reference for teachers, students, and applied researchers. In addition, we…

Comparative Research of Navy Voluntary Education at Operational Commands

DTIC Science & Technology

2017-03-01

return on investment, ROI, logistic regression, multivariate analysis, descriptive statistics, Markov, time-series, linear programming 15. NUMBER...21  B.  DESCRIPTIVE STATISTICS TABLES ...............................................25  C.  PRIVACY CONSIDERATIONS...THIS PAGE INTENTIONALLY LEFT BLANK xi LIST OF TABLES Table 1.  Variables and Descriptions . Adapted from NETC (2016). .......................21

Social Inequality and Labor Force Participation.

ERIC Educational Resources Information Center

King, Jonathan

The labor force participation rates of whites, blacks, and Spanish-Americans, grouped by sex, are explained in a linear regression model fitted with 1970 U. S. Census data on Standard Metropolitan Statistical Area (SMSA). The explanatory variables are: average age, average years of education, vocational training rate, disabled rate, unemployment…

Human and bovine viruses and bacteria at three great lakes beaches: Environmental variable associations and health risk

USDA-ARS?s Scientific Manuscript database

Waterborne pathogens were detected in 96% of samples collected at three Lake Michigan beaches during the summer of 2010. Linear regression models were developed to explore environmental factors that may be influential for pathogen prevalence. Simulation of pathogen concentration using these models, ...

USING HYDROGRAPHIC DATA AND THE EPA VIRTUAL BEACH MODEL TO TEST PREDICTIONS OF BEACH BACTERIA CONCENTRATIONS

EPA Science Inventory

A modeling study of 2006 Huntington Beach (Lake Erie) beach bacteria concentrations indicates multi-variable linear regression (MLR) can effectively estimate bacteria concentrations compared to the persistence model. Our use of the Virtual Beach (VB) model affirms that fact. VB i...

New Zealand Management Students' Perceptions of Communication Technologies in Correspondence Education.

ERIC Educational Resources Information Center

Ostman, Ronald E.; Wagner, Graham A.

1987-01-01

Describes a survey of 724 management students in New Zealand's Technical Correspondence Institute which was conducted to determine whether the introduction of educational technologies could decrease the dropout rate. The multiple linear regression model that was used to analyze the questionnaire responses is presented, and predictor variables are…

Predictors of physical performance and functional ability in people 50+ with and without fibromyalgia.

PubMed

Jones, C Jessie; Rutledge, Dana N; Aquino, Jordan

2010-07-01

The purposes of this study were to determine whether people with and without fibromyalgia (FM) age 50 yr and above showed differences in physical performance and perceived functional ability and to determine whether age, gender, depression, and physical activity level altered the impact of FM status on these factors. Dependent variables included perceived function and 6 performance measures (multidimensional balance, aerobic endurance, overall functional mobility, lower body strength, and gait velocity-normal or fast). Independent (predictor) variables were FM status, age, gender, depression, and physical activity level. Results indicated significant differences between adults with and without FM on all physical-performance measures and perceived function. Linear-regression models showed that the contribution of significant predictors was in expected directions. All regression models were significant, accounting for 16-65% of variance in the dependent variables.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method.

PubMed

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2015-11-18

Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method

PubMed Central

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2016-01-01

Introduction: Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. Methods: This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. Results: From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). Conclusion: This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available. PMID:26925889

Exact and Approximate Statistical Inference for Nonlinear Regression and the Estimating Equation Approach.

PubMed

Demidenko, Eugene

2017-09-01

The exact density distribution of the nonlinear least squares estimator in the one-parameter regression model is derived in closed form and expressed through the cumulative distribution function of the standard normal variable. Several proposals to generalize this result are discussed. The exact density is extended to the estimating equation (EE) approach and the nonlinear regression with an arbitrary number of linear parameters and one intrinsically nonlinear parameter. For a very special nonlinear regression model, the derived density coincides with the distribution of the ratio of two normally distributed random variables previously obtained by Fieller (1932), unlike other approximations previously suggested by other authors. Approximations to the density of the EE estimators are discussed in the multivariate case. Numerical complications associated with the nonlinear least squares are illustrated, such as nonexistence and/or multiple solutions, as major factors contributing to poor density approximation. The nonlinear Markov-Gauss theorem is formulated based on the near exact EE density approximation.

REGRES: A FORTRAN-77 program to calculate nonparametric and ``structural'' parametric solutions to bivariate regression equations

NASA Astrophysics Data System (ADS)

Rock, N. M. S.; Duffy, T. R.

REGRES allows a range of regression equations to be calculated for paired sets of data values in which both variables are subject to error (i.e. neither is the "independent" variable). Nonparametric regressions, based on medians of all possible pairwise slopes and intercepts, are treated in detail. Estimated slopes and intercepts are output, along with confidence limits, Spearman and Kendall rank correlation coefficients. Outliers can be rejected with user-determined stringency. Parametric regressions can be calculated for any value of λ (the ratio of the variances of the random errors for y and x)—including: (1) major axis ( λ = 1); (2) reduced major axis ( λ = variance of y/variance of x); (3) Y on Xλ = infinity; or (4) X on Y ( λ = 0) solutions. Pearson linear correlation coefficients also are output. REGRES provides an alternative to conventional isochron assessment techniques where bivariate normal errors cannot be assumed, or weighting methods are inappropriate.

Parameterizing sorption isotherms using a hybrid global-local fitting procedure.

PubMed

Matott, L Shawn; Singh, Anshuman; Rabideau, Alan J

2017-05-01

Predictive modeling of the transport and remediation of groundwater contaminants requires an accurate description of the sorption process, which is usually provided by fitting an isotherm model to site-specific laboratory data. Commonly used calibration procedures, listed in order of increasing sophistication, include: trial-and-error, linearization, non-linear regression, global search, and hybrid global-local search. Given the considerable variability in fitting procedures applied in published isotherm studies, we investigated the importance of algorithm selection through a series of numerical experiments involving 13 previously published sorption datasets. These datasets, considered representative of state-of-the-art for isotherm experiments, had been previously analyzed using trial-and-error, linearization, or non-linear regression methods. The isotherm expressions were re-fit using a 3-stage hybrid global-local search procedure (i.e. global search using particle swarm optimization followed by Powell's derivative free local search method and Gauss-Marquardt-Levenberg non-linear regression). The re-fitted expressions were then compared to previously published fits in terms of the optimized weighted sum of squared residuals (WSSR) fitness function, the final estimated parameters, and the influence on contaminant transport predictions - where easily computed concentration-dependent contaminant retardation factors served as a surrogate measure of likely transport behavior. Results suggest that many of the previously published calibrated isotherm parameter sets were local minima. In some cases, the updated hybrid global-local search yielded order-of-magnitude reductions in the fitness function. In particular, of the candidate isotherms, the Polanyi-type models were most likely to benefit from the use of the hybrid fitting procedure. In some cases, improvements in fitness function were associated with slight (<10%) changes in parameter values, but in other cases significant (>50%) changes in parameter values were noted. Despite these differences, the influence of isotherm misspecification on contaminant transport predictions was quite variable and difficult to predict from inspection of the isotherms. Copyright © 2017 Elsevier B.V. All rights reserved.

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

Representational change and strategy use in children's number line estimation during the first years of primary school.

PubMed

White, Sonia L J; Szűcs, Dénes

2012-01-04

The objective of this study was to scrutinize number line estimation behaviors displayed by children in mathematics classrooms during the first three years of schooling. We extend existing research by not only mapping potential logarithmic-linear shifts but also provide a new perspective by studying in detail the estimation strategies of individual target digits within a number range familiar to children. Typically developing children (n = 67) from Years 1-3 completed a number-to-position numerical estimation task (0-20 number line). Estimation behaviors were first analyzed via logarithmic and linear regression modeling. Subsequently, using an analysis of variance we compared the estimation accuracy of each digit, thus identifying target digits that were estimated with the assistance of arithmetic strategy. Our results further confirm a developmental logarithmic-linear shift when utilizing regression modeling; however, uniquely we have identified that children employ variable strategies when completing numerical estimation, with levels of strategy advancing with development. In terms of the existing cognitive research, this strategy factor highlights the limitations of any regression modeling approach, or alternatively, it could underpin the developmental time course of the logarithmic-linear shift. Future studies need to systematically investigate this relationship and also consider the implications for educational practice.

Representational change and strategy use in children's number line estimation during the first years of primary school

PubMed Central

2012-01-01

Background The objective of this study was to scrutinize number line estimation behaviors displayed by children in mathematics classrooms during the first three years of schooling. We extend existing research by not only mapping potential logarithmic-linear shifts but also provide a new perspective by studying in detail the estimation strategies of individual target digits within a number range familiar to children. Methods Typically developing children (n = 67) from Years 1-3 completed a number-to-position numerical estimation task (0-20 number line). Estimation behaviors were first analyzed via logarithmic and linear regression modeling. Subsequently, using an analysis of variance we compared the estimation accuracy of each digit, thus identifying target digits that were estimated with the assistance of arithmetic strategy. Results Our results further confirm a developmental logarithmic-linear shift when utilizing regression modeling; however, uniquely we have identified that children employ variable strategies when completing numerical estimation, with levels of strategy advancing with development. Conclusion In terms of the existing cognitive research, this strategy factor highlights the limitations of any regression modeling approach, or alternatively, it could underpin the developmental time course of the logarithmic-linear shift. Future studies need to systematically investigate this relationship and also consider the implications for educational practice. PMID:22217191

An investigation to improve the Menhaden fishery prediction and detection model through the application of ERTS-A data

NASA Technical Reports Server (NTRS)

Maughan, P. M. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Linear regression of secchi disc visibility against number of sets yielded significant results in a number of instances. The variability seen in the slope of the regression lines is due to the nonuniformity of sample size. The longer the period sampled, the larger the total number of attempts. Further, there is no reason to expect either the influence of transparency or of other variables to remain constant throughout the season. However, the fact that the data for the entire season, variable as it is, was significant at the 5% level, suggests its potential utility for predictive modeling. Thus, this regression equation will be considered representative and will be utilized for the first numerical model. Secchi disc visibility was also regressed against number of sets for the three day period September 27-September 29, 1972 to determine if surface truth data supported the intense relationship between ERTS-1 identified turbidity and fishing effort previously discussed. A very negative correlation was found. These relationship lend additional credence to the hypothesis that ERTS imagery, when utilized as a source of visibility (turbidity) data, may be useful as a predictive tool.

Shrinkage Estimation of Varying Covariate Effects Based On Quantile Regression

PubMed Central

Peng, Limin; Xu, Jinfeng; Kutner, Nancy

2013-01-01

Varying covariate effects often manifest meaningful heterogeneity in covariate-response associations. In this paper, we adopt a quantile regression model that assumes linearity at a continuous range of quantile levels as a tool to explore such data dynamics. The consideration of potential non-constancy of covariate effects necessitates a new perspective for variable selection, which, under the assumed quantile regression model, is to retain variables that have effects on all quantiles of interest as well as those that influence only part of quantiles considered. Current work on l1-penalized quantile regression either does not concern varying covariate effects or may not produce consistent variable selection in the presence of covariates with partial effects, a practical scenario of interest. In this work, we propose a shrinkage approach by adopting a novel uniform adaptive LASSO penalty. The new approach enjoys easy implementation without requiring smoothing. Moreover, it can consistently identify the true model (uniformly across quantiles) and achieve the oracle estimation efficiency. We further extend the proposed shrinkage method to the case where responses are subject to random right censoring. Numerical studies confirm the theoretical results and support the utility of our proposals. PMID:25332515

[Optimal extraction of effective constituents from Aralia elata by central composite design and response surface methodology].

PubMed

Lv, Shao-Wa; Liu, Dong; Hu, Pan-Pan; Ye, Xu-Yan; Xiao, Hong-Bin; Kuang, Hai-Xue

2010-03-01

To optimize the process of extracting effective constituents from Aralia elata by response surface methodology. The independent variables were ethanol concentration, reflux time and solvent fold, the dependent variable was extraction rate of total saponins in Aralia elata. Linear or no-linear mathematic models were used to estimate the relationship between independent and dependent variables. Response surface methodology was used to optimize the process of extraction. The prediction was carried out through comparing the observed and predicted values. Regression coefficient of binomial fitting complex model was as high as 0.9617, the optimum conditions of extraction process were 70% ethanol, 2.5 hours for reflux, 20-fold solvent and 3 times for extraction. The bias between observed and predicted values was -2.41%. It shows the optimum model is highly predictive.

Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products.

PubMed

Bello, Alessandra; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena

2007-11-05

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new "leave one out" method, so that the number of original variables resulted further reduced.

Bayesian feature selection for high-dimensional linear regression via the Ising approximation with applications to genomics.

PubMed

Fisher, Charles K; Mehta, Pankaj

2015-06-01

Feature selection, identifying a subset of variables that are relevant for predicting a response, is an important and challenging component of many methods in statistics and machine learning. Feature selection is especially difficult and computationally intensive when the number of variables approaches or exceeds the number of samples, as is often the case for many genomic datasets. Here, we introduce a new approach--the Bayesian Ising Approximation (BIA)-to rapidly calculate posterior probabilities for feature relevance in L2 penalized linear regression. In the regime where the regression problem is strongly regularized by the prior, we show that computing the marginal posterior probabilities for features is equivalent to computing the magnetizations of an Ising model with weak couplings. Using a mean field approximation, we show it is possible to rapidly compute the feature selection path described by the posterior probabilities as a function of the L2 penalty. We present simulations and analytical results illustrating the accuracy of the BIA on some simple regression problems. Finally, we demonstrate the applicability of the BIA to high-dimensional regression by analyzing a gene expression dataset with nearly 30 000 features. These results also highlight the impact of correlations between features on Bayesian feature selection. An implementation of the BIA in C++, along with data for reproducing our gene expression analyses, are freely available at http://physics.bu.edu/∼pankajm/BIACode. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Bias due to two-stage residual-outcome regression analysis in genetic association studies.

PubMed

Demissie, Serkalem; Cupples, L Adrienne

2011-11-01

Association studies of risk factors and complex diseases require careful assessment of potential confounding factors. Two-stage regression analysis, sometimes referred to as residual- or adjusted-outcome analysis, has been increasingly used in association studies of single nucleotide polymorphisms (SNPs) and quantitative traits. In this analysis, first, a residual-outcome is calculated from a regression of the outcome variable on covariates and then the relationship between the adjusted-outcome and the SNP is evaluated by a simple linear regression of the adjusted-outcome on the SNP. In this article, we examine the performance of this two-stage analysis as compared with multiple linear regression (MLR) analysis. Our findings show that when a SNP and a covariate are correlated, the two-stage approach results in biased genotypic effect and loss of power. Bias is always toward the null and increases with the squared-correlation between the SNP and the covariate (). For example, for , 0.1, and 0.5, two-stage analysis results in, respectively, 0, 10, and 50% attenuation in the SNP effect. As expected, MLR was always unbiased. Since individual SNPs often show little or no correlation with covariates, a two-stage analysis is expected to perform as well as MLR in many genetic studies; however, it produces considerably different results from MLR and may lead to incorrect conclusions when independent variables are highly correlated. While a useful alternative to MLR under , the two -stage approach has serious limitations. Its use as a simple substitute for MLR should be avoided. © 2011 Wiley Periodicals, Inc.

Linear regression techniques for use in the EC tracer method of secondary organic aerosol estimation

NASA Astrophysics Data System (ADS)

Saylor, Rick D.; Edgerton, Eric S.; Hartsell, Benjamin E.

A variety of linear regression techniques and simple slope estimators are evaluated for use in the elemental carbon (EC) tracer method of secondary organic carbon (OC) estimation. Linear regression techniques based on ordinary least squares are not suitable for situations where measurement uncertainties exist in both regressed variables. In the past, regression based on the method of Deming [1943. Statistical Adjustment of Data. Wiley, London] has been the preferred choice for EC tracer method parameter estimation. In agreement with Chu [2005. Stable estimate of primary OC/EC ratios in the EC tracer method. Atmospheric Environment 39, 1383-1392], we find that in the limited case where primary non-combustion OC (OC non-comb) is assumed to be zero, the ratio of averages (ROA) approach provides a stable and reliable estimate of the primary OC-EC ratio, (OC/EC) pri. In contrast with Chu [2005. Stable estimate of primary OC/EC ratios in the EC tracer method. Atmospheric Environment 39, 1383-1392], however, we find that the optimal use of Deming regression (and the more general York et al. [2004. Unified equations for the slope, intercept, and standard errors of the best straight line. American Journal of Physics 72, 367-375] regression) provides excellent results as well. For the more typical case where OC non-comb is allowed to obtain a non-zero value, we find that regression based on the method of York is the preferred choice for EC tracer method parameter estimation. In the York regression technique, detailed information on uncertainties in the measurement of OC and EC is used to improve the linear best fit to the given data. If only limited information is available on the relative uncertainties of OC and EC, then Deming regression should be used. On the other hand, use of ROA in the estimation of secondary OC, and thus the assumption of a zero OC non-comb value, generally leads to an overestimation of the contribution of secondary OC to total measured OC.

Regression calibration for models with two predictor variables measured with error and their interaction, using instrumental variables and longitudinal data.

PubMed

Strand, Matthew; Sillau, Stefan; Grunwald, Gary K; Rabinovitch, Nathan

2014-02-10

Regression calibration provides a way to obtain unbiased estimators of fixed effects in regression models when one or more predictors are measured with error. Recent development of measurement error methods has focused on models that include interaction terms between measured-with-error predictors, and separately, methods for estimation in models that account for correlated data. In this work, we derive explicit and novel forms of regression calibration estimators and associated asymptotic variances for longitudinal models that include interaction terms, when data from instrumental and unbiased surrogate variables are available but not the actual predictors of interest. The longitudinal data are fit using linear mixed models that contain random intercepts and account for serial correlation and unequally spaced observations. The motivating application involves a longitudinal study of exposure to two pollutants (predictors) - outdoor fine particulate matter and cigarette smoke - and their association in interactive form with levels of a biomarker of inflammation, leukotriene E4 (LTE 4 , outcome) in asthmatic children. Because the exposure concentrations could not be directly observed, we used measurements from a fixed outdoor monitor and urinary cotinine concentrations as instrumental variables, and we used concentrations of fine ambient particulate matter and cigarette smoke measured with error by personal monitors as unbiased surrogate variables. We applied the derived regression calibration methods to estimate coefficients of the unobserved predictors and their interaction, allowing for direct comparison of toxicity of the different pollutants. We used simulations to verify accuracy of inferential methods based on asymptotic theory. Copyright © 2013 John Wiley & Sons, Ltd.

Potential impacts of climate variability on dengue hemorrhagic fever in Honduras, 2010.

PubMed

Zambrano, L I; Sevilla, C; Reyes-García, S Z; Sierra, M; Kafati, R; Rodriguez-Morales, A J; Mattar, S

2012-12-01

Climate change and variability are affecting human health and disease direct or indirectly through many mechanisms. Dengue is one of those diseases that is strongly influenced by climate variability; however its study in Central America has been poorly approached. In this study, we assessed potential associations between macroclimatic and microclimatic variation and dengue hemorrhagic fever (DHF) cases in the main hospital of Honduras during 2010. In this year, 3,353 cases of DHF were reported in the Hospital Escuela, Tegucigalpa. Climatic periods marked a difference of 158% in the mean incidence of cases, from El Niño weeks (-99% of cases below the mean incidence) to La Niña months (+59% of cases above it) (p<0.01). Linear regression showed significantly higher dengue incidence with lower values of Oceanic Niño Index (p=0.0097), higher rain probability (p=0.0149), accumulated rain (p=0.0443) and higher relative humidity (p=0.0292). At a multiple linear regression model using those variables, ONI values shown to be the most important and significant factor found to be associated with the monthly occurrence of DHF cases (r²=0.649; βstandardized=-0.836; p=0.01). As has been shown herein, climate variability is an important element influencing the dengue epidemiology in Honduras. However, it is necessary to extend these studies in this and other countries in the Central America region, because these models can be applied for surveillance as well as for prediction of dengue.

Seasonality in trauma admissions - Are daylight and weather variables better predictors than general cyclic effects?

PubMed

Røislien, Jo; Søvik, Signe; Eken, Torsten

2018-01-01

Trauma is a leading global cause of death, and predicting the burden of trauma admissions is vital for good planning of trauma care. Seasonality in trauma admissions has been found in several studies. Seasonal fluctuations in daylight hours, temperature and weather affect social and cultural practices but also individual neuroendocrine rhythms that may ultimately modify behaviour and potentially predispose to trauma. The aim of the present study was to explore to what extent the observed seasonality in daily trauma admissions could be explained by changes in daylight and weather variables throughout the year. Retrospective registry study on trauma admissions in the 10-year period 2001-2010 at Oslo University Hospital, Ullevål, Norway, where the amount of daylight varies from less than 6 hours to almost 19 hours per day throughout the year. Daily number of admissions was analysed by fitting non-linear Poisson time series regression models, simultaneously adjusting for several layers of temporal patterns, including a non-linear long-term trend and both seasonal and weekly cyclic effects. Five daylight and weather variables were explored, including hours of daylight and amount of precipitation. Models were compared using Akaike's Information Criterion (AIC). A regression model including daylight and weather variables significantly outperformed a traditional seasonality model in terms of AIC. A cyclic week effect was significant in all models. Daylight and weather variables are better predictors of seasonality in daily trauma admissions than mere information on day-of-year.

Statistical Methods for Quality Control of Steel Coils Manufacturing Process using Generalized Linear Models

NASA Astrophysics Data System (ADS)

García-Díaz, J. Carlos

2009-11-01

Fault detection and diagnosis is an important problem in process engineering. Process equipments are subject to malfunctions during operation. Galvanized steel is a value added product, furnishing effective performance by combining the corrosion resistance of zinc with the strength and formability of steel. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing and the increasingly stringent quality requirements in automotive industry has also demanded ongoing efforts in process control to make the process more robust. When faults occur, they change the relationship among these observed variables. This work compares different statistical regression models proposed in the literature for estimating the quality of galvanized steel coils on the basis of short time histories. Data for 26 batches were available. Five variables were selected for monitoring the process: the steel strip velocity, four bath temperatures and bath level. The entire data consisting of 48 galvanized steel coils was divided into sets. The first training data set was 25 conforming coils and the second data set was 23 nonconforming coils. Logistic regression is a modeling tool in which the dependent variable is categorical. In most applications, the dependent variable is binary. The results show that the logistic generalized linear models do provide good estimates of quality coils and can be useful for quality control in manufacturing process.

Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform - A case study in Wild Duck Lake Wetland, Beijing, China

NASA Astrophysics Data System (ADS)

Jing, Ran; Gong, Zhaoning; Zhao, Wenji; Pu, Ruiliang; Deng, Lei

2017-12-01

Above-bottom biomass (ABB) is considered as an important parameter for measuring the growth status of aquatic plants, and is of great significance for assessing health status of wetland ecosystems. In this study, Structure from Motion (SfM) technique was used to rebuild the study area with high overlapped images acquired by an unmanned aerial vehicle (UAV). We generated orthoimages and SfM dense point cloud data, from which vegetation indices (VIs) and SfM point cloud variables including average height (HAVG), standard deviation of height (HSD) and coefficient of variation of height (HCV) were extracted. These VIs and SfM point cloud variables could effectively characterize the growth status of aquatic plants, and thus they could be used to develop a simple linear regression model (SLR) and a stepwise linear regression model (SWL) with field measured ABB samples of aquatic plants. We also utilized a decision tree method to discriminate different types of aquatic plants. The experimental results indicated that (1) the SfM technique could effectively process high overlapped UAV images and thus be suitable for the reconstruction of fine texture feature of aquatic plant canopy structure; and (2) an SWL model based on point cloud variables: HAVG, HSD, HCV and two VIs: NGRDI, ExGR as independent variables has produced the best predictive result of ABB of aquatic plants in the study area, with a coefficient of determination of 0.84 and a relative root mean square error of 7.13%. In this analysis, a novel method for the quantitative inversion of a growth parameter (i.e., ABB) of aquatic plants in wetlands was demonstrated.

The impact of intrinsic and extrinsic factors on the job satisfaction of dentists.

PubMed

Goetz, K; Campbell, S M; Broge, B; Dörfer, C E; Brodowski, M; Szecsenyi, J

2012-10-01

The Two-Factor Theory of job satisfaction distinguishes between intrinsic-motivation (i.e. recognition, responsibility) and extrinsic-hygiene (i.e. job security, salary, working conditions) factors. The presence of intrinsic-motivation facilitates higher satisfaction and performance, whereas the absences of extrinsic factors help mitigate against dissatisfaction. The consideration of these factors and their impact on dentists' job satisfaction is essential for the recruitment and retention of dentists. The objective of the study is to assess the level of job satisfaction of German dentists and the factors that are associated with it. This cross-sectional study was based on a job satisfaction survey. Data were collected from 147 dentists working in 106 dental practices. Job satisfaction was measured with the 10-item Warr-Cook-Wall job satisfaction scale. Organizational characteristics were measured with two items. Linear regression analyses were performed in which each of the nine items of the job satisfaction scale (excluding overall satisfaction) were handled as dependent variables. A stepwise linear regression analysis was performed with overall job satisfaction as the dependent outcome variable, the nine items of job satisfaction and the two items of organizational characteristics controlled for age and gender as predictors. The response rate was 95.0%. Dentists were satisfied with 'freedom of working method' and mostly dissatisfied with their 'income'. Both variables are extrinsic factors. The regression analyses identified five items that were significantly associated with each item of the job satisfaction scale: 'age', 'mean weekly working time', 'period in the practice', 'number of dentist's assistant' and 'working atmosphere'. Within the stepwise linear regression analysis the intrinsic factor 'opportunity to use abilities' (β = 0.687) showed the highest score of explained variance (R(2) = 0.468) regarding overall job satisfaction. With respect to the Two-Factor Theory of job satisfaction both components, intrinsic and extrinsic, are essential for dentists but the presence of intrinsic motivating factors like the opportunity to use abilities has most positive impact on job satisfaction. The findings of this study will be helpful for further activities to improve the working conditions of dentists and to ensure quality of care. © 2012 John Wiley & Sons A/S.

Multi-decadal trend and space-time variability of sea level over the Indian Ocean since the 1950s: impact of decadal climate modes

NASA Astrophysics Data System (ADS)

Han, W.; Stammer, D.; Meehl, G. A.; Hu, A.; Sienz, F.

2016-12-01

Sea level varies on decadal and multi-decadal timescales over the Indian Ocean. The variations are not spatially uniform, and can deviate considerably from the global mean sea level rise (SLR) due to various geophysical processes. One of these processes is the change of ocean circulation, which can be partly attributed to natural internal modes of climate variability. Over the Indian Ocean, the most influential climate modes on decadal and multi-decadal timescales are the Interdecadal Pacific Oscillation (IPO) and decadal variability of the Indian Ocean dipole (IOD). Here, we first analyze observational datasets to investigate the impacts of IPO and IOD on spatial patterns of decadal and interdecadal (hereafter decal) sea level variability & multi-decadal trend over the Indian Ocean since the 1950s, using a new statistical approach of Bayesian Dynamical Linear regression Model (DLM). The Bayesian DLM overcomes the limitation of "time-constant (static)" regression coefficients in conventional multiple linear regression model, by allowing the coefficients to vary with time and therefore measuring "time-evolving (dynamical)" relationship between climate modes and sea level. For the multi-decadal sea level trend since the 1950s, our results show that climate modes and non-climate modes (the part that cannot be explained by climate modes) have comparable contributions in magnitudes but with different spatial patterns, with each dominating different regions of the Indian Ocean. For decadal variability, climate modes are the major contributors for sea level variations over most region of the tropical Indian Ocean. The relative importance of IPO and decadal variability of IOD, however, varies spatially. For example, while IOD decadal variability dominates IPO in the eastern equatorial basin (85E-100E, 5S-5N), IPO dominates IOD in causing sea level variations in the tropical southwest Indian Ocean (45E-65E, 12S-2S). To help decipher the possible contribution of external forcing to the multi-decadal sea level trend and decadal variability, we also analyze the model outputs from NCAR's Community Earth System Model (CESM) Large Ensemble Experiments, and compare the results with our observational analyses.

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Determination of water depth with high-resolution satellite imagery over variable bottom types

USGS Publications Warehouse

Stumpf, Richard P.; Holderied, Kristine; Sinclair, Mark

2003-01-01

A standard algorithm for determining depth in clear water from passive sensors exists; but it requires tuning of five parameters and does not retrieve depths where the bottom has an extremely low albedo. To address these issues, we developed an empirical solution using a ratio of reflectances that has only two tunable parameters and can be applied to low-albedo features. The two algorithms--the standard linear transform and the new ratio transform--were compared through analysis of IKONOS satellite imagery against lidar bathymetry. The coefficients for the ratio algorithm were tuned manually to a few depths from a nautical chart, yet performed as well as the linear algorithm tuned using multiple linear regression against the lidar. Both algorithms compensate for variable bottom type and albedo (sand, pavement, algae, coral) and retrieve bathymetry in water depths of less than 10-15 m. However, the linear transform does not distinguish depths >15 m and is more subject to variability across the studied atolls. The ratio transform can, in clear water, retrieve depths in >25 m of water and shows greater stability between different areas. It also performs slightly better in scattering turbidity than the linear transform. The ratio algorithm is somewhat noisier and cannot always adequately resolve fine morphology (structures smaller than 4-5 pixels) in water depths >15-20 m. In general, the ratio transform is more robust than the linear transform.

Habitat characteristics affecting fish assemblages on a Hawaiian coral reef

USGS Publications Warehouse

Friedlander, A.M.; Parrish, J.D.

1998-01-01

Habitat characteristics of a reef were examined as potential influences on fish assemblage structure, using underwater visual census to estimate numbers and biomass of all fishes visible on 42 benthic transects and making quantitative measurements of 13 variables of the corresponding physical habitat and sessile biota. Fish assemblages in the diverse set of benthic habitats were grouped by detrended correspondence analysis, and associated with six major habitat types. Statistical differences were shown between a number of these habitat types for various ensemble variables of the fish assemblages. Overall, both for complete assemblages and for component major trophic and mobility guilds, these variables tended to have higher values where reef substratum was more structurally or topographically complex, and closer to reef edges. When study sites were separately divided into five depth strata, the deeper strata tended to have statistically higher values of ensemble variables for the fish assemblages. Patterns with depth varied among the various trophic and mobility guilds. Multiple linear regression models indicated that for the complete assemblages and for most trophic and mobility guilds, a large part of the variability for most ensemble variables was explained by measures of holes in the substratum, with important contributions from measured substratum rugosity and depth. A strong linear relationship found by regression of mean fish length on mean volume of holes in the reef surface emphasized the importance of shelter for fish assemblages. Results of this study may have practical applications in designing reserve areas as well as theoretical value in helping to explain the organization of reef fish assemblages.

Predictors of Grades for Black Americans in a Non-Calculus, Preprofessional Physics Sequence.

ERIC Educational Resources Information Center

Vincent, Harold A.; And Others

Variables to predict grades in a noncalculus, preprofessional college physics course at Xavier University of Louisiana, a historically-black institution, were identified using linear regression. The two-semester, noncalculus physics course emphasizes the application of physics in the health professions. The study population consisted of 123…

EMI-Sensor Data to Identify Areas of Manure Accumulation on a Feedlot Surface

USDA-ARS?s Scientific Manuscript database

A study was initiated to test the validity of using electromagnetic induction (EMI) survey data, a prediction-based sampling strategy and ordinary linear regression modeling to predict spatially variable feedlot surface manure accumulation. A 30 m × 60 m feedlot pen with a central mound was selecte...

Post-Modeling Histogram Matching of Maps Produced Using Regression Trees

Treesearch

Andrew J. Lister; Tonya W. Lister

2006-01-01

Spatial predictive models often use statistical techniques that in some way rely on averaging of values. Estimates from linear modeling are known to be susceptible to truncation of variance when the independent (predictor) variables are measured with error. A straightforward post-processing technique (histogram matching) for attempting to mitigate this effect is...

Developing an Adequately Specified Model of State Level Student Achievement with Multilevel Data.

ERIC Educational Resources Information Center

Bernstein, Lawrence

Limitations of using linear, unilevel regression procedures in modeling student achievement are discussed. This study is a part of a broader study that is developing an empirically-based predictive model of variables associated with academic achievement from a multilevel perspective and examining the differences by which parameters are estimated…

The Prediction of Achievement and Time Spent in Instruction in a Self-Paced Individualized Course.

ERIC Educational Resources Information Center

Franklin, Thomas E.

Multiple linear regressions were employed to determine the relative contributions of cognitive and affective variables accounting for variance in college students' achievement and amount of time taken to complete a self-paced, individualized course. Study habits and attitudes (SSHA) made greater relative contributions to explaining total course…

Electronic Resource Expenditure and the Decline in Reference Transaction Statistics in Academic Libraries

ERIC Educational Resources Information Center

Dubnjakovic, Ana

2012-01-01

The current study investigates factors influencing increase in reference transactions in a typical week in academic libraries across the United States of America. Employing multiple regression analysis and general linear modeling, variables of interest from the "Academic Library Survey (ALS) 2006" survey (sample size 3960 academic libraries) were…

Spatio-temporal variations of nitric acid total columns from 9 years of IASI measurements - a driver study

NASA Astrophysics Data System (ADS)

Ronsmans, Gaétane; Wespes, Catherine; Hurtmans, Daniel; Clerbaux, Cathy; Coheur, Pierre-François

2018-04-01

This study aims to understand the spatial and temporal variability of HNO3 total columns in terms of explanatory variables. To achieve this, multiple linear regressions are used to fit satellite-derived time series of HNO3 daily averaged total columns. First, an analysis of the IASI 9-year time series (2008-2016) is conducted based on various equivalent latitude bands. The strong and systematic denitrification of the southern polar stratosphere is observed very clearly. It is also possible to distinguish, within the polar vortex, three regions which are differently affected by the denitrification. Three exceptional denitrification episodes in 2011, 2014 and 2016 are also observed in the Northern Hemisphere, due to unusually low arctic temperatures. The time series are then fitted by multivariate regressions to identify what variables are responsible for HNO3 variability in global distributions and time series, and to quantify their respective influence. Out of an ensemble of proxies (annual cycle, solar flux, quasi-biennial oscillation, multivariate ENSO index, Arctic and Antarctic oscillations and volume of polar stratospheric clouds), only the those defined as significant (p value < 0.05) by a selection algorithm are retained for each equivalent latitude band. Overall, the regression gives a good representation of HNO3 variability, with especially good results at high latitudes (60-80 % of the observed variability explained by the model). The regressions show the dominance of annual variability in all latitudinal bands, which is related to specific chemistry and dynamics depending on the latitudes. We find that the polar stratospheric clouds (PSCs) also have a major influence in the polar regions, and that their inclusion in the model improves the correlation coefficients and the residuals. However, there is still a relatively large portion of HNO3 variability that remains unexplained by the model, especially in the intertropical regions, where factors not included in the regression model (such as vegetation fires or lightning) may be at play.

Cooperation without Culture? The Null Effect of Generalized Trust on Intentional Homicide: A Cross-National Panel Analysis, 1995–2009

PubMed Central

Robbins, Blaine

2013-01-01

Sociologists, political scientists, and economists all suggest that culture plays a pivotal role in the development of large-scale cooperation. In this study, I used generalized trust as a measure of culture to explore if and how culture impacts intentional homicide, my operationalization of cooperation. I compiled multiple cross-national data sets and used pooled time-series linear regression, single-equation instrumental-variables linear regression, and fixed- and random-effects estimation techniques on an unbalanced panel of 118 countries and 232 observations spread over a 15-year time period. Results suggest that culture and large-scale cooperation form a tenuous relationship, while economic factors such as development, inequality, and geopolitics appear to drive large-scale cooperation. PMID:23527211

Evaluation of confidence intervals for a steady-state leaky aquifer model

USGS Publications Warehouse

Christensen, S.; Cooley, R.L.

1999-01-01

The fact that dependent variables of groundwater models are generally nonlinear functions of model parameters is shown to be a potentially significant factor in calculating accurate confidence intervals for both model parameters and functions of the parameters, such as the values of dependent variables calculated by the model. The Lagrangian method of Vecchia and Cooley [Vecchia, A.V. and Cooley, R.L., Water Resources Research, 1987, 23(7), 1237-1250] was used to calculate nonlinear Scheffe-type confidence intervals for the parameters and the simulated heads of a steady-state groundwater flow model covering 450 km2 of a leaky aquifer. The nonlinear confidence intervals are compared to corresponding linear intervals. As suggested by the significant nonlinearity of the regression model, linear confidence intervals are often not accurate. The commonly made assumption that widths of linear confidence intervals always underestimate the actual (nonlinear) widths was not correct. Results show that nonlinear effects can cause the nonlinear intervals to be asymmetric and either larger or smaller than the linear approximations. Prior information on transmissivities helps reduce the size of the confidence intervals, with the most notable effects occurring for the parameters on which there is prior information and for head values in parameter zones for which there is prior information on the parameters.The fact that dependent variables of groundwater models are generally nonlinear functions of model parameters is shown to be a potentially significant factor in calculating accurate confidence intervals for both model parameters and functions of the parameters, such as the values of dependent variables calculated by the model. The Lagrangian method of Vecchia and Cooley was used to calculate nonlinear Scheffe-type confidence intervals for the parameters and the simulated heads of a steady-state groundwater flow model covering 450 km2 of a leaky aquifer. The nonlinear confidence intervals are compared to corresponding linear intervals. As suggested by the significant nonlinearity of the regression model, linear confidence intervals are often not accurate. The commonly made assumption that widths of linear confidence intervals always underestimate the actual (nonlinear) widths was not correct. Results show that nonlinear effects can cause the nonlinear intervals to be asymmetric and either larger or smaller than the linear approximations. Prior information on transmissivities helps reduce the size of the confidence intervals, with the most notable effects occurring for the parameters on which there is prior information and for head values in parameter zones for which there is prior information on the parameters.

Regression Analysis of Stage Variability for West-Central Florida Lakes

USGS Publications Warehouse

Sacks, Laura A.; Ellison, Donald L.; Swancar, Amy

2008-01-01

The variability in a lake's stage depends upon many factors, including surface-water flows, meteorological conditions, and hydrogeologic characteristics near the lake. An understanding of the factors controlling lake-stage variability for a population of lakes may be helpful to water managers who set regulatory levels for lakes. The goal of this study is to determine whether lake-stage variability can be predicted using multiple linear regression and readily available lake and basin characteristics defined for each lake. Regressions were evaluated for a recent 10-year period (1996-2005) and for a historical 10-year period (1954-63). Ground-water pumping is considered to have affected stage at many of the 98 lakes included in the recent period analysis, and not to have affected stage at the 20 lakes included in the historical period analysis. For the recent period, regression models had coefficients of determination (R2) values ranging from 0.60 to 0.74, and up to five explanatory variables. Standard errors ranged from 21 to 37 percent of the average stage variability. Net leakage was the most important explanatory variable in regressions describing the full range and low range in stage variability for the recent period. The most important explanatory variable in the model predicting the high range in stage variability was the height over median lake stage at which surface-water outflow would occur. Other explanatory variables in final regression models for the recent period included the range in annual rainfall for the period and several variables related to local and regional hydrogeology: (1) ground-water pumping within 1 mile of each lake, (2) the amount of ground-water inflow (by category), (3) the head gradient between the lake and the Upper Floridan aquifer, and (4) the thickness of the intermediate confining unit. Many of the variables in final regression models are related to hydrogeologic characteristics, underscoring the importance of ground-water exchange in controlling the stage of karst lakes in Florida. Regression equations were used to predict lake-stage variability for the recent period for 12 additional lakes, and the median difference between predicted and observed values ranged from 11 to 23 percent. Coefficients of determination for the historical period were considerably lower (maximum R2 of 0.28) than for the recent period. Reasons for these low R2 values are probably related to the small number of lakes (20) with stage data for an equivalent time period that were unaffected by ground-water pumping, the similarity of many of the lake types (large surface-water drainage lakes), and the greater uncertainty in defining historical basin characteristics. The lack of lake-stage data unaffected by ground-water pumping and the poor regression results obtained for that group of lakes limit the ability to predict natural lake-stage variability using this method in west-central Florida.

Normality of raw data in general linear models: The most widespread myth in statistics

USGS Publications Warehouse

Kery, Marc; Hatfield, Jeff S.

2003-01-01

In years of statistical consulting for ecologists and wildlife biologists, by far the most common misconception we have come across has been the one about normality in general linear models. These comprise a very large part of the statistical models used in ecology and include t tests, simple and multiple linear regression, polynomial regression, and analysis of variance (ANOVA) and covariance (ANCOVA). There is a widely held belief that the normality assumption pertains to the raw data rather than to the model residuals. We suspect that this error may also occur in countless published studies, whenever the normality assumption is tested prior to analysis. This may lead to the use of nonparametric alternatives (if there are any), when parametric tests would indeed be appropriate, or to use of transformations of raw data, which may introduce hidden assumptions such as multiplicative effects on the natural scale in the case of log-transformed data. Our aim here is to dispel this myth. We very briefly describe relevant theory for two cases of general linear models to show that the residuals need to be normally distributed if tests requiring normality are to be used, such as t and F tests. We then give two examples demonstrating that the distribution of the response variable may be nonnormal, and yet the residuals are well behaved. We do not go into the issue of how to test normality; instead we display the distributions of response variables and residuals graphically.

Efficient robust doubly adaptive regularized regression with applications.

PubMed

Karunamuni, Rohana J; Kong, Linglong; Tu, Wei

2018-01-01

We consider the problem of estimation and variable selection for general linear regression models. Regularized regression procedures have been widely used for variable selection, but most existing methods perform poorly in the presence of outliers. We construct a new penalized procedure that simultaneously attains full efficiency and maximum robustness. Furthermore, the proposed procedure satisfies the oracle properties. The new procedure is designed to achieve sparse and robust solutions by imposing adaptive weights on both the decision loss and the penalty function. The proposed method of estimation and variable selection attains full efficiency when the model is correct and, at the same time, achieves maximum robustness when outliers are present. We examine the robustness properties using the finite-sample breakdown point and an influence function. We show that the proposed estimator attains the maximum breakdown point. Furthermore, there is no loss in efficiency when there are no outliers or the error distribution is normal. For practical implementation of the proposed method, we present a computational algorithm. We examine the finite-sample and robustness properties using Monte Carlo studies. Two datasets are also analyzed.

Prediction of hourly PM2.5 using a space-time support vector regression model

NASA Astrophysics Data System (ADS)

Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

2018-05-01

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

Estimating effects of limiting factors with regression quantiles

USGS Publications Warehouse

Cade, B.S.; Terrell, J.W.; Schroeder, R.L.

1999-01-01

In a recent Concepts paper in Ecology, Thomson et al. emphasized that assumptions of conventional correlation and regression analyses fundamentally conflict with the ecological concept of limiting factors, and they called for new statistical procedures to address this problem. The analytical issue is that unmeasured factors may be the active limiting constraint and may induce a pattern of unequal variation in the biological response variable through an interaction with the measured factors. Consequently, changes near the maxima, rather than at the center of response distributions, are better estimates of the effects expected when the observed factor is the active limiting constraint. Regression quantiles provide estimates for linear models fit to any part of a response distribution, including near the upper bounds, and require minimal assumptions about the form of the error distribution. Regression quantiles extend the concept of one-sample quantiles to the linear model by solving an optimization problem of minimizing an asymmetric function of absolute errors. Rank-score tests for regression quantiles provide tests of hypotheses and confidence intervals for parameters in linear models with heteroscedastic errors, conditions likely to occur in models of limiting ecological relations. We used selected regression quantiles (e.g., 5th, 10th, ..., 95th) and confidence intervals to test hypotheses that parameters equal zero for estimated changes in average annual acorn biomass due to forest canopy cover of oak (Quercus spp.) and oak species diversity. Regression quantiles also were used to estimate changes in glacier lily (Erythronium grandiflorum) seedling numbers as a function of lily flower numbers, rockiness, and pocket gopher (Thomomys talpoides fossor) activity, data that motivated the query by Thomson et al. for new statistical procedures. Both example applications showed that effects of limiting factors estimated by changes in some upper regression quantile (e.g., 90-95th) were greater than if effects were estimated by changes in the means from standard linear model procedures. Estimating a range of regression quantiles (e.g., 5-95th) provides a comprehensive description of biological response patterns for exploratory and inferential analyses in observational studies of limiting factors, especially when sampling large spatial and temporal scales.

Prediction system of hydroponic plant growth and development using algorithm Fuzzy Mamdani method

NASA Astrophysics Data System (ADS)

Sudana, I. Made; Purnawirawan, Okta; Arief, Ulfa Mediaty

2017-03-01

Hydroponics is a method of farming without soil. One of the Hydroponic plants is Watercress (Nasturtium Officinale). The development and growth process of hydroponic Watercress was influenced by levels of nutrients, acidity and temperature. The independent variables can be used as input variable system to predict the value level of plants growth and development. The prediction system is using Fuzzy Algorithm Mamdani method. This system was built to implement the function of Fuzzy Inference System (Fuzzy Inference System/FIS) as a part of the Fuzzy Logic Toolbox (FLT) by using MATLAB R2007b. FIS is a computing system that works on the principle of fuzzy reasoning which is similar to humans' reasoning. Basically FIS consists of four units which are fuzzification unit, fuzzy logic reasoning unit, base knowledge unit and defuzzification unit. In addition to know the effect of independent variables on the plants growth and development that can be visualized with the function diagram of FIS output surface that is shaped three-dimensional, and statistical tests based on the data from the prediction system using multiple linear regression method, which includes multiple linear regression analysis, T test, F test, the coefficient of determination and donations predictor that are calculated using SPSS (Statistical Product and Service Solutions) software applications.

Comparative study of Poincaré plot analysis using short electroencephalogram signals during anaesthesia with spectral edge frequency 95 and bispectral index.

PubMed

Hayashi, K; Yamada, T; Sawa, T

2015-03-01

The return or Poincaré plot is a non-linear analytical approach in a two-dimensional plane, where a timed signal is plotted against itself after a time delay. Its scatter pattern reflects the randomness and variability in the signals. Quantification of a Poincaré plot of the electroencephalogram has potential to determine anaesthesia depth. We quantified the degree of dispersion (i.e. standard deviation, SD) along the diagonal line of the electroencephalogram-Poincaré plot (named as SD1/SD2), and compared SD1/SD2 values with spectral edge frequency 95 (SEF95) and bispectral index values. The regression analysis showed a tight linear regression equation with a coefficient of determination (R(2) ) value of 0.904 (p < 0.0001) between the Poincaré index (SD1/SD2) and SEF95, and a moderate linear regression equation between SD1/SD2 and bispectral index (R(2)  = 0.346, p < 0.0001). Quantification of the Poincaré plot tightly correlates with SEF95, reflecting anaesthesia-dependent changes in electroencephalogram oscillation. © 2014 The Association of Anaesthetists of Great Britain and Ireland.

The effects of cognitive – linguistic variables and language experience on behavioural and kinematic performances in nonword learning

PubMed Central

Sasisekaran, Jayanthi; Weisberg, Sanford

2013-01-01

The aim of the present study was to investigate the effect of cognitive – linguistic variables and language experience on behavioral and kinematic measures of nonword learning in young adults. Group 1 consisted of thirteen participants who spoke American English as the first and only language. Group 2 consisted of seven participants with varying levels of proficiency in a second language. Logistic regression of the percent of correct productions revealed short-term memory to be a significant contributor. The bilingual group showed better performance compared to the monolinguals. Linear regression of the kinematic data revealed that the short – term memory variable contributed significantly to movement coordination. Differences were not observed between the bilingual and the monolingual speakers in kinematic performance. Nonword properties including syllable length and complexity influenced both behavioral and kinematic performance. The findings supported the observation that nonword repetition is multiply determined in adults. PMID:22476630

Sibling dilution hypothesis: a regression surface analysis.

PubMed

Marjoribanks, K

2001-08-01

This study examined relationships between sibship size (the number of children in a family), birth order, and measures of academic performance, academic self-concept, and educational aspirations at different levels of family educational resources. As part of a national longitudinal study of Australian secondary school students data were collected from 2,530 boys and 2,450 girls in Years 9 and 10. Regression surfaces were constructed from models that included terms to account for linear, interaction, and curvilinear associations among the variables. Analysis suggests the general propositions (a) family educational resources have significant associations with children's school-related outcomes at different levels of sibling variables, the relationships for girls being curvilinear, and (b) sibling variables continue to have small significant associations with affective and cognitive outcomes, after taking into account variations in family educational resources. That is, the investigation provides only partial support for the sibling dilution hypothesis.

Relationship of negative self-schemas and attachment styles with appearance schemas.

PubMed

Ledoux, Tracey; Winterowd, Carrie; Richardson, Tamara; Clark, Julie Dorton

2010-06-01

The purpose was to test, among women, the relationship between negative self-schemas and styles of attachment with men and women and two types of appearance investment (Self-evaluative and Motivational Salience). Predominantly Caucasian undergraduate women (N=194) completed a modified version of the Relationship Questionnaire, the Young Schema Questionnaire-Short Form, and the Appearance Schemas Inventory-Revised. Linear multiple regression analyses were conducted with Motivational Salience and Self-evaluative Salience of appearance serving as dependent variables and relevant demographic variables, negative self-schemas, and styles of attachment to men serving as independent variables. Styles of attachment to women were not entered into these regression models because Pearson correlations indicated they were not related to either dependent variable. Self-evaluative Salience of appearance was related to impaired autonomy and performance negative self-schema and the preoccupation style of attachment with men, while Motivational Salience of appearance was related only to the preoccupation style of attachment with men. 2010 Elsevier Ltd. All rights reserved.

What makes patients with fibromyalgia feel better? Correlations between Patient Global Impression of Improvement and changes in clinical symptoms and function: a pooled analysis of 4 randomized placebo-controlled trials of duloxetine.

PubMed

Hudson, James I; Arnold, Lesley M; Bradley, Laurence A; Choy, Ernest H S; Mease, Philip J; Wang, Fujun; Ahl, Jonna; Wohlreich, Madelaine M

2009-11-01

To investigate the relationship between changes in clinical rating scale items and endpoint Patient Global Impression of Improvement (PGI-I). Data were pooled from 4 randomized, double-blind, placebo-controlled studies of duloxetine in patients with fibromyalgia (FM). Variables included in the analyses were those that assessed symptoms in FM domains of pain, fatigue, sleep, cognitive difficulties, emotional well-being, physical function, and impact on daily living. The association of endpoint PGI-I with changes from baseline in individual variables was assessed using Pearson product-moment correlations (r). Stepwise linear regression was used to identify those variables for which changes from baseline were statistically significant independent predictors of the endpoint PGI-I ratings. Changes in pain variables and interference of symptoms with the ability to work were highly correlated (r >or= 0.5 or r

Associations of blood lead, cadmium, and mercury with estimated glomerular filtration rate in the Korean general population: Analysis of 2008-2010 Korean National Health and Nutrition Examination Survey data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Yangho; Lee, Byung-Kook, E-mail: bklee@sch.ac.kr

Introduction: The objective of this study was to evaluate associations between blood lead, cadmium, and mercury levels with estimated glomerular filtration rate in a general population of South Korean adults. Methods: This was a cross-sectional study based on data obtained in the Korean National Health and Nutrition Examination Survey (KNHANES) (2008-2010). The final analytical sample consisted of 5924 participants. Estimated glomerular filtration rate (eGFR) was calculated using the MDRD Study equation as an indicator of glomerular function. Results: In multiple linear regression analysis of log2-transformed blood lead as a continuous variable on eGFR, after adjusting for covariates including cadmium andmore » mercury, the difference in eGFR levels associated with doubling of blood lead were -2.624 mL/min per 1.73 m Superscript-Two (95% CI: -3.803 to -1.445). In multiple linear regression analysis using quartiles of blood lead as the independent variable, the difference in eGFR levels comparing participants in the highest versus the lowest quartiles of blood lead was -3.835 mL/min per 1.73 m Superscript-Two (95% CI: -5.730 to -1.939). In a multiple linear regression analysis using blood cadmium and mercury, as continuous or categorical variables, as independent variables, neither metal was a significant predictor of eGFR. Odds ratios (ORs) and 95% CI values for reduced eGFR calculated for log2-transformed blood metals and quartiles of the three metals showed similar trends after adjustment for covariates. Discussion: In this large, representative sample of South Korean adults, elevated blood lead level was consistently associated with lower eGFR levels and with the prevalence of reduced eGFR even in blood lead levels below 10 {mu}g/dL. In conclusion, elevated blood lead level was associated with lower eGFR in a Korean general population, supporting the role of lead as a risk factor for chronic kidney disease.« less

Mean Velocity vs. Mean Propulsive Velocity vs. Peak Velocity: Which Variable Determines Bench Press Relative Load With Higher Reliability?

PubMed

García-Ramos, Amador; Pestaña-Melero, Francisco L; Pérez-Castilla, Alejandro; Rojas, Francisco J; Gregory Haff, G

2018-05-01

García-Ramos, A, Pestaña-Melero, FL, Pérez-Castilla, A, Rojas, FJ, and Haff, GG. Mean velocity vs. mean propulsive velocity vs. peak velocity: which variable determines bench press relative load with higher reliability? J Strength Cond Res 32(5): 1273-1279, 2018-This study aimed to compare between 3 velocity variables (mean velocity [MV], mean propulsive velocity [MPV], and peak velocity [PV]): (a) the linearity of the load-velocity relationship, (b) the accuracy of general regression equations to predict relative load (%1RM), and (c) the between-session reliability of the velocity attained at each percentage of the 1-repetition maximum (%1RM). The full load-velocity relationship of 30 men was evaluated by means of linear regression models in the concentric-only and eccentric-concentric bench press throw (BPT) variants performed with a Smith machine. The 2 sessions of each BPT variant were performed within the same week separated by 48-72 hours. The main findings were as follows: (a) the MV showed the strongest linearity of the load-velocity relationship (median r = 0.989 for concentric-only BPT and 0.993 for eccentric-concentric BPT), followed by MPV (median r = 0.983 for concentric-only BPT and 0.980 for eccentric-concentric BPT), and finally PV (median r = 0.974 for concentric-only BPT and 0.969 for eccentric-concentric BPT); (b) the accuracy of the general regression equations to predict relative load (%1RM) from movement velocity was higher for MV (SEE = 3.80-4.76%1RM) than for MPV (SEE = 4.91-5.56%1RM) and PV (SEE = 5.36-5.77%1RM); and (c) the PV showed the lowest within-subjects coefficient of variation (3.50%-3.87%), followed by MV (4.05%-4.93%), and finally MPV (5.11%-6.03%). Taken together, these results suggest that the MV could be the most appropriate variable for monitoring the relative load (%1RM) in the BPT exercise performed in a Smith machine.

Multiple Use One-Sided Hypotheses Testing in Univariate Linear Calibration

NASA Technical Reports Server (NTRS)

Krishnamoorthy, K.; Kulkarni, Pandurang M.; Mathew, Thomas

1996-01-01

Consider a normally distributed response variable, related to an explanatory variable through the simple linear regression model. Data obtained on the response variable, corresponding to known values of the explanatory variable (i.e., calibration data), are to be used for testing hypotheses concerning unknown values of the explanatory variable. We consider the problem of testing an unlimited sequence of one sided hypotheses concerning the explanatory variable, using the corresponding sequence of values of the response variable and the same set of calibration data. This is the situation of multiple use of the calibration data. The tests derived in this context are characterized by two types of uncertainties: one uncertainty associated with the sequence of values of the response variable, and a second uncertainty associated with the calibration data. We derive tests based on a condition that incorporates both of these uncertainties. The solution has practical applications in the decision limit problem. We illustrate our results using an example dealing with the estimation of blood alcohol concentration based on breath estimates of the alcohol concentration. In the example, the problem is to test if the unknown blood alcohol concentration of an individual exceeds a threshold that is safe for driving.

Testing concordance of instrumental variable effects in generalized linear models with application to Mendelian randomization

PubMed Central

Dai, James Y.; Chan, Kwun Chuen Gary; Hsu, Li

2014-01-01

Instrumental variable regression is one way to overcome unmeasured confounding and estimate causal effect in observational studies. Built on structural mean models, there has been considerale work recently developed for consistent estimation of causal relative risk and causal odds ratio. Such models can sometimes suffer from identification issues for weak instruments. This hampered the applicability of Mendelian randomization analysis in genetic epidemiology. When there are multiple genetic variants available as instrumental variables, and causal effect is defined in a generalized linear model in the presence of unmeasured confounders, we propose to test concordance between instrumental variable effects on the intermediate exposure and instrumental variable effects on the disease outcome, as a means to test the causal effect. We show that a class of generalized least squares estimators provide valid and consistent tests of causality. For causal effect of a continuous exposure on a dichotomous outcome in logistic models, the proposed estimators are shown to be asymptotically conservative. When the disease outcome is rare, such estimators are consistent due to the log-linear approximation of the logistic function. Optimality of such estimators relative to the well-known two-stage least squares estimator and the double-logistic structural mean model is further discussed. PMID:24863158

Can Functional Cardiac Age be Predicted from ECG in a Normal Healthy Population

NASA Technical Reports Server (NTRS)

Schlegel, Todd; Starc, Vito; Leban, Manja; Sinigoj, Petra; Vrhovec, Milos

2011-01-01

In a normal healthy population, we desired to determine the most age-dependent conventional and advanced ECG parameters. We hypothesized that changes in several ECG parameters might correlate with age and together reliably characterize the functional age of the heart. Methods: An initial study population of 313 apparently healthy subjects was ultimately reduced to 148 subjects (74 men, 84 women, in the range from 10 to 75 years of age) after exclusion criteria. In all subjects, ECG recordings (resting 5-minute 12-lead high frequency ECG) were evaluated via custom software programs to calculate up to 85 different conventional and advanced ECG parameters including beat-to-beat QT and RR variability, waveform complexity, and signal-averaged, high-frequency and spatial/spatiotemporal ECG parameters. The prediction of functional age was evaluated by multiple linear regression analysis using the best 5 univariate predictors. Results: Ignoring what were ultimately small differences between males and females, the functional age was found to be predicted (R2= 0.69, P < 0.001) from a linear combination of 5 independent variables: QRS elevation in the frontal plane (p<0.001), a new repolarization parameter QTcorr (p<0.001), mean high frequency QRS amplitude (p=0.009), the variability parameter % VLF of RRV (p=0.021) and the P-wave width (p=0.10). Here, QTcorr represents the correlation between the calculated QT and the measured QT signal. Conclusions: In apparently healthy subjects with normal conventional ECGs, functional cardiac age can be estimated by multiple linear regression analysis of mostly advanced ECG results. Because some parameters in the regression formula, such as QTcorr, high frequency QRS amplitude and P-wave width also change with disease in the same direction as with increased age, increased functional age of the heart may reflect subtle age-related pathologies in cardiac electrical function that are usually hidden on conventional ECG.

A quasi-Monte-Carlo comparison of parametric and semiparametric regression methods for heavy-tailed and non-normal data: an application to healthcare costs.

PubMed

Jones, Andrew M; Lomas, James; Moore, Peter T; Rice, Nigel

2016-10-01

We conduct a quasi-Monte-Carlo comparison of the recent developments in parametric and semiparametric regression methods for healthcare costs, both against each other and against standard practice. The population of English National Health Service hospital in-patient episodes for the financial year 2007-2008 (summed for each patient) is randomly divided into two equally sized subpopulations to form an estimation set and a validation set. Evaluating out-of-sample using the validation set, a conditional density approximation estimator shows considerable promise in forecasting conditional means, performing best for accuracy of forecasting and among the best four for bias and goodness of fit. The best performing model for bias is linear regression with square-root-transformed dependent variables, whereas a generalized linear model with square-root link function and Poisson distribution performs best in terms of goodness of fit. Commonly used models utilizing a log-link are shown to perform badly relative to other models considered in our comparison.

[Quantitative structure-gas chromatographic retention relationship of polycyclic aromatic sulfur heterocycles using molecular electronegativity-distance vector].

PubMed

Li, Zhenghua; Cheng, Fansheng; Xia, Zhining

2011-01-01

The chemical structures of 114 polycyclic aromatic sulfur heterocycles (PASHs) have been studied by molecular electronegativity-distance vector (MEDV). The linear relationships between gas chromatographic retention index and the MEDV have been established by a multiple linear regression (MLR) model. The results of variable selection by stepwise multiple regression (SMR) and the powerful predictive abilities of the optimization model appraised by leave-one-out cross-validation showed that the optimization model with the correlation coefficient (R) of 0.994 7 and the cross-validated correlation coefficient (Rcv) of 0.994 0 possessed the best statistical quality. Furthermore, when the 114 PASHs compounds were divided into calibration and test sets in the ratio of 2:1, the statistical analysis showed our models possesses almost equal statistical quality, the very similar regression coefficients and the good robustness. The quantitative structure-retention relationship (QSRR) model established may provide a convenient and powerful method for predicting the gas chromatographic retention of PASHs.

Hierarchical cluster-based partial least squares regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models.

PubMed

Tøndel, Kristin; Indahl, Ulf G; Gjuvsland, Arne B; Vik, Jon Olav; Hunter, Peter; Omholt, Stig W; Martens, Harald

2011-06-01

Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

PubMed Central

2011-01-01

Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. Conclusions HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems. PMID:21627852

Assessing environmental inequalities in ambient air pollution across urban Australia.

PubMed

Knibbs, Luke D; Barnett, Adrian G

2015-04-01

Identifying inequalities in air pollution levels across population groups can help address environmental justice concerns. We were interested in assessing these inequalities across major urban areas in Australia. We used a land-use regression model to predict ambient nitrogen dioxide (NO2) levels and sought the best socio-economic and population predictor variables. We used a generalised least squares model that accounted for spatial correlation in NO2 levels to examine the associations between the variables. We found that the best model included the index of economic resources (IER) score as a non-linear variable and the percentage of non-Indigenous persons as a linear variable. NO2 levels decreased with increasing IER scores (higher scores indicate less disadvantage) in almost all major urban areas, and NO2 also decreased slightly as the percentage of non-Indigenous persons increased. However, the magnitude of differences in NO2 levels was small and may not translate into substantive differences in health. Copyright © 2015 Elsevier Ltd. All rights reserved.

4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling.

PubMed

Ghasemi, Jahan B; Safavi-Sohi, Reihaneh; Barbosa, Euzébio G

2012-02-01

A quasi 4D-QSAR has been carried out on a series of potent Gram-negative LpxC inhibitors. This approach makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. This new methodology is based on the generation of a conformational ensemble profile, CEP, for each compound instead of only one conformation, followed by the calculation intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are independent variables employed in a QSAR analysis. The comparison of the proposed methodology to comparative molecular field analysis (CoMFA) formalism was performed. This methodology explores jointly the main features of CoMFA and 4D-QSAR models. Step-wise multiple linear regression was used for the selection of the most informative variables. After variable selection, multiple linear regression (MLR) and partial least squares (PLS) methods used for building the regression models. Leave-N-out cross-validation (LNO), and Y-randomization were performed in order to confirm the robustness of the model in addition to analysis of the independent test set. Best models provided the following statistics: [Formula in text] (PLS) and [Formula in text] (MLR). Docking study was applied to investigate the major interactions in protein-ligand complex with CDOCKER algorithm. Visualization of the descriptors of the best model helps us to interpret the model from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Correlation of Vitamin D status and orthodontic-induced external apical root resorption.

PubMed

Tehranchi, Azita; Sadighnia, Azin; Younessian, Farnaz; Abdi, Amir H; Shirvani, Armin

2017-01-01

Adequate Vitamin D is essential for dental and skeletal health in children and adult. The purpose of this study was to assess the correlation of serum Vitamin D level with external-induced apical root resorption (EARR) following fixed orthodontic treatment. In this cross-sectional study, the prevalence of Vitamin D deficiency (defined by25-hydroxyvitamin-D) was determined in 34 patients (23.5% male; age range 12-23 years; mean age 16.63 ± 2.84) treated with fixed orthodontic treatment. Root resorption of four maxillary incisors was measured using before and after periapical radiographs (136 measured teeth) by means of a design-to-purpose software to optimize data collection. Teeth with a maximum percentage of root resorption (%EARR) were indicated as representative root resorption for each patient. A multiple linear regression model and Pearson correlation coefficient were used to assess the association of Vitamin D status and observed EARR. P < 0.05 was considered statistically significant. The Pearson coefficient between these two variables was determined about 0.15 ( P = 0.38). Regression analysis revealed that Vitamin D status of the patients demonstrated no significant statistical correlation with EARR, after adjustment of confounding variables using linear regression model ( P > 0.05). This study suggests that Vitamin D level is not among the clinical variables that are potential contributors for EARR. The prevalence of Vitamin D deficiency does not differ in patients with higher EARR. These data suggest the possibility that Vitamin D insufficiency may not contribute to the development of more apical root resorption although this remains to be confirmed by further longitudinal cohort studies.

Missing-value estimation using linear and non-linear regression with Bayesian gene selection.

PubMed

Zhou, Xiaobo; Wang, Xiaodong; Dougherty, Edward R

2003-11-22

Data from microarray experiments are usually in the form of large matrices of expression levels of genes under different experimental conditions. Owing to various reasons, there are frequently missing values. Estimating these missing values is important because they affect downstream analysis, such as clustering, classification and network design. Several methods of missing-value estimation are in use. The problem has two parts: (1) selection of genes for estimation and (2) design of an estimation rule. We propose Bayesian variable selection to obtain genes to be used for estimation, and employ both linear and nonlinear regression for the estimation rule itself. Fast implementation issues for these methods are discussed, including the use of QR decomposition for parameter estimation. The proposed methods are tested on data sets arising from hereditary breast cancer and small round blue-cell tumors. The results compare very favorably with currently used methods based on the normalized root-mean-square error. The appendix is available from http://gspsnap.tamu.edu/gspweb/zxb/missing_zxb/ (user: gspweb; passwd: gsplab).

A quadratic regression modelling on paddy production in the area of Perlis

NASA Astrophysics Data System (ADS)

Goh, Aizat Hanis Annas; Ali, Zalila; Nor, Norlida Mohd; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

2017-08-01

Polynomial regression models are useful in situations in which the relationship between a response variable and predictor variables is curvilinear. Polynomial regression fits the nonlinear relationship into a least squares linear regression model by decomposing the predictor variables into a kth order polynomial. The polynomial order determines the number of inflexions on the curvilinear fitted line. A second order polynomial forms a quadratic expression (parabolic curve) with either a single maximum or minimum, a third order polynomial forms a cubic expression with both a relative maximum and a minimum. This study used paddy data in the area of Perlis to model paddy production based on paddy cultivation characteristics and environmental characteristics. The results indicated that a quadratic regression model best fits the data and paddy production is affected by urea fertilizer application and the interaction between amount of average rainfall and percentage of area defected by pest and disease. Urea fertilizer application has a quadratic effect in the model which indicated that if the number of days of urea fertilizer application increased, paddy production is expected to decrease until it achieved a minimum value and paddy production is expected to increase at higher number of days of urea application. The decrease in paddy production with an increased in rainfall is greater, the higher the percentage of area defected by pest and disease.

Analyzing industrial energy use through ordinary least squares regression models

NASA Astrophysics Data System (ADS)

Golden, Allyson Katherine

Extensive research has been performed using regression analysis and calibrated simulations to create baseline energy consumption models for residential buildings and commercial institutions. However, few attempts have been made to discuss the applicability of these methodologies to establish baseline energy consumption models for industrial manufacturing facilities. In the few studies of industrial facilities, the presented linear change-point and degree-day regression analyses illustrate ideal cases. It follows that there is a need in the established literature to discuss the methodologies and to determine their applicability for establishing baseline energy consumption models of industrial manufacturing facilities. The thesis determines the effectiveness of simple inverse linear statistical regression models when establishing baseline energy consumption models for industrial manufacturing facilities. Ordinary least squares change-point and degree-day regression methods are used to create baseline energy consumption models for nine different case studies of industrial manufacturing facilities located in the southeastern United States. The influence of ambient dry-bulb temperature and production on total facility energy consumption is observed. The energy consumption behavior of industrial manufacturing facilities is only sometimes sufficiently explained by temperature, production, or a combination of the two variables. This thesis also provides methods for generating baseline energy models that are straightforward and accessible to anyone in the industrial manufacturing community. The methods outlined in this thesis may be easily replicated by anyone that possesses basic spreadsheet software and general knowledge of the relationship between energy consumption and weather, production, or other influential variables. With the help of simple inverse linear regression models, industrial manufacturing facilities may better understand their energy consumption and production behavior, and identify opportunities for energy and cost savings. This thesis study also utilizes change-point and degree-day baseline energy models to disaggregate facility annual energy consumption into separate industrial end-user categories. The baseline energy model provides a suitable and economical alternative to sub-metering individual manufacturing equipment. One case study describes the conjoined use of baseline energy models and facility information gathered during a one-day onsite visit to perform an end-point energy analysis of an injection molding facility conducted by the Alabama Industrial Assessment Center. Applying baseline regression model results to the end-point energy analysis allowed the AIAC to better approximate the annual energy consumption of the facility's HVAC system.

Handling nonnormality and variance heterogeneity for quantitative sublethal toxicity tests.

PubMed

Ritz, Christian; Van der Vliet, Leana

2009-09-01

The advantages of using regression-based techniques to derive endpoints from environmental toxicity data are clear, and slowly, this superior analytical technique is gaining acceptance. As use of regression-based analysis becomes more widespread, some of the associated nuances and potential problems come into sharper focus. Looking at data sets that cover a broad spectrum of standard test species, we noticed that some model fits to data failed to meet two key assumptions-variance homogeneity and normality-that are necessary for correct statistical analysis via regression-based techniques. Failure to meet these assumptions often is caused by reduced variance at the concentrations showing severe adverse effects. Although commonly used with linear regression analysis, transformation of the response variable only is not appropriate when fitting data using nonlinear regression techniques. Through analysis of sample data sets, including Lemna minor, Eisenia andrei (terrestrial earthworm), and algae, we show that both the so-called Box-Cox transformation and use of the Poisson distribution can help to correct variance heterogeneity and nonnormality and so allow nonlinear regression analysis to be implemented. Both the Box-Cox transformation and the Poisson distribution can be readily implemented into existing protocols for statistical analysis. By correcting for nonnormality and variance heterogeneity, these two statistical tools can be used to encourage the transition to regression-based analysis and the depreciation of less-desirable and less-flexible analytical techniques, such as linear interpolation.

Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring.

PubMed

Caicedo, Alexander; Varon, Carolina; Hunyadi, Borbala; Papademetriou, Maria; Tachtsidis, Ilias; Van Huffel, Sabine

2016-01-01

Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS) and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP), assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + ϵ . SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP) with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first 3 days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen saturation from the NIRS measurements, facilitating the use of NIRS as a surrogate measure for cerebral blood flow (CBF). The second case study used data from a 3-years old infant under Extra Corporeal Membrane Oxygenation (ECMO), here SIDE-ObSP decomposed cerebral/peripheral tissue oxygenation, as a sum of the partial contributions from different systemic variables, facilitating the comparison between the effects of each systemic variable on the cerebral/peripheral hemodynamics.

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

Relations that affect the probability and prediction of nitrate concentration in private wells in the glacial aquifer system in the United States

USGS Publications Warehouse

Warner, Kelly L.; Arnold, Terri L.

2010-01-01

Nitrate in private wells in the glacial aquifer system is a concern for an estimated 17 million people using private wells because of the proximity of many private wells to nitrogen sources. Yet, less than 5 percent of private wells sampled in this study contained nitrate in concentrations that exceeded the U.S. Environmental Protection Agency (USEPA) Maximum Contaminant Level (MCL) of 10 mg/L (milligrams per liter) as N (nitrogen). However, this small group with nitrate concentrations above the USEPA MCL includes some of the highest nitrate concentrations detected in groundwater from private wells (77 mg/L). Median nitrate concentration measured in groundwater from private wells in the glacial aquifer system (0.11 mg/L as N) is lower than that in water from other unconsolidated aquifers and is not strongly related to surface sources of nitrate. Background concentration of nitrate is less than 1 mg/L as N. Although overall nitrate concentration in private wells was low relative to the MCL, concentrations were highly variable over short distances and at various depths below land surface. Groundwater from wells in the glacial aquifer system at all depths was a mixture of old and young water. Oxidation and reduction potential changes with depth and groundwater age were important influences on nitrate concentrations in private wells. A series of 10 logistic regression models was developed to estimate the probability of nitrate concentration above various thresholds. The threshold concentration (1 to 10 mg/L) affected the number of variables in the model. Fewer explanatory variables are needed to predict nitrate at higher threshold concentrations. The variables that were identified as significant predictors for nitrate concentration above 4 mg/L as N included well characteristics such as open-interval diameter, open-interval length, and depth to top of open interval. Environmental variables in the models were mean percent silt in soil, soil type, and mean depth to saturated soil. The 10-year mean (1992-2001) application rate of nitrogen fertilizer applied to farms was included as the potential source variable. A linear regression model also was developed to predict mean nitrate concentrations in well networks. The model is based on network averages because nitrate concentrations are highly variable over short distances. Using values for each of the predictor variables averaged by network (network mean value) from the logistic regression models, the linear regression model developed in this study predicted the mean nitrate concentration in well networks with a 95 percent confidence in predictions.

Stature estimation from the lengths of the growing foot-a study on North Indian adolescents.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Passi, Neelam; DiMaggio, John A

2012-12-01

Stature estimation is considered as one of the basic parameters of the investigation process in unknown and commingled human remains in medico-legal case work. Race, age and sex are the other parameters which help in this process. Stature estimation is of the utmost importance as it completes the biological profile of a person along with the other three parameters of identification. The present research is intended to formulate standards for stature estimation from foot dimensions in adolescent males from North India and study the pattern of foot growth during the growing years. 154 male adolescents from the Northern part of India were included in the study. Besides stature, five anthropometric measurements that included the length of the foot from each toe (T1, T2, T3, T4, and T5 respectively) to pternion were measured on each foot. The data was analyzed statistically using Student's t-test, Pearson's correlation, linear and multiple regression analysis for estimation of stature and growth of foot during ages 13-18 years. Correlation coefficients between stature and all the foot measurements were found to be highly significant and positively correlated. Linear regression models and multiple regression models (with age as a co-variable) were derived for estimation of stature from the different measurements of the foot. Multiple regression models (with age as a co-variable) estimate stature with greater accuracy than the regression models for 13-18 years age group. The study shows the growth pattern of feet in North Indian adolescents and indicates that anthropometric measurements of the foot and its segments are valuable in estimation of stature in growing individuals of that population. Copyright © 2012 Elsevier Ltd. All rights reserved.

Using Remote Sensing Data to Evaluate Surface Soil Properties in Alabama Ultisols

NASA Technical Reports Server (NTRS)

Sullivan, Dana G.; Shaw, Joey N.; Rickman, Doug; Mask, Paul L.; Luvall, Jeff

2005-01-01

Evaluation of surface soil properties via remote sensing could facilitate soil survey mapping, erosion prediction and allocation of agrochemicals for precision management. The objective of this study was to evaluate the relationship between soil spectral signature and surface soil properties in conventionally managed row crop systems. High-resolution RS data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0-1 cm) were collected from 163 sampling points for soil organic carbon, particle size distribution, and citrate dithionite extractable iron content. Surface roughness, soil water content, and crusting were also measured during sampling. Two methods of analysis were evaluated: 1) multiple linear regression using common spectral band ratios, and 2) partial least squares regression. Our data show that thermal infrared spectra are highly, linearly related to soil organic carbon, sand and clay content. Soil organic carbon content was the most difficult to quantify in these highly weathered systems, where soil organic carbon was generally less than 1.2%. Estimates of sand and clay content were best using partial least squares regression at the Valley site, explaining 42-59% of the variability. In the Coastal Plain, sandy surfaces prone to crusting limited estimates of sand and clay content via partial least squares and regression with common band ratios. Estimates of iron oxide content were a function of mineralogy and best accomplished using specific band ratios, with regression explaining 36-65% of the variability at the Valley and Coastal Plain sites, respectively.

THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE

PubMed Central

Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan

2015-01-01

Background: The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran’s universities. Methods: This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran’s public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For predicting the organizational climate pattern of the libraries is used from the multivariate linear regression and track diagram. Results: of the 9 variables affecting organizational climate, 5 variables of innovation, teamwork, customer service, psychological safety and deep diversity play a major role in prediction of the organizational climate of Iran’s libraries. The results also indicate that each of these variables with different coefficient have the power to predict organizational climate but the climate score of psychological safety (0.94) plays a very crucial role in predicting the organizational climate. Track diagram showed that five variables of teamwork, customer service, psychological safety, deep diversity and innovation directly effects on the organizational climate variable that contribution of the team work from this influence is more than any other variables. Conclusions: Of the indicator of the organizational climate of climateQual, the contribution of the team work from this influence is more than any other variables that reinforcement of teamwork in academic libraries can be more effective in improving the organizational climate of this type libraries. PMID:26622203

THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE.

PubMed

Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan

2015-10-01

The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran's universities. This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran's public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For predicting the organizational climate pattern of the libraries is used from the multivariate linear regression and track diagram. of the 9 variables affecting organizational climate, 5 variables of innovation, teamwork, customer service, psychological safety and deep diversity play a major role in prediction of the organizational climate of Iran's libraries. The results also indicate that each of these variables with different coefficient have the power to predict organizational climate but the climate score of psychological safety (0.94) plays a very crucial role in predicting the organizational climate. Track diagram showed that five variables of teamwork, customer service, psychological safety, deep diversity and innovation directly effects on the organizational climate variable that contribution of the team work from this influence is more than any other variables. Of the indicator of the organizational climate of climateQual, the contribution of the team work from this influence is more than any other variables that reinforcement of teamwork in academic libraries can be more effective in improving the organizational climate of this type libraries.

Human Language Technology: Opportunities and Challenges

DTIC Science & Technology

2005-01-01

because of the connections to and reliance on signal processing. Audio diarization critically includes indexing of speakers [12], since speaker ...to reduce inter- speaker variability in training. Standard techniques include vocal-tract length normalization, adaptation of acoustic models using...maximum likelihood linear regression (MLLR), and speaker -adaptive training based on MLLR. The acoustic models are mixtures of Gaussians, typically with

Civic Purpose in Late Adolescence: Factors That Prevent Decline in Civic Engagement after High School

ERIC Educational Resources Information Center

Malin, Heather; Han, Hyemin; Liauw, Indrawati

2017-01-01

This study investigated the effects of internal and demographic variables on civic development in late adolescence using the construct "civic purpose." We conducted surveys on civic engagement with 480 high school seniors, and surveyed them again 2 years later. Using multivariate regression and linear mixed models, we tested the main…

Thematic Mapper Analysis of Blue Oak (Quercus douglasii) in Central California

Treesearch

Paul A. Lefebvre Jr.; Frank W. Davis; Mark Borchert

1991-01-01

Digital Thematic Mapper (TM) satellite data from September 1986 and December 1985 were analyzed to determine seasonal reflectance properties of blue oak rangeland in the La Panza mountains of San Luis Obispo County. Linear regression analysis was conducted to examine relationships between TM reflectance and oak canopy cover, basal area, and site topographic variables....

Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level

ERIC Educational Resources Information Center

Savalei, Victoria; Rhemtulla, Mijke

2017-01-01

In many modeling contexts, the variables in the model are linear composites of the raw items measured for each participant; for instance, regression and path analysis models rely on scale scores, and structural equation models often use parcels as indicators of latent constructs. Currently, no analytic estimation method exists to appropriately…

Modeling Outcomes with Floor or Ceiling Effects: An Introduction to the Tobit Model

ERIC Educational Resources Information Center

McBee, Matthew

2010-01-01

In gifted education research, it is common for outcome variables to exhibit strong floor or ceiling effects due to insufficient range of measurement of many instruments when used with gifted populations. Common statistical methods (e.g., analysis of variance, linear regression) produce biased estimates when such effects are present. In practice,…

Ecological and Topographic Features of Volcanic Ash-Influenced Forest Soils

Treesearch

Mark Kimsey; Brian Gardner; Alan Busacca

2007-01-01

Volcanic ash distribution and thickness were determined for a forested region of north-central Idaho. Mean ash thickness and multiple linear regression analyses were used to model the effect of environmental variables on ash thickness. Slope and slope curvature relationships with volcanic ash thickness varied on a local spatial scale across the study area. Ash...

Examining the Variability of Mathematics Performance and Its Correlates Using Data from TIMSS '95 and TIMSS '99

ERIC Educational Resources Information Center

O'Dwyer, Laura M.

2005-01-01

International studies in education provide researchers with opportunities to examine how students with both similar and dissimilar formal education systems perform on a single test and provide rich information about the relationships among student outcomes and the factors that affect them. Using hierarchical linear regression techniques and TIMSS…

Background stratified Poisson regression analysis of cohort data.

PubMed

Richardson, David B; Langholz, Bryan

2012-03-01

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

Controls on the variability of net infiltration to desert sandstone

USGS Publications Warehouse

Heilweil, Victor M.; McKinney, Tim S.; Zhdanov, Michael S.; Watt, Dennis E.

2007-01-01

As populations grow in arid climates and desert bedrock aquifers are increasingly targeted for future development, understanding and quantifying the spatial variability of net infiltration becomes critically important for accurately inventorying water resources and mapping contamination vulnerability. This paper presents a conceptual model of net infiltration to desert sandstone and then develops an empirical equation for its spatial quantification at the watershed scale using linear least squares inversion methods for evaluating controlling parameters (independent variables) based on estimated net infiltration rates (dependent variables). Net infiltration rates used for this regression analysis were calculated from environmental tracers in boreholes and more than 3000 linear meters of vadose zone excavations in an upland basin in southwestern Utah underlain by Navajo sandstone. Soil coarseness, distance to upgradient outcrop, and topographic slope were shown to be the primary physical parameters controlling the spatial variability of net infiltration. Although the method should be transferable to other desert sandstone settings for determining the relative spatial distribution of net infiltration, further study is needed to evaluate the effects of other potential parameters such as slope aspect, outcrop parameters, and climate on absolute net infiltration rates.

Improving near-infrared prediction model robustness with support vector machine regression: a pharmaceutical tablet assay example.

PubMed

Igne, Benoît; Drennen, James K; Anderson, Carl A

2014-01-01

Changes in raw materials and process wear and tear can have significant effects on the prediction error of near-infrared calibration models. When the variability that is present during routine manufacturing is not included in the calibration, test, and validation sets, the long-term performance and robustness of the model will be limited. Nonlinearity is a major source of interference. In near-infrared spectroscopy, nonlinearity can arise from light path-length differences that can come from differences in particle size or density. The usefulness of support vector machine (SVM) regression to handle nonlinearity and improve the robustness of calibration models in scenarios where the calibration set did not include all the variability present in test was evaluated. Compared to partial least squares (PLS) regression, SVM regression was less affected by physical (particle size) and chemical (moisture) differences. The linearity of the SVM predicted values was also improved. Nevertheless, although visualization and interpretation tools have been developed to enhance the usability of SVM-based methods, work is yet to be done to provide chemometricians in the pharmaceutical industry with a regression method that can supplement PLS-based methods.

Climate change but not unemployment explains the changing suicidality in Thessaloniki Greece (2000-2012).

PubMed

Fountoulakis, Konstantinos N; Savopoulos, Christos; Zannis, Prodromos; Apostolopoulou, Martha; Fountoukidis, Ilias; Kakaletsis, Nikolaos; Kanellos, Ilias; Dimellis, Dimos; Hyphantis, Thomas; Tsikerdekis, Athanasios; Pompili, Maurizio; Hatzitolios, Apostolos I

2016-03-15

Recently there was a debate concerning the etiology behind attempts and completed suicides. The aim of the current study was to search for possible correlations between the rates of attempted and completed suicide and climate variables and regional unemployment per year in the county of Thessaloniki, Macedonia, northern Greece, for the years 2000-12. The regional rates of suicide and attempted suicide as well as regional unemployment were available from previous publications of the authors. The climate variables were calculated from the daily E-OBS gridded dataset which is based on observational data Only the male suicide rates correlate significantly with high mean annual temperature but not with unemployment. The multiple linear regression analysis results suggest that temperature is the only variable that determines male suicides and explains 51% of their variance. Unemployment fails to contribute significantly to the model. There seems to be a seasonal distribution for attempts with mean rates being higher for the period from May to October and the rates clearly correlate with temperature. The highest mean rates were observed during May and August and the lowest during December and February. Multiple linear regression analysis suggests that temperature also determines the female attempts rate although the explained variable is significant but very low (3-5%) Climate variables and specifically high temperature correlate both with suicide and attempted suicide rates but with a different way between males and females. The climate effect was stronger than the effect of unemployment. Copyright © 2016 Elsevier B.V. All rights reserved.

Refractive Status at Birth: Its Relation to Newborn Physical Parameters at Birth and Gestational Age

PubMed Central

Varghese, Raji Mathew; Sreenivas, Vishnubhatla; Puliyel, Jacob Mammen; Varughese, Sara

2009-01-01

Background Refractive status at birth is related to gestational age. Preterm babies have myopia which decreases as gestational age increases and term babies are known to be hypermetropic. This study looked at the correlation of refractive status with birth weight in term and preterm babies, and with physical indicators of intra-uterine growth such as the head circumference and length of the baby at birth. Methods All babies delivered at St. Stephens Hospital and admitted in the nursery were eligible for the study. Refraction was performed within the first week of life. 0.8% tropicamide with 0.5% phenylephrine was used to achieve cycloplegia and paralysis of accommodation. 599 newborn babies participated in the study. Data pertaining to the right eye is utilized for all the analyses except that for anisometropia where the two eyes were compared. Growth parameters were measured soon after birth. Simple linear regression analysis was performed to see the association of refractive status, (mean spherical equivalent (MSE), astigmatism and anisometropia) with each of the study variables, namely gestation, length, weight and head circumference. Subsequently, multiple linear regression was carried out to identify the independent predictors for each of the outcome parameters. Results Simple linear regression showed a significant relation between all 4 study variables and refractive error but in multiple regression only gestational age and weight were related to refractive error. The partial correlation of weight with MSE adjusted for gestation was 0.28 and that of gestation with MSE adjusted for weight was 0.10. Birth weight had a higher correlation to MSE than gestational age. Conclusion This is the first study to look at refractive error against all these growth parameters, in preterm and term babies at birth. It would appear from this study that birth weight rather than gestation should be used as criteria for screening for refractive error, especially in developing countries where the incidence of intrauterine malnutrition is higher. PMID:19214228

Assessing the risk of bovine fasciolosis using linear regression analysis for the state of Rio Grande do Sul, Brazil.

PubMed

Silva, Ana Elisa Pereira; Freitas, Corina da Costa; Dutra, Luciano Vieira; Molento, Marcelo Beltrão

2016-02-15

Fasciola hepatica is the causative agent of fasciolosis, a disease that triggers a chronic inflammatory process in the liver affecting mainly ruminants and other animals including humans. In Brazil, F. hepatica occurs in larger numbers in the most Southern state of Rio Grande do Sul. The objective of this study was to estimate areas at risk using an eight-year (2002-2010) time series of climatic and environmental variables that best relate to the disease using a linear regression method to municipalities in the state of Rio Grande do Sul. The positivity index of the disease, which is the rate of infected animal per slaughtered animal, was divided into three risk classes: low, medium and high. The accuracy of the known sample classification on the confusion matrix for the low, medium and high rates produced by the estimated model presented values between 39 and 88% depending of the year. The regression analysis showed the importance of the time-based data for the construction of the model, considering the two variables of the previous year of the event (positivity index and maximum temperature). The generated data is important for epidemiological and parasite control studies mainly because F. hepatica is an infection that can last from months to years. Copyright © 2015 Elsevier B.V. All rights reserved.

Prediction of erodibility in Oxisols using iron oxides, soil color and diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Arantes Camargo, Livia; Marques, José, Jr.

2015-04-01

The prediction of erodibility using indirect methods such as diffuse reflectance spectroscopy could facilitate the characterization of the spatial variability in large areas and optimize implementation of conservation practices. The aim of this study was to evaluate the prediction of interrill erodibility (Ki) and rill erodibility (Kr) by means of iron oxides content and soil color using multiple linear regression and diffuse reflectance spectroscopy (DRS) using regression analysis by least squares partial (PLSR). The soils were collected from three geomorphic surfaces and analyzed for chemical, physical and mineralogical properties, plus scanned in the spectral range from the visible and infrared. Maps of spatial distribution of Ki and Kr were built with the values calculated by the calibrated models that obtained the best accuracy using geostatistics. Interrill-rill erodibility presented negative correlation with iron extracted by dithionite-citrate-bicarbonate, hematite, and chroma, confirming the influence of iron oxides in soil structural stability. Hematite and hue were the attributes that most contributed in calibration models by multiple linear regression for the prediction of Ki (R2 = 0.55) and Kr (R2 = 0.53). The diffuse reflectance spectroscopy via PLSR allowed to predict Interrill-rill erodibility with high accuracy (R2adj = 0.76, 0.81 respectively and RPD> 2.0) in the range of the visible spectrum (380-800 nm) and the characterization of the spatial variability of these attributes by geostatistics.

Prevalence and correlates of cognitive impairment in kidney transplant recipients.

PubMed

Gupta, Aditi; Mahnken, Jonathan D; Johnson, David K; Thomas, Tashra S; Subramaniam, Dipti; Polshak, Tyler; Gani, Imran; John Chen, G; Burns, Jeffrey M; Sarnak, Mark J

2017-05-12

There is a high prevalence of cognitive impairment in dialysis patients. The prevalence of cognitive impairment after kidney transplantation is unknown. Study Design: Cross-sectional study. Single center study of prevalent kidney transplant recipients from a transplant clinic in a large academic center. Assessment of cognition using the Montreal Cognitive Assessment (MoCA). Demographic and clinical variables associated with cognitive impairment were also examined. Outcomes and Measurements: a) Prevalence of cognitive impairment defined by a MoCA score of <26. b) Multivariable linear and logistic regression to examine the association of demographic and clinical factors with cognitive impairment. Data from 226 patients were analyzed. Mean (SD) age was 54 (13.4) years, 73% were white, 60% were male, 37% had diabetes, 58% had an education level of college or above, and the mean (SD) time since kidney transplant was 3.4 (4.1) years. The prevalence of cognitive impairment was 58.0%. Multivariable linear regression demonstrated that older age, male gender and absence of diabetes were associated with lower MoCA scores (p < 0.01 for all). Estimated glomerular filtration rate (eGFR) was not associated with level of cognition. The logistic regression analysis confirmed the association of older age with cognitive impairment. Cognitive impairment is common in prevalent kidney transplant recipients, at a younger age compared to general population, and is associated with certain demographic variables, but not level of eGFR.

Personality as a Source of Individual Differences in Cognition among Older African Americans

PubMed Central

Aiken-Morgan, Adrienne T.; Bichsel, Jacqueline; Allaire, Jason C.; Savla, Jyoti; Edwards, Christopher L.; Whitfield, Keith E.

2012-01-01

Previous research suggests that demographic factors are important correlates of cognitive functioning in African Americans; however, less attention has been given to the influence of personality. The present study explored how dimensions and facets of personality predicted individual variability in cognition in a sample of older African Americans from the Baltimore Study of Black Aging. Cognition was assessed by verbal learning and attention/working memory measures. Personality was measured by the NEO Personality Inventory. Linear regressions controlling for demographic factors showed that Neuroticism, Openness, and Agreeableness were significant regression predictors of cognitive performance. Individual facets of all five personality dimensions were also associated with cognitive performance. These findings suggest personality is important in understanding variability in cognition among older African Americans. PMID:22962505

Advanced glycation end products and antioxidant status in type 2 diabetic patients with and without peripheral artery disease.

PubMed

Lapolla, Annunziata; Piarulli, Francesco; Sartore, Giovanni; Ceriello, Antonio; Ragazzi, Eugenio; Reitano, Rachele; Baccarin, Lorenzo; Laverda, Barbara; Fedele, Domenico

2007-03-01

Advanced glycation end products (AGEs), pentosidine and malondialdehyde (MDA), are elevated in type 2 diabetic subjects with coronary and carotid angiopathy. We investigated the relationship of AGEs, MDA, total reactive antioxidant potentials (TRAPs), and vitamin E in type 2 diabetic patients with and without peripheral artery disease (PAD). AGEs, pentosidine, MDA, TRAP, vitamin E, and ankle-brachial index (ABI) were measured in 99 consecutive type 2 diabetic subjects and 20 control subjects. AGEs, pentosidine, and MDA were higher and vitamin E and TRAP were lower in patients with PAD (ABI <0.9) than in patients without PAD (ABI >0.9) (P < 0.001). After multiple regression analysis, a correlation between AGEs and pentosidine, as independent variables, and ABI, as the dependent variable, was found in both patients with and without PAD (r = 0.9198, P < 0.001 and r = 0.5764, P < 0.001, respectively) but not in control subjects. When individual regression coefficients were evaluated, only that due to pentosidine was confirmed as significant. For patients with PAD, considering TRAP, vitamin E, and MDA as independent variables and ABI as the dependent variable produced an overall significant regression (r = 0.6913, P < 0.001). The regression coefficients for TRAP and vitamin E were not significant, indicating that the model is best explained by a single linear regression between MDA and ABI. These findings were also confirmed by principal component analysis. Results show that pentosidine and MDA are strongly associated with PAD in type 2 diabetic patients.

Regression modeling plan for 29 biochemical indicators of diet and nutrition measured in NHANES 2003-2006.

PubMed

Sternberg, Maya R; Schleicher, Rosemary L; Pfeiffer, Christine M

2013-06-01

The collection of articles in this supplement issue provides insight into the association of various covariates with concentrations of biochemical indicators of diet and nutrition (biomarkers), beyond age, race, and sex, using linear regression. We studied 10 specific sociodemographic and lifestyle covariates in combination with 29 biomarkers from NHANES 2003-2006 for persons aged ≥ 20 y. The covariates were organized into 2 sets or "chunks": sociodemographic (age, sex, race-ethnicity, education, and income) and lifestyle (dietary supplement use, smoking, alcohol consumption, BMI, and physical activity) and fit in hierarchical fashion by using each category or set of related variables to determine how covariates, jointly, are related to biomarker concentrations. In contrast to many regression modeling applications, all variables were retained in a full regression model regardless of significance to preserve the interpretation of the statistical properties of β coefficients, P values, and CIs and to keep the interpretation consistent across a set of biomarkers. The variables were preselected before data analysis, and the data analysis plan was designed at the outset to minimize the reporting of false-positive findings by limiting the amount of preliminary hypothesis testing. Although we generally found that demographic differences seen in biomarkers were over- or underestimated when ignoring other key covariates, the demographic differences generally remained significant after adjusting for sociodemographic and lifestyle variables. These articles are intended to provide a foundation to researchers to help them generate hypotheses for future studies or data analyses and/or develop predictive regression models using the wealth of NHANES data.

wayGoo recommender system: personalized recommendations for events scheduling, based on static and real-time information

NASA Astrophysics Data System (ADS)

Thanos, Konstantinos-Georgios; Thomopoulos, Stelios C. A.

2016-05-01

wayGoo is a fully functional application whose main functionalities include content geolocation, event scheduling, and indoor navigation. However, significant information about events do not reach users' attention, either because of the size of this information or because some information comes from real - time data sources. The purpose of this work is to facilitate event management operations by prioritizing the presented events, based on users' interests using both, static and real - time data. Through the wayGoo interface, users select conceptual topics that are interesting for them. These topics constitute a browsing behavior vector which is used for learning users' interests implicitly, without being intrusive. Then, the system estimates user preferences and return an events list sorted from the most preferred one to the least. User preferences are modeled via a Naïve Bayesian Network which consists of: a) the `decision' random variable corresponding to users' decision on attending an event, b) the `distance' random variable, modeled by a linear regression that estimates the probability that the distance between a user and each event destination is not discouraging, ` the seat availability' random variable, modeled by a linear regression, which estimates the probability that the seat availability is encouraging d) and the `relevance' random variable, modeled by a clustering - based collaborative filtering, which determines the relevance of each event users' interests. Finally, experimental results show that the proposed system contribute essentially to assisting users in browsing and selecting events to attend.

Sources of variability in satellite-derived estimates of phytoplankton production in the eastern tropical Pacific

NASA Technical Reports Server (NTRS)

Banse, Karl; Yong, Marina

1990-01-01

As a proxy for satellite CZCS observations and concurrent measurements of primary production rates, data from 138 stations occupied seasonally during 1967-1968 in the offshore eastern tropical Pacific were analyzed in terms of six temporal groups and our current regimes. Multiple linear regressions on column production Pt show that simulated satellite pigment is generally weakly correlated, but sometimes not correlated with Pt, and that incident irradiance, sea surface temperature, nitrate, transparency, and depths of mixed layer or nitracline assume little or no importance. After a proxy for the light-saturated chlorophyll-specific photosynthetic rate P(max) is added, the coefficient of determination ranges from 0.55 to 0.91 (median of 0.85) for the 10 cases. In stepwise multiple linear regressions the P(max) proxy is the best predictor for Pt.

Using Simulation Technique to overcome the multi-collinearity problem for estimating fuzzy linear regression parameters.

NASA Astrophysics Data System (ADS)

Mansoor Gorgees, Hazim; Hilal, Mariam Mohammed

2018-05-01

Fatigue cracking is one of the common types of pavement distresses and is an indicator of structural failure; cracks allow moisture infiltration, roughness, may further deteriorate to a pothole. Some causes of pavement deterioration are: traffic loading; environment influences; drainage deficiencies; materials quality problems; construction deficiencies and external contributors. Many researchers have made models that contain many variables like asphalt content, asphalt viscosity, fatigue life, stiffness of asphalt mixture, temperature and other parameters that affect the fatigue life. For this situation, a fuzzy linear regression model was employed and analyzed by using the traditional methods and our proposed method in order to overcome the multi-collinearity problem. The total spread error was used as a criterion to compare the performance of the studied methods. Simulation program was used to obtain the required results.

Predonation Volume of Future Remnant Cortical Kidney Helps Predict Postdonation Renal Function in Live Kidney Donors.

PubMed

Fananapazir, Ghaneh; Benzl, Robert; Corwin, Michael T; Chen, Ling-Xin; Sageshima, Junichiro; Stewart, Susan L; Troppmann, Christoph

2018-07-01

Purpose To determine whether the predonation computed tomography (CT)-based volume of the future remnant kidney is predictive of postdonation renal function in living kidney donors. Materials and Methods This institutional review board-approved, retrospective, HIPAA-compliant study included 126 live kidney donors who had undergone predonation renal CT between January 2007 and December 2014 as well as 2-year postdonation measurement of estimated glomerular filtration rate (eGFR). The whole kidney volume and cortical volume of the future remnant kidney were measured and standardized for body surface area (BSA). Bivariate linear associations between the ratios of whole kidney volume to BSA and cortical volume to BSA were obtained. A linear regression model for 2-year postdonation eGFR that incorporated donor age, sex, and either whole kidney volume-to-BSA ratio or cortical volume-to-BSA ratio was created, and the coefficient of determination (R 2 ) for the model was calculated. Factors not statistically additive in assessing 2-year eGFR were removed by using backward elimination, and the coefficient of determination for this parsimonious model was calculated. Results Correlation was slightly better for cortical volume-to-BSA ratio than for whole kidney volume-to-BSA ratio (r = 0.48 vs r = 0.44, respectively). The linear regression model incorporating all donor factors had an R 2 of 0.66. The only factors that were significantly additive to the equation were cortical volume-to-BSA ratio and predonation eGFR (P = .01 and P < .01, respectively), and the final parsimonious linear regression model incorporating these two variables explained almost the same amount of variance (R 2 = 0.65) as did the full model. Conclusion The cortical volume of the future remnant kidney helped predict postdonation eGFR at 2 years. The cortical volume-to-BSA ratio should thus be considered for addition as an important variable to living kidney donor evaluation and selection guidelines. © RSNA, 2018.

Pulp Sensitivity: Influence of Sex, Psychosocial Variables, COMT Gene, and Chronic Facial Pain.

PubMed

Mladenovic, Irena; Krunic, Jelena; Supic, Gordana; Kozomara, Ruzica; Bokonjic, Dejan; Stojanovic, Nikola; Magic, Zvonko

2018-05-01

The purpose of this study was to evaluate the associations of variability in pulp sensitivity with sex, psychosocial variables, the gene that encodes for the enzyme catechol-O-methyltransferase (COMT), and chronic painful conditions (temporomandibular disorders [TMDs]). The study was composed of 97 subjects (68 women and 29 men aged 20-44 years). The electric (electric pulp tester) and cold (refrigerant spray) stimuli were performed on mandibular lateral incisors. The results were expressed as pain threshold values for electric pulp stimulation (0-80 units) and as pain intensity scores (visual numeric scale from 0-10) for cold stimulation. The Research Diagnostic Criteria for TMD were used to assess TMD, depression, and somatization. DNA extracted from peripheral blood was genotyped for 3 COMT polymorphisms (rs4680, rs6269, and rs165774) using the real-time TaqMan method. Multivariate linear regression was used to investigate the joint effect of the predictor variables (clinical and genetic) on pulp sensitivity (dependent variables). Threshold responses to electric stimuli were related to female sex (P < .01) and the homozygous GG genotype for the rs165774 polymorphism (P < .05). Pain intensity to cold stimuli was higher in TMD patients (P < .01) and tended to be higher in women. Multivariate linear regression identified sex and the rs165774 COMT polymorphism as the determinants of electric pain sensitivity, whereas TMD accounts for the variability in the cold response. Our findings indicate that sex/a COMT gene variant and TMD as a chronic painful condition may contribute to individual variation in electric and cold pulp sensitivity, respectively. Copyright © 2018 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

An Analysis of San Diego's Housing Market Using a Geographically Weighted Regression Approach

NASA Astrophysics Data System (ADS)

Grant, Christina P.

San Diego County real estate transaction data was evaluated with a set of linear models calibrated by ordinary least squares and geographically weighted regression (GWR). The goal of the analysis was to determine whether the spatial effects assumed to be in the data are best studied globally with no spatial terms, globally with a fixed effects submarket variable, or locally with GWR. 18,050 single-family residential sales which closed in the six months between April 2014 and September 2014 were used in the analysis. Diagnostic statistics including AICc, R2, Global Moran's I, and visual inspection of diagnostic plots and maps indicate superior model performance by GWR as compared to both global regressions.

Tympanic ear thermometer assessment of body temperature among patients with cognitive disturbances. An acceptable and ethically desirable alternative?

PubMed

Aadal, Lena; Fog, Lisbet; Pedersen, Asger Roer

2016-12-01

Investigation of a possible relation between body temperature measurements by the current generation of tympanic ear and rectal thermometers. In Denmark, a national guideline recommends the rectal measurement. Subsequently, the rectal thermometers and tympanic ear devices are the most frequently used and first choice in Danish hospital wards. Cognitive changes constitute challenges with cooperating in rectal temperature assessments. With regard to diagnosing, ethics, safety and the patients' dignity, the tympanic ear thermometer might comprise a desirable alternative to rectal noninvasive measurement of body temperature during in-hospital-based neurorehabilitation. A prospective, descriptive cohort study. Consecutive inclusion of 27 patients. Linear regression models were used to analyse 284 simultaneous temperature measurements. Ethical approval for this study was granted by the Danish Data Protection Agency, and the study was completed in accordance with the Helsinki Declaration 2008. About 284 simultaneous rectal and ear temperature measurements on 27 patients were analysed. The patient-wise variability of measured temperatures was significantly higher for the ear measurements. Patient-wise linear regressions for the 25 patients with at least three pairs of simultaneous ear and rectal temperature measurements showed large interpatient variability of the association. A linear relationship between the rectal body temperature assessment and the temperature assessment employing the tympanic thermometer is weak. Both measuring methods reflect variance in temperature, but ear measurements showed larger variation. © 2016 Nordic College of Caring Science.

The role of chemometrics in single and sequential extraction assays: a review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques.

PubMed

Giacomino, Agnese; Abollino, Ornella; Malandrino, Mery; Mentasti, Edoardo

2011-03-04

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied. Copyright © 2010 Elsevier B.V. All rights reserved.

Evaluation of job satisfaction and working atmosphere of dental nurses in Germany.

PubMed

Goetz, Katja; Hasse, Philipp; Campbell, Stephen M; Berger, Sarah; Dörfer, Christof E; Hahn, Karolin; Szecsenyi, Joachim

2016-02-01

The purpose of the study was to assess the level of job satisfaction of dental nurses in ambulatory care and to explore the impact of aspects of working atmosphere on and their association with job satisfaction. This cross-sectional study was based on a job satisfaction survey. Data were collected from 612 dental nurses working in 106 dental care practices. Job satisfaction was measured with the 10-item Warr-Cook-Wall job satisfaction scale. Working atmosphere was measured with five items. Linear regression analyses were performed in which each item of the job satisfaction scale was handled as dependent variables. A stepwise linear regression analysis was performed with overall job satisfaction and the five items of working atmosphere, job satisfaction, and individual characteristics. The response rate was 88.3%. Dental nurses were satisfied with 'colleagues' and least satisfied with 'income.' Different aspects of job satisfaction were mostly associated with the following working atmosphere issues: 'responsibilities within the practice team are clear,' 'suggestions for improvement are taken seriously,' 'working atmosphere in the practice team is good,' and 'made easier to admit own mistakes.' Within the stepwise linear regression analysis, the aspect 'physical working condition' (β = 0.304) showed the highest association with overall job satisfaction. The total explained variance of the 14 associated variables was 0.722 with overall job satisfaction. Working atmosphere within this discrete sample of dental care practice seemed to be an important influence on reported working condition and job satisfaction for dental nurses. Because of the high association of job satisfaction with physical working condition, the importance of paying more attention to an ergonomic working position for dental nurses to ensure optimal quality of care is highlighted. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

[Bioacoustic of the advertisement call of Ceratophrys cranwelli (Anura: Ceratophryidae)].

PubMed

Valetti, Julián Alonso; Salas, Nancy Edith; Martino, Adolfo Ludovico

2013-03-01

The advertisement call plays an important role in the life history of anuran amphibians, mainly during the breeding season. Call features represent an important character to discriminate species, and sound emissions are very effective to assure or reinforce genetic incompatibility, especially in the case of sibling species. Since frogs are ectotherms, acoustic properties of their calls will vary with temperature. In this study, we described the advertisement call of C. cranwelli, quantifying the temperature effect on its components. The acoustic emissions were recorded during 2007 using a DAT record Sony TCD-100 with stereo microphone ECM-MS907 Sony and tape TDK DAT-RGX 60. As males emit their calls floating in temporary ponds, water temperatures were registered after recording the advertisement calls with a digital thermometer TES 1300+/-0.1 degreeC. Altogether, 54 calls from 18 males were analyzed. The temporal variables of each advertisement call were measured using oscillograms and sonograms and the analyses of dominant frequency were performed using a spectrogram. Multiple correlation analysis was used to identify the temperature-dependent acoustic variables and the temperature effect on these variables was quantified using linear regression models. The advertisement call of C. cranwelli consists of a single pulse group. Call duration, Pulse duration and Pulse interval decreased with the temperature, whereas the Pulse rate increased with temperature. The temperature-dependent variables were standardized at 25 degreeC according to the linear regression model obtained. The acoustic variables that were correlated with the temperature are the variables which emissions depend on laryngeal muscles and the temperature constraints the contractile properties of muscles. Our results indicated that temperature explains an important fraction of the variability in some acoustic variables (79% in the Pulse rate), and demonstrated the importance of considering the effect of temperature in acoustic components. The results suggest that acoustic variables show geographic variation to compare data with previous works.

What is the relationship between renal function and visit-to-visit blood pressure variability in primary care? Retrospective cohort study from routinely collected healthcare data.

PubMed

Lasserson, Daniel S; Scherpbier de Haan, Nynke; de Grauw, Wim; van der Wel, Mark; Wetzels, Jack F; O'Callaghan, Christopher A

2016-06-10

To determine the relationship between renal function and visit-to-visit blood pressure (BP) variability in a cohort of primary care patients. Retrospective cohort study from routinely collected healthcare data. Primary care in Nijmegen, the Netherlands, from 2007 to 2012. 19 175 patients who had a measure of renal function, and 7 separate visits with BP readings in the primary care record. Visit-to-visit variability in systolic BP, calculated from the first 7 office measurements, including SD, successive variation, absolute real variation and metrics of variability shown to be independent of mean. Multiple linear regression was used to analyse the influence of estimated glomerular filtration rate (eGFR) on BP variability measures with adjustment for age, sex, diabetes, mean BP, proteinuria, cardiovascular disease, time interval between measures and antihypertensive use. In the patient cohort, 57% were women, mean (SD) age was 65.5 (12.3) years, mean (SD) eGFR was 75.6 (18.0) mL/min/1.73m(2) and SD systolic BP 148.3 (21.4) mm Hg. All BP variability measures were negatively correlated with eGFR and positively correlated with age. However, multiple linear regressions demonstrated consistent, small magnitude negative relationships between eGFR and all measures of BP variability adjusting for confounding variables. Worsening renal function is associated with small increases in measures of visit-to-visit BP variability after adjustment for confounding factors. This is seen across the spectrum of renal function in the population, and provides a mechanism whereby chronic kidney disease may raise the risk of cardiovascular events. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

A comparison of model-based imputation methods for handling missing predictor values in a linear regression model: A simulation study

NASA Astrophysics Data System (ADS)

Hasan, Haliza; Ahmad, Sanizah; Osman, Balkish Mohd; Sapri, Shamsiah; Othman, Nadirah

2017-08-01

In regression analysis, missing covariate data has been a common problem. Many researchers use ad hoc methods to overcome this problem due to the ease of implementation. However, these methods require assumptions about the data that rarely hold in practice. Model-based methods such as Maximum Likelihood (ML) using the expectation maximization (EM) algorithm and Multiple Imputation (MI) are more promising when dealing with difficulties caused by missing data. Then again, inappropriate methods of missing value imputation can lead to serious bias that severely affects the parameter estimates. The main objective of this study is to provide a better understanding regarding missing data concept that can assist the researcher to select the appropriate missing data imputation methods. A simulation study was performed to assess the effects of different missing data techniques on the performance of a regression model. The covariate data were generated using an underlying multivariate normal distribution and the dependent variable was generated as a combination of explanatory variables. Missing values in covariate were simulated using a mechanism called missing at random (MAR). Four levels of missingness (10%, 20%, 30% and 40%) were imposed. ML and MI techniques available within SAS software were investigated. A linear regression analysis was fitted and the model performance measures; MSE, and R-Squared were obtained. Results of the analysis showed that MI is superior in handling missing data with highest R-Squared and lowest MSE when percent of missingness is less than 30%. Both methods are unable to handle larger than 30% level of missingness.

Linear models: permutation methods

USGS Publications Warehouse

Cade, B.S.; Everitt, B.S.; Howell, D.C.

2005-01-01

Permutation tests (see Permutation Based Inference) for the linear model have applications in behavioral studies when traditional parametric assumptions about the error term in a linear model are not tenable. Improved validity of Type I error rates can be achieved with properly constructed permutation tests. Perhaps more importantly, increased statistical power, improved robustness to effects of outliers, and detection of alternative distributional differences can be achieved by coupling permutation inference with alternative linear model estimators. For example, it is well-known that estimates of the mean in linear model are extremely sensitive to even a single outlying value of the dependent variable compared to estimates of the median [7, 19]. Traditionally, linear modeling focused on estimating changes in the center of distributions (means or medians). However, quantile regression allows distributional changes to be estimated in all or any selected part of a distribution or responses, providing a more complete statistical picture that has relevance to many biological questions [6]...

Principal components analysis in clinical studies.

PubMed

Zhang, Zhongheng; Castelló, Adela

2017-09-01

In multivariate analysis, independent variables are usually correlated to each other which can introduce multicollinearity in the regression models. One approach to solve this problem is to apply principal components analysis (PCA) over these variables. This method uses orthogonal transformation to represent sets of potentially correlated variables with principal components (PC) that are linearly uncorrelated. PCs are ordered so that the first PC has the largest possible variance and only some components are selected to represent the correlated variables. As a result, the dimension of the variable space is reduced. This tutorial illustrates how to perform PCA in R environment, the example is a simulated dataset in which two PCs are responsible for the majority of the variance in the data. Furthermore, the visualization of PCA is highlighted.

The use of auxiliary variables in capture-recapture and removal experiments

USGS Publications Warehouse

Pollock, K.H.; Hines, J.E.; Nichols, J.D.

1984-01-01

The dependence of animal capture probabilities on auxiliary variables is an important practical problem which has not been considered in the development of estimation procedures for capture-recapture and removal experiments. In this paper the linear logistic binary regression model is used to relate the probability of capture to continuous auxiliary variables. The auxiliary variables could be environmental quantities such as air or water temperature, or characteristics of individual animals, such as body length or weight. Maximum likelihood estimators of the population parameters are considered for a variety of models which all assume a closed population. Testing between models is also considered. The models can also be used when one auxiliary variable is a measure of the effort expended in obtaining the sample.

Modeled summer background concentration nutrients and ...

EPA Pesticide Factsheets

We used regression models to predict background concentration of four water quality indictors: total nitrogen (N), total phosphorus (P), chloride, and total suspended solids (TSS), in the mid-continent (USA) great rivers, the Upper Mississippi, the Lower Missouri, and the Ohio. From best-model linear regressions of water quality indicators with land use and other stressor variables, we determined the concentration of the indicators when the land use and stressor variables were all set to zero the y-intercept. Except for total P on the Upper Mississippi River and chloride on the Ohio River, we were able to predict background concentration from significant regression models. In every model with more than one predictor variable, the model included at least one variable representing agricultural land use and one variable representing development. Predicted background concentration of total N was the same on the Upper Mississippi and Lower Missouri rivers (350 ug l-1), which was much lower than a published eutrophication threshold and percentile-based thresholds (25th percentile of concentration at all sites in the population) but was similar to a threshold derived from the response of sestonic chlorophyll a to great river total N concentration. Background concentration of total P on the Lower Missouri (53 ug l-1) was also lower than published and percentile-based thresholds. Background TSS concentration was higher on the Lower Missouri (30 mg l-1) than the other ri

Relationship between body composition and postural control in prepubertal overweight/obese children: A cross-sectional study.

PubMed

Villarrasa-Sapiña, Israel; Álvarez-Pitti, Julio; Cabeza-Ruiz, Ruth; Redón, Pau; Lurbe, Empar; García-Massó, Xavier

2018-02-01

Excess body weight during childhood causes reduced motor functionality and problems in postural control, a negative influence which has been reported in the literature. Nevertheless, no information regarding the effect of body composition on the postural control of overweight and obese children is available. The objective of this study was therefore to establish these relationships. A cross-sectional design was used to establish relationships between body composition and postural control variables obtained in bipedal eyes-open and eyes-closed conditions in twenty-two children. Centre of pressure signals were analysed in the temporal and frequency domains. Pearson correlations were applied to establish relationships between variables. Principal component analysis was applied to the body composition variables to avoid potential multicollinearity in the regression models. These principal components were used to perform a multiple linear regression analysis, from which regression models were obtained to predict postural control. Height and leg mass were the body composition variables that showed the highest correlation with postural control. Multiple regression models were also obtained and several of these models showed a higher correlation coefficient in predicting postural control than simple correlations. These models revealed that leg and trunk mass were good predictors of postural control. More equations were found in the eyes-open than eyes-closed condition. Body weight and height are negatively correlated with postural control. However, leg and trunk mass are better postural control predictors than arm or body mass. Finally, body composition variables are more useful in predicting postural control when the eyes are open. Copyright © 2017 Elsevier Ltd. All rights reserved.

Drivers and potential predictability of summer time North Atlantic polar front jet variability

NASA Astrophysics Data System (ADS)

Hall, Richard J.; Jones, Julie M.; Hanna, Edward; Scaife, Adam A.; Erdélyi, Róbert

2017-06-01

The variability of the North Atlantic polar front jet stream is crucial in determining summer weather around the North Atlantic basin. Recent extreme summers in western Europe and North America have highlighted the need for greater understanding of this variability, in order to aid seasonal forecasting and mitigate societal, environmental and economic impacts. Here we find that simple linear regression and composite models based on a few predictable factors are able to explain up to 35 % of summertime jet stream speed and latitude variability from 1955 onwards. Sea surface temperature forcings impact predominantly on jet speed, whereas solar and cryospheric forcings appear to influence jet latitude. The cryospheric associations come from the previous autumn, suggesting the survival of an ice-induced signal through the winter season, whereas solar influences lead jet variability by a few years. Regression models covering the earlier part of the twentieth century are much less effective, presumably due to decreased availability of data, and increased uncertainty in observational reanalyses. Wavelet coherence analysis identifies that associations fluctuate over the study period but it is not clear whether this is just internal variability or genuine non-stationarity. Finally we identify areas for future research.

A simple approach to power and sample size calculations in logistic regression and Cox regression models.

PubMed

Vaeth, Michael; Skovlund, Eva

2004-06-15

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.

Estimating energy expenditure from heart rate in older adults: a case for calibration.

PubMed

Schrack, Jennifer A; Zipunnikov, Vadim; Goldsmith, Jeff; Bandeen-Roche, Karen; Crainiceanu, Ciprian M; Ferrucci, Luigi

2014-01-01

Accurate measurement of free-living energy expenditure is vital to understanding changes in energy metabolism with aging. The efficacy of heart rate as a surrogate for energy expenditure is rooted in the assumption of a linear function between heart rate and energy expenditure, but its validity and reliability in older adults remains unclear. To assess the validity and reliability of the linear function between heart rate and energy expenditure in older adults using different levels of calibration. Heart rate and energy expenditure were assessed across five levels of exertion in 290 adults participating in the Baltimore Longitudinal Study of Aging. Correlation and random effects regression analyses assessed the linearity of the relationship between heart rate and energy expenditure and cross-validation models assessed predictive performance. Heart rate and energy expenditure were highly correlated (r=0.98) and linear regardless of age or sex. Intra-person variability was low but inter-person variability was high, with substantial heterogeneity of the random intercept (s.d. =0.372) despite similar slopes. Cross-validation models indicated individual calibration data substantially improves accuracy predictions of energy expenditure from heart rate, reducing the potential for considerable measurement bias. Although using five calibration measures provided the greatest reduction in the standard deviation of prediction errors (1.08 kcals/min), substantial improvement was also noted with two (0.75 kcals/min). These findings indicate standard regression equations may be used to make population-level inferences when estimating energy expenditure from heart rate in older adults but caution should be exercised when making inferences at the individual level without proper calibration.

Efficient least angle regression for identification of linear-in-the-parameters models

PubMed Central

Beach, Thomas H.; Rezgui, Yacine

2017-01-01

Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm. PMID:28293140

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

A Study of the Effect of the Front-End Styling of Sport Utility Vehicles on Pedestrian Head Injuries

PubMed Central

Qin, Qin; Chen, Zheng; Bai, Zhonghao; Cao, Libo

2018-01-01

Background The number of sport utility vehicles (SUVs) on China market is continuously increasing. It is necessary to investigate the relationships between the front-end styling features of SUVs and head injuries at the styling design stage for improving the pedestrian protection performance and product development efficiency. Methods Styling feature parameters were extracted from the SUV side contour line. And simplified finite element models were established based on the 78 SUV side contour lines. Pedestrian headform impact simulations were performed and validated. The head injury criterion of 15 ms (HIC15) at four wrap-around distances was obtained. A multiple linear regression analysis method was employed to describe the relationships between the styling feature parameters and the HIC15 at each impact point. Results The relationship between the selected styling features and the HIC15 showed reasonable correlations, and the regression models and the selected independent variables showed statistical significance. Conclusions The regression equations obtained by multiple linear regression can be used to assess the performance of SUV styling in protecting pedestrians' heads and provide styling designers with technical guidance regarding their artistic creations.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

Creating a non-linear total sediment load formula using polynomial best subset regression model

NASA Astrophysics Data System (ADS)

Okcu, Davut; Pektas, Ali Osman; Uyumaz, Ali

2016-08-01

The aim of this study is to derive a new total sediment load formula which is more accurate and which has less application constraints than the well-known formulae of the literature. 5 most known stream power concept sediment formulae which are approved by ASCE are used for benchmarking on a wide range of datasets that includes both field and flume (lab) observations. The dimensionless parameters of these widely used formulae are used as inputs in a new regression approach. The new approach is called Polynomial Best subset regression (PBSR) analysis. The aim of the PBRS analysis is fitting and testing all possible combinations of the input variables and selecting the best subset. Whole the input variables with their second and third powers are included in the regression to test the possible relation between the explanatory variables and the dependent variable. While selecting the best subset a multistep approach is used that depends on significance values and also the multicollinearity degrees of inputs. The new formula is compared to others in a holdout dataset and detailed performance investigations are conducted for field and lab datasets within this holdout data. Different goodness of fit statistics are used as they represent different perspectives of the model accuracy. After the detailed comparisons are carried out we figured out the most accurate equation that is also applicable on both flume and river data. Especially, on field dataset the prediction performance of the proposed formula outperformed the benchmark formulations.

Factors contributing to tooth loss among the elderly: A cross sectional study.

PubMed

Natto, Zuhair S; Aladmawy, Majdi; Alasqah, Mohammed; Papas, Athena

2014-12-01

The present study evaluates the influence of several demographic, health, personal, and clinical factors on the number of missing teeth in old age sample. The number of patients included was 259; they received a full mouth examination and answered a questionnaire provided by one examiner. All the variables related to teeth loss based on the literature were included. These variables focused on age, gender, race, marital status, clinical attachment level, pocket depth, year of smoking, number of cigarettes smoked per day, number of medications, root decay, coronal decay, health status, and year of education. Statistical analysis involved stepwise multivariate linear regression. Teeth loss was statistically associated with clinical attachment level (CAL)(p value 0.0001), pocket depth (PD) (0.0007) and education level (0.0048). When smoking was included in the model, age was significantly associated with teeth loss (0.0037). At least one of these four factors was also related to teeth loss in several specific groups such as diabetes mellitus, male, and White. The multiple linear regressions for all the proposed variables showed that they contributed to teeth loss by about 23%. It can be concluded that less education or increased clinical attachment level loss may increase number of missing teeth. Additionally, age may cause teeth loss in the presence of smoking. Copyright © 2014. Published by Elsevier B.V.

An Application of Robust Method in Multiple Linear Regression Model toward Credit Card Debt

NASA Astrophysics Data System (ADS)

Amira Azmi, Nur; Saifullah Rusiman, Mohd; Khalid, Kamil; Roslan, Rozaini; Sufahani, Suliadi; Mohamad, Mahathir; Salleh, Rohayu Mohd; Hamzah, Nur Shamsidah Amir

2018-04-01

Credit card is a convenient alternative replaced cash or cheque, and it is essential component for electronic and internet commerce. In this study, the researchers attempt to determine the relationship and significance variables between credit card debt and demographic variables such as age, household income, education level, years with current employer, years at current address, debt to income ratio and other debt. The provided data covers 850 customers information. There are three methods that applied to the credit card debt data which are multiple linear regression (MLR) models, MLR models with least quartile difference (LQD) method and MLR models with mean absolute deviation method. After comparing among three methods, it is found that MLR model with LQD method became the best model with the lowest value of mean square error (MSE). According to the final model, it shows that the years with current employer, years at current address, household income in thousands and debt to income ratio are positively associated with the amount of credit debt. Meanwhile variables for age, level of education and other debt are negatively associated with amount of credit debt. This study may serve as a reference for the bank company by using robust methods, so that they could better understand their options and choice that is best aligned with their goals for inference regarding to the credit card debt.

Fasting insulin levels and metabolic risk factors in type 2 diabetic patients at the first visit in Japan: a 10-year, nationwide, observational study (JDDM 28).

PubMed

Matsuba, Ikuro; Saito, Kazumi; Takai, Masahiko; Hirao, Koichi; Sone, Hirohito

2012-09-01

To investigate the relationship between fasting insulin levels and metabolic risk factors (MRFs) in type 2 diabetic patients at the first clinic/hospital visit in Japan over the years 2000 to 2009. In total, 4,798 drug-naive Japanese patients with type 2 diabetes were registered on their first clinic/hospital visits. Conventional clinical factors and fasting insulin levels were observed at baseline within the Japan Diabetes Clinical Data Management (JDDM) study between consecutive 2-year groups. Multiple linear regression analysis was performed using a model in which the dependent variable was fasting insulin values using various clinical explanatory variables. Fasting insulin levels were found to be decreasing from 2000 to 2009. Multiple linear regression analysis with the fasting insulin levels as the dependent variable showed that waist circumference (WC), BMI, mean blood pressure, triglycerides, and HDL cholesterol were significant, with WC and BMI as the main factors. ANCOVA after adjustment for age and fasting plasma glucose clearly shows the decreasing trend in fasting insulin levels and the increasing trend in BMI. During the 10-year observation period, the decreasing trend in fasting insulin was related to the slight increase in WC/BMI in type 2 diabetes. Low pancreatic β-cell reserve on top of a lifestyle background might be dependent on an increase in MRFs.

Fasting Insulin Levels and Metabolic Risk Factors in Type 2 Diabetic Patients at the First Visit in Japan

PubMed Central

Matsuba, Ikuro; Saito, Kazumi; Takai, Masahiko; Hirao, Koichi; Sone, Hirohito

2012-01-01

OBJECTIVE To investigate the relationship between fasting insulin levels and metabolic risk factors (MRFs) in type 2 diabetic patients at the first clinic/hospital visit in Japan over the years 2000 to 2009. RESEARCH DESIGN AND METHODS In total, 4,798 drug-naive Japanese patients with type 2 diabetes were registered on their first clinic/hospital visits. Conventional clinical factors and fasting insulin levels were observed at baseline within the Japan Diabetes Clinical Data Management (JDDM) study between consecutive 2-year groups. Multiple linear regression analysis was performed using a model in which the dependent variable was fasting insulin values using various clinical explanatory variables. RESULTS Fasting insulin levels were found to be decreasing from 2000 to 2009. Multiple linear regression analysis with the fasting insulin levels as the dependent variable showed that waist circumference (WC), BMI, mean blood pressure, triglycerides, and HDL cholesterol were significant, with WC and BMI as the main factors. ANCOVA after adjustment for age and fasting plasma glucose clearly shows the decreasing trend in fasting insulin levels and the increasing trend in BMI. CONCLUSIONS During the 10-year observation period, the decreasing trend in fasting insulin was related to the slight increase in WC/BMI in type 2 diabetes. Low pancreatic β-cell reserve on top of a lifestyle background might be dependent on an increase in MRFs. PMID:22665215

Does raising type 1 error rate improve power to detect interactions in linear regression models? A simulation study.

PubMed

Durand, Casey P

2013-01-01

Statistical interactions are a common component of data analysis across a broad range of scientific disciplines. However, the statistical power to detect interactions is often undesirably low. One solution is to elevate the Type 1 error rate so that important interactions are not missed in a low power situation. To date, no study has quantified the effects of this practice on power in a linear regression model. A Monte Carlo simulation study was performed. A continuous dependent variable was specified, along with three types of interactions: continuous variable by continuous variable; continuous by dichotomous; and dichotomous by dichotomous. For each of the three scenarios, the interaction effect sizes, sample sizes, and Type 1 error rate were varied, resulting in a total of 240 unique simulations. In general, power to detect the interaction effect was either so low or so high at α = 0.05 that raising the Type 1 error rate only served to increase the probability of including a spurious interaction in the model. A small number of scenarios were identified in which an elevated Type 1 error rate may be justified. Routinely elevating Type 1 error rate when testing interaction effects is not an advisable practice. Researchers are best served by positing interaction effects a priori and accounting for them when conducting sample size calculations.

A Linear Dynamical Systems Approach to Streamflow Reconstruction Reveals History of Regime Shifts in Northern Thailand

NASA Astrophysics Data System (ADS)

Nguyen, Hung T. T.; Galelli, Stefano

2018-03-01

Catchment dynamics is not often modeled in streamflow reconstruction studies; yet, the streamflow generation process depends on both catchment state and climatic inputs. To explicitly account for this interaction, we contribute a linear dynamic model, in which streamflow is a function of both catchment state (i.e., wet/dry) and paleoclimatic proxies. The model is learned using a novel variant of the Expectation-Maximization algorithm, and it is used with a paleo drought record—the Monsoon Asia Drought Atlas—to reconstruct 406 years of streamflow for the Ping River (northern Thailand). Results for the instrumental period show that the dynamic model has higher accuracy than conventional linear regression; all performance scores improve by 45-497%. Furthermore, the reconstructed trajectory of the state variable provides valuable insights about the catchment history—e.g., regime-like behavior—thereby complementing the information contained in the reconstructed streamflow time series. The proposed technique can replace linear regression, since it only requires information on streamflow and climatic proxies (e.g., tree-rings, drought indices); furthermore, it is capable of readily generating stochastic streamflow replicates. With a marginal increase in computational requirements, the dynamic model brings more desirable features and value to streamflow reconstructions.

Prevalence of health literacy and its correlates among patients with type II diabetes in Kuwait: A population based study.

PubMed

Hussein, Shaimaa H; Almajran, Abdullah; Albatineh, Ahmed N

2018-05-03

The purpose of this study is to estimate the prevalence of health literacy among patients with type II diabetes and investigate its association with several covariates. No studies were conducted in the Arabian Gulf region characterizing such factors for this population. A cross sectional study was implemented in which 359 type II diabetes patients were recruited from diabetes centers across Kuwait. Health literacy was measured by STOFHLA. Multivariate linear regression was applied to investigate the relationship between health literacy and several covariates. About 44.5% had inadequate, 19.5% marginal, and 35.5% adequate health literacy. Patients with inadequate health literacy were more likely to be older, females, widowed, low education, with income less than 500 KD/month. Multivariate linear regression indicated residence, nationality, education level, and age were significantly associated with health literacy. Adding marital status and gender, hierarchical linear regression revealed that 43.4% of the variability was accounted for. Inadequate health literacy is high in Kuwait. Interventions should be implemented to improve health literacy. This will reduce the prevalence of diabetes-related complications, produce better diabetes outcomes, and improve patients' quality-of-life. Health literacy should be an integral part to health promotion and chronic diseases' management programs in Kuwait. Copyright © 2018 Elsevier B.V. All rights reserved.

On the equivalence of case-crossover and time series methods in environmental epidemiology.

PubMed

Lu, Yun; Zeger, Scott L

2007-04-01

The case-crossover design was introduced in epidemiology 15 years ago as a method for studying the effects of a risk factor on a health event using only cases. The idea is to compare a case's exposure immediately prior to or during the case-defining event with that same person's exposure at otherwise similar "reference" times. An alternative approach to the analysis of daily exposure and case-only data is time series analysis. Here, log-linear regression models express the expected total number of events on each day as a function of the exposure level and potential confounding variables. In time series analyses of air pollution, smooth functions of time and weather are the main confounders. Time series and case-crossover methods are often viewed as competing methods. In this paper, we show that case-crossover using conditional logistic regression is a special case of time series analysis when there is a common exposure such as in air pollution studies. This equivalence provides computational convenience for case-crossover analyses and a better understanding of time series models. Time series log-linear regression accounts for overdispersion of the Poisson variance, while case-crossover analyses typically do not. This equivalence also permits model checking for case-crossover data using standard log-linear model diagnostics.

Revisiting the Impact of NCLB High-Stakes School Accountability, Capacity, and Resources: State NAEP 1990-2009 Reading and Math Achievement Gaps and Trends

ERIC Educational Resources Information Center

Lee, Jaekyung; Reeves, Todd

2012-01-01

This study examines the impact of high-stakes school accountability, capacity, and resources under NCLB on reading and math achievement outcomes through comparative interrupted time-series analyses of 1990-2009 NAEP state assessment data. Through hierarchical linear modeling latent variable regression with inverse probability of treatment…

Placement Model for First-Time Freshmen in Calculus I (Math 131): University of Northern Colorado

ERIC Educational Resources Information Center

Heiny, Robert L.; Heiny, Erik L.; Raymond, Karen

2017-01-01

Two approaches, Linear Discriminant Analysis, and Logistic Regression are used and compared to predict success or failure for first-time freshmen in the first calculus course at a medium-sized public, 4-year institution prior to Fall registration. The predictor variables are high school GPA, the number, and GPA's of college prep mathematics…

Analysis of the Effects of the Commander’s Battle Positioning on Unit Combat Performance

DTIC Science & Technology

1991-03-01

Analysis ......... .. 58 Logistic Regression Analysis ......... .. 61 Canonical Correlation Analysis ........ .. 62 Descriminant Analysis...entails classifying objects into two or more distinct groups, or responses. Dillon defines descriminant analysis as "deriving linear combinations of the...object given it’s predictor variables. The second objective is, through analysis of the parameters of the descriminant functions, determine those

Examining the Influence of Selected Factors on Perceived Co-Op Work-Term Quality from a Student Perspective

ERIC Educational Resources Information Center

Drewery, David; Nevison, Colleen; Pretti, T. Judene; Cormier, Lauren; Barclay, Sage; Pennaforte, Antoine

2016-01-01

This study discusses and tests a conceptual model of co-op work-term quality from a student perspective. Drawing from an earlier exploration of co-op students' perceptions of work-term quality, variables related to role characteristics, interpersonal dynamics, and organizational elements were used in a multiple linear regression analysis to…

Personal Best Time, Percent Body Fat, and Training Are Differently Associated with Race Time for Male and Female Ironman Triathletes

ERIC Educational Resources Information Center

Knechtle, Beat; Wirth, Andrea; Baumann, Barbara; Knechtle, Patrizia; Rosemann, Thomas

2010-01-01

We studied male and female nonprofessional Ironman triathletes to determine whether percent body fat, training, and/or previous race experience were associated with race performance. We used simple linear regression analysis, with total race time as the dependent variable, to investigate the relationship among athletes' percent body fat, average…

Alcohol consumption and its related harms in The Netherlands since 1960: relationships with planned and unplanned factors.

PubMed

Knibbe, Ronald A; Derickx, Mieke; Allamani, Allaman; Massini, Giulia

2014-10-01

to establish which unplanned (social developments) and planned (alcohol policy measures) factors are related to per capita consumption and alcohol-related harms in the Netherlands. linear regression was used to establish which of the planned and unplanned factors were most strongly connected with alcohol consumption and harms. Artificial Neural Analysis (ANN) was used to inspect the interconnections between all variables. mothers age at birth was most strongly associated with increase in consumption. The ban on selling alcoholic beverages at petrol station was associated with a decrease in consumption. The linear regression of harms did not show any relation between alcohol policy measures and harms. The ANN-analyses indicate a very high interconnectedness between all variables allowing no causal inferences. Exceptions are the relation between price of beer and wine and the consumption of these beverages and the relation between a decrease in transport mortality and the increased use of breathalyzers tests and a restriction of paracommercial selling. unplanned factors are most strongly associated with per capita consumption and harms. ANN-analysis indicates that price of alcoholic beverages, breath testing, and restriction of sales may have had some influence. The study's limitations are noted.

Use of a tracing task to assess visuomotor performance for evidence of concussion and recuperation.

PubMed

Kelty-Stephen, Damian G; Qureshi Ahmad, Mona; Stirling, Leia

2015-12-01

The likelihood of suffering a concussion while playing a contact sport ranges from 15-45% per year of play. These rates are highly variable as athletes seldom report concussive symptoms, or do not recognize their symptoms. We performed a prospective cohort study (n = 206, aged 10-17) to examine visuomotor tracing to determine the sensitivity for detecting neuromotor components of concussion. Tracing variability measures were investigated for a mean shift with presentation of concussion-related symptoms and a linear return toward baseline over subsequent return visits. Furthermore, previous research relating brain injury to the dissociation of smooth movements into "submovements" led to the expectation that cumulative micropause duration, a measure of motion continuity, might detect likelihood of injury. Separate linear mixed effects regressions of tracing measures indicated that 4 of the 5 tracing measures captured both short-term effects of injury and longer-term effects of recovery with subsequent visits. Cumulative micropause duration has a positive relationship with likelihood of participants having had a concussion. The present results suggest that future research should evaluate how well the coefficients for the tracing parameter in the logistic regression help to detect concussion in novel cases. (c) 2015 APA, all rights reserved).

Predicting the occurrence of wildfires with binary structured additive regression models.

PubMed

Ríos-Pena, Laura; Kneib, Thomas; Cadarso-Suárez, Carmen; Marey-Pérez, Manuel

2017-02-01

Wildfires are one of the main environmental problems facing societies today, and in the case of Galicia (north-west Spain), they are the main cause of forest destruction. This paper used binary structured additive regression (STAR) for modelling the occurrence of wildfires in Galicia. Binary STAR models are a recent contribution to the classical logistic regression and binary generalized additive models. Their main advantage lies in their flexibility for modelling non-linear effects, while simultaneously incorporating spatial and temporal variables directly, thereby making it possible to reveal possible relationships among the variables considered. The results showed that the occurrence of wildfires depends on many covariates which display variable behaviour across space and time, and which largely determine the likelihood of ignition of a fire. The joint possibility of working on spatial scales with a resolution of 1 × 1 km cells and mapping predictions in a colour range makes STAR models a useful tool for plotting and predicting wildfire occurrence. Lastly, it will facilitate the development of fire behaviour models, which can be invaluable when it comes to drawing up fire-prevention and firefighting plans. Copyright © 2016 Elsevier Ltd. All rights reserved.

Calibration of the dietary data obtained from the Brazilian center of the Natural History of HPV Infection in Men study: the HIM Study.

PubMed

Teixeira, Juliana Araujo; Baggio, Maria Luiza; Fisberg, Regina Mara; Marchioni, Dirce Maria Lobo

2010-12-01

The objective of this study was to estimate the regressions calibration for the dietary data that were measured using the quantitative food frequency questionnaire (QFFQ) in the Natural History of HPV Infection in Men: the HIM Study in Brazil. A sample of 98 individuals from the HIM study answered one QFFQ and three 24-hour recalls (24HR) at interviews. The calibration was performed using linear regression analysis in which the 24HR was the dependent variable and the QFFQ was the independent variable. Age, body mass index, physical activity, income and schooling were used as adjustment variables in the models. The geometric means between the 24HR and the calibration-corrected QFFQ were statistically equal. The dispersion graphs between the instruments demonstrate increased correlation after making the correction, although there is greater dispersion of the points with worse explanatory power of the models. Identification of the regressions calibration for the dietary data of the HIM study will make it possible to estimate the effect of the diet on HPV infection, corrected for the measurement error of the QFFQ.

Multiple long-term trends and trend reversals dominate environmental conditions in a man-made freshwater reservoir.

PubMed

Znachor, Petr; Nedoma, Jiří; Hejzlar, Josef; Seďa, Jaromír; Kopáček, Jiří; Boukal, David; Mrkvička, Tomáš

2018-05-15

Man-made reservoirs are common across the world and provide a wide range of ecological services. Environmental conditions in riverine reservoirs are affected by the changing climate, catchment-wide processes and manipulations with the water level, and water abstraction from the reservoir. Long-term trends of environmental conditions in reservoirs thus reflect a wider range of drivers in comparison to lakes, which makes the understanding of reservoir dynamics more challenging. We analysed a 32-year time series of 36 environmental variables characterising weather, land use in the catchment, reservoir hydrochemistry, hydrology and light availability in the small, canyon-shaped Římov Reservoir in the Czech Republic to detect underlying trends, trend reversals and regime shifts. To do so, we fitted linear and piecewise linear regression and a regime shift model to the time series of mean annual values of each variable and to principal components produced by Principal Component Analysis. Models were weighted and ranked using Akaike information criterion and the model selection approach. Most environmental variables exhibited temporal changes that included time-varying trends and trend reversals. For instance, dissolved organic carbon showed a linear increasing trend while nitrate concentration or conductivity exemplified trend reversal. All trend reversals and cessations of temporal trends in reservoir hydrochemistry (except total phosphorus concentrations) occurred in the late 1980s and during 1990s as a consequence of dramatic socioeconomic changes. After a series of heavy rains in the late 1990s, an administrative decision to increase the flood-retention volume of the reservoir resulted in a significant regime shift in reservoir hydraulic conditions in 1999. Our analyses also highlight the utility of the model selection framework, based on relatively simple extensions of linear regression, to describe temporal trends in reservoir characteristics. This approach can provide a solid basis for a better understanding of processes in freshwater reservoirs. Copyright © 2017 Elsevier B.V. All rights reserved.

Unbiased split variable selection for random survival forests using maximally selected rank statistics.

PubMed

Wright, Marvin N; Dankowski, Theresa; Ziegler, Andreas

2017-04-15

The most popular approach for analyzing survival data is the Cox regression model. The Cox model may, however, be misspecified, and its proportionality assumption may not always be fulfilled. An alternative approach for survival prediction is random forests for survival outcomes. The standard split criterion for random survival forests is the log-rank test statistic, which favors splitting variables with many possible split points. Conditional inference forests avoid this split variable selection bias. However, linear rank statistics are utilized by default in conditional inference forests to select the optimal splitting variable, which cannot detect non-linear effects in the independent variables. An alternative is to use maximally selected rank statistics for the split point selection. As in conditional inference forests, splitting variables are compared on the p-value scale. However, instead of the conditional Monte-Carlo approach used in conditional inference forests, p-value approximations are employed. We describe several p-value approximations and the implementation of the proposed random forest approach. A simulation study demonstrates that unbiased split variable selection is possible. However, there is a trade-off between unbiased split variable selection and runtime. In benchmark studies of prediction performance on simulated and real datasets, the new method performs better than random survival forests if informative dichotomous variables are combined with uninformative variables with more categories and better than conditional inference forests if non-linear covariate effects are included. In a runtime comparison, the method proves to be computationally faster than both alternatives, if a simple p-value approximation is used. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Correlation of Vitamin D status and orthodontic-induced external apical root resorption

PubMed Central

Tehranchi, Azita; Sadighnia, Azin; Younessian, Farnaz; Abdi, Amir H.; Shirvani, Armin

2017-01-01

Background: Adequate Vitamin D is essential for dental and skeletal health in children and adult. The purpose of this study was to assess the correlation of serum Vitamin D level with external-induced apical root resorption (EARR) following fixed orthodontic treatment. Materials and Methods: In this cross-sectional study, the prevalence of Vitamin D deficiency (defined by25-hydroxyvitamin-D) was determined in 34 patients (23.5% male; age range 12–23 years; mean age 16.63 ± 2.84) treated with fixed orthodontic treatment. Root resorption of four maxillary incisors was measured using before and after periapical radiographs (136 measured teeth) by means of a design-to-purpose software to optimize data collection. Teeth with a maximum percentage of root resorption (%EARR) were indicated as representative root resorption for each patient. A multiple linear regression model and Pearson correlation coefficient were used to assess the association of Vitamin D status and observed EARR. P < 0.05 was considered statistically significant. Results: The Pearson coefficient between these two variables was determined about 0.15 (P = 0.38). Regression analysis revealed that Vitamin D status of the patients demonstrated no significant statistical correlation with EARR, after adjustment of confounding variables using linear regression model (P > 0.05). Conclusion: This study suggests that Vitamin D level is not among the clinical variables that are potential contributors for EARR. The prevalence of Vitamin D deficiency does not differ in patients with higher EARR. These data suggest the possibility that Vitamin D insufficiency may not contribute to the development of more apical root resorption although this remains to be confirmed by further longitudinal cohort studies. PMID:29238379

Short-term quality of life after subthalamic stimulation depends on non-motor symptoms in Parkinson's disease.

PubMed

Dafsari, Haidar Salimi; Weiß, Luisa; Silverdale, Monty; Rizos, Alexandra; Reddy, Prashanth; Ashkan, Keyoumars; Evans, Julian; Reker, Paul; Petry-Schmelzer, Jan Niklas; Samuel, Michael; Visser-Vandewalle, Veerle; Antonini, Angelo; Martinez-Martin, Pablo; Ray-Chaudhuri, K; Timmermann, Lars

2018-02-24

Subthalamic nucleus (STN) deep brain stimulation (DBS) improves quality of life (QoL), motor, and non-motor symptoms (NMS) in advanced Parkinson's disease (PD). However, considerable inter-individual variability has been observed for QoL outcome. We hypothesized that demographic and preoperative NMS characteristics can predict postoperative QoL outcome. In this ongoing, prospective, multicenter study (Cologne, Manchester, London) including 88 patients, we collected the following scales preoperatively and on follow-up 6 months postoperatively: PDQuestionnaire-8 (PDQ-8), NMSScale (NMSS), NMSQuestionnaire (NMSQ), Scales for Outcomes in PD (SCOPA)-motor examination, -complications, and -activities of daily living, levodopa equivalent daily dose. We dichotomized patients into "QoL responders"/"non-responders" and screened for factors associated with QoL improvement with (1) Spearman-correlations between baseline test scores and QoL improvement, (2) step-wise linear regressions with baseline test scores as independent and QoL improvement as dependent variables, (3) logistic regressions using aforementioned "responders/non-responders" as dependent variable. All outcomes improved significantly on follow-up. However, approximately 44% of patients were categorized as "QoL non-responders". Spearman-correlations, linear and logistic regression analyses were significant for NMSS and NMSQ but not for SCOPA-motor examination. Post-hoc, we identified specific NMS (flat moods, difficulties experiencing pleasure, pain, bladder voiding) as significant contributors to QoL outcome. Our results provide evidence that QoL improvement after STN-DBS depends on preoperative NMS characteristics. These findings are important in the advising and selection of individuals for DBS therapy. Future studies investigating motor and non-motor PD clusters may enable stratifying QoL outcomes and help predict patients' individual prospects of benefiting from DBS. Copyright © 2018. Published by Elsevier Inc.

Do Our Means of Inquiry Match our Intentions?

PubMed Central

Petscher, Yaacov

2016-01-01

A key stage of the scientific method is the analysis of data, yet despite the variety of methods that are available to researchers they are most frequently distilled to a model that focuses on the average relation between variables. Although research questions are frequently conceived with broad inquiry in mind, most regression methods are limited in comprehensively evaluating how observed behaviors are related to each other. Quantile regression is a largely unknown yet well-suited analytic technique similar to traditional regression analysis, but allows for a more systematic approach to understanding complex associations among observed phenomena in the psychological sciences. Data from the National Education Longitudinal Study of 1988/2000 are used to illustrate how quantile regression overcomes the limitations of average associations in linear regression by showing that psychological well-being and sex each differentially relate to reading achievement depending on one’s level of reading achievement. PMID:27486410

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space.

PubMed

Jacob, Benjamin J; Krapp, Fiorella; Ponce, Mario; Gottuzzo, Eduardo; Griffith, Daniel A; Novak, Robert J

2010-05-01

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multi-drug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDRTB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e., the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird 0.61 m data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centers, using a 10 m2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health center were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS(R) module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centers and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

Improving the Accuracy of Mapping Urban Vegetation Carbon Density by Combining Shadow Remove, Spectral Unmixing Analysis and Spatial Modeling

NASA Astrophysics Data System (ADS)

Qie, G.; Wang, G.; Wang, M.

2016-12-01

Mixed pixels and shadows due to buildings in urban areas impede accurate estimation and mapping of city vegetation carbon density. In most of previous studies, these factors are often ignored, which thus result in underestimation of city vegetation carbon density. In this study we presented an integrated methodology to improve the accuracy of mapping city vegetation carbon density. Firstly, we applied a linear shadow remove analysis (LSRA) on remotely sensed Landsat 8 images to reduce the shadow effects on carbon estimation. Secondly, we integrated a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), and utilized and compared the integrated models on shadow-removed images to map vegetation carbon density. This methodology was examined in Shenzhen City of Southeast China. A data set from a total of 175 sample plots measured in 2013 and 2014 was used to train the models. The independent variables statistically significantly contributing to improving the fit of the models to the data and reducing the sum of squared errors were selected from a total of 608 variables derived from different image band combinations and transformations. The vegetation fraction from LSUA was then added into the models as an important independent variable. The estimates obtained were evaluated using a cross-validation method. Our results showed that higher accuracies were obtained from the integrated models compared with the ones using traditional methods which ignore the effects of mixed pixels and shadows. This study indicates that the integrated method has great potential on improving the accuracy of urban vegetation carbon density estimation. Key words: Urban vegetation carbon, shadow, spectral unmixing, spatial modeling, Landsat 8 images

Episiotomy increases perineal laceration length in primiparous women.

PubMed

Nager, C W; Helliwell, J P

2001-08-01

The aim of this study was to determine the clinical factors that contribute to posterior perineal laceration length. A prospective observational study was performed in 80 consenting, mostly primiparous women with term pregnancies. Posterior perineal lacerations were measured immediately after delivery. Numerous maternal, fetal, and operator variables were evaluated against laceration length and degree of tear. Univariate and multivariate regression analyses were performed to evaluate laceration length and parametric clinical variables. Nonparametric clinical variables were evaluated against laceration length by the Mann-Whitney U test. A multivariate stepwise linear regression equation revealed that episiotomy adds nearly 3 cm to perineal lacerations. Tear length was highly associated with the degree of tear (R = 0.86, R(2) = 0.73) and the risk of recognized anal sphincter disruption. None of 35 patients without an episiotomy had a recognized anal sphincter disruption, but 6 of 27 patients with an episiotomy did (P <.001). Body mass index was the only maternal or fetal variable that showed even a slight correlation with laceration length (R = 0.30, P =.04). Episiotomy is the overriding determinant of perineal laceration length and recognized anal sphincter disruption.

Computational simulation of coupled material degradation processes for probabilistic lifetime strength of aerospace materials

NASA Technical Reports Server (NTRS)

Boyce, Lola; Bast, Callie C.

1992-01-01

The research included ongoing development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects or primative variables. These primative variable may include high temperature, fatigue or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation has been randomized and is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the above described constitutive equation using actual experimental materials data together with linear regression of that data, thereby predicting values for the empirical material constraints for each effect or primative variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from the open literature for materials typically of interest to those studying aerospace propulsion system components. Material data for Inconel 718 was analyzed using the developed methodology.

Selection of higher order regression models in the analysis of multi-factorial transcription data.

PubMed

Prazeres da Costa, Olivia; Hoffman, Arthur; Rey, Johannes W; Mansmann, Ulrich; Buch, Thorsten; Tresch, Achim

2014-01-01

Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control), and treatment/non-treatment with interferon-γ. We set up four different linear regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction), alleviating (co-occurring effects are weaker than expected from the single effects), or aggravating (stronger than expected). We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes. We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data.

Meta-regression analysis of the effect of trans fatty acids on low-density lipoprotein cholesterol.

PubMed

Allen, Bruce C; Vincent, Melissa J; Liska, DeAnn; Haber, Lynne T

2016-12-01

We conducted a meta-regression of controlled clinical trial data to investigate quantitatively the relationship between dietary intake of industrial trans fatty acids (iTFA) and increased low-density lipoprotein cholesterol (LDL-C). Previous regression analyses included insufficient data to determine the nature of the dose response in the low-dose region and have nonetheless assumed a linear relationship between iTFA intake and LDL-C levels. This work contributes to the previous work by 1) including additional studies examining low-dose intake (identified using an evidence mapping procedure); 2) investigating a range of curve shapes, including both linear and nonlinear models; and 3) using Bayesian meta-regression to combine results across trials. We found that, contrary to previous assumptions, the linear model does not acceptably fit the data, while the nonlinear, S-shaped Hill model fits the data well. Based on a conservative estimate of the degree of intra-individual variability in LDL-C (0.1 mmoL/L), as an estimate of a change in LDL-C that is not adverse, a change in iTFA intake of 2.2% of energy intake (%en) (corresponding to a total iTFA intake of 2.2-2.9%en) does not cause adverse effects on LDL-C. The iTFA intake associated with this change in LDL-C is substantially higher than the average iTFA intake (0.5%en). Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time‐to‐Event Analysis

PubMed Central

Gong, Xiajing; Hu, Meng

2018-01-01

Abstract Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time‐to‐event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high‐dimensional data featured by a large number of predictor variables. Our results showed that ML‐based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high‐dimensional data. The prediction performances of ML‐based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML‐based methods provide a powerful tool for time‐to‐event analysis, with a built‐in capacity for high‐dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. PMID:29536640

Incorporating nonlinearity into mediation analyses.

PubMed

Knafl, George J; Knafl, Kathleen A; Grey, Margaret; Dixon, Jane; Deatrick, Janet A; Gallo, Agatha M

2017-03-21

Mediation is an important issue considered in the behavioral, medical, and social sciences. It addresses situations where the effect of a predictor variable X on an outcome variable Y is explained to some extent by an intervening, mediator variable M. Methods for addressing mediation have been available for some time. While these methods continue to undergo refinement, the relationships underlying mediation are commonly treated as linear in the outcome Y, the predictor X, and the mediator M. These relationships, however, can be nonlinear. Methods are needed for assessing when mediation relationships can be treated as linear and for estimating them when they are nonlinear. Existing adaptive regression methods based on fractional polynomials are extended here to address nonlinearity in mediation relationships, but assuming those relationships are monotonic as would be consistent with theories about directionality of such relationships. Example monotonic mediation analyses are provided assessing linear and monotonic mediation of the effect of family functioning (X) on a child's adaptation (Y) to a chronic condition by the difficulty (M) for the family in managing the child's condition. Example moderated monotonic mediation and simulation analyses are also presented. Adaptive methods provide an effective way to incorporate possibly nonlinear monotonicity into mediation relationships.

Understanding Coupling of Global and Diffuse Solar Radiation with Climatic Variability

NASA Astrophysics Data System (ADS)

Hamdan, Lubna

Global solar radiation data is very important for wide variety of applications and scientific studies. However, this data is not readily available because of the cost of measuring equipment and the tedious maintenance and calibration requirements. Wide variety of models have been introduced by researchers to estimate and/or predict the global solar radiations and its components (direct and diffuse radiation) using other readily obtainable atmospheric parameters. The goal of this research is to understand the coupling of global and diffuse solar radiation with climatic variability, by investigating the relationships between these radiations and atmospheric parameters. For this purpose, we applied multilinear regression analysis on the data of National Solar Radiation Database 1991--2010 Update. The analysis showed that the main atmospheric parameters that affect the amount of global radiation received on earth's surface are cloud cover and relative humidity. Global radiation correlates negatively with both variables. Linear models are excellent approximations for the relationship between atmospheric parameters and global radiation. A linear model with the predictors total cloud cover, relative humidity, and extraterrestrial radiation is able to explain around 98% of the variability in global radiation. For diffuse radiation, the analysis showed that the main atmospheric parameters that affect the amount received on earth's surface are cloud cover and aerosol optical depth. Diffuse radiation correlates positively with both variables. Linear models are very good approximations for the relationship between atmospheric parameters and diffuse radiation. A linear model with the predictors total cloud cover, aerosol optical depth, and extraterrestrial radiation is able to explain around 91% of the variability in diffuse radiation. Prediction analysis showed that the linear models we fitted were able to predict diffuse radiation with efficiency of test adjusted R2 values equal to 0.93, using the data of total cloud cover, aerosol optical depth, relative humidity and extraterrestrial radiation. However, for prediction purposes, using nonlinear terms or nonlinear models might enhance the prediction of diffuse radiation.

Estimating top-of-atmosphere thermal infrared radiance using MERRA-2 atmospheric data

NASA Astrophysics Data System (ADS)

Kleynhans, Tania; Montanaro, Matthew; Gerace, Aaron; Kanan, Christopher

2017-05-01

Thermal infrared satellite images have been widely used in environmental studies. However, satellites have limited temporal resolution, e.g., 16 day Landsat or 1 to 2 day Terra MODIS. This paper investigates the use of the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data product, produced by NASA's Global Modeling and Assimilation Office (GMAO) to predict global topof-atmosphere (TOA) thermal infrared radiance. The high temporal resolution of the MERRA-2 data product presents opportunities for novel research and applications. Various methods were applied to estimate TOA radiance from MERRA-2 variables namely (1) a parameterized physics based method, (2) Linear regression models and (3) non-linear Support Vector Regression. Model prediction accuracy was evaluated using temporally and spatially coincident Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data as reference data. This research found that Support Vector Regression with a radial basis function kernel produced the lowest error rates. Sources of errors are discussed and defined. Further research is currently being conducted to train deep learning models to predict TOA thermal radiance

Theory of mind and executive function: working-memory capacity and inhibitory control as predictors of false-belief task performance.

PubMed

Mutter, Brigitte; Alcorn, Mark B; Welsh, Marilyn

2006-06-01

This study of the relationship between theory of mind and executive function examined whether on the false-belief task age differences between 3 and 5 ears of age are related to development of working-memory capacity and inhibitory processes. 72 children completed tasks measuring false belief, working memory, and inhibition. Significant age effects were observed for false-belief and working-memory performance, as well as for the false-alarm and perseveration measures of inhibition. A simultaneous multiple linear regression specified the contribution of age, inhibition, and working memory to the prediction of false-belief performance. This model was significant, explaining a total of 36% of the variance. To examine the independent contributions of the working-memory and inhibition variables, after controlling for age, two hierarchical multiple linear regressions were conducted. These multiple regression analyses indicate that working memory and inhibition make small, overlapping contributions to false-belief performance after accounting for age, but that working memory, as measured in this study, is a somewhat better predictor of false-belief understanding than is inhibition.

Neck-focused panic attacks among Cambodian refugees; a logistic and linear regression analysis.

PubMed

Hinton, Devon E; Chhean, Dara; Pich, Vuth; Um, Khin; Fama, Jeanne M; Pollack, Mark H

2006-01-01

Consecutive Cambodian refugees attending a psychiatric clinic were assessed for the presence and severity of current--i.e., at least one episode in the last month--neck-focused panic. Among the whole sample (N=130), in a logistic regression analysis, the Anxiety Sensitivity Index (ASI; odds ratio=3.70) and the Clinician-Administered PTSD Scale (CAPS; odds ratio=2.61) significantly predicted the presence of current neck panic (NP). Among the neck panic patients (N=60), in the linear regression analysis, NP severity was significantly predicted by NP-associated flashbacks (beta=.42), NP-associated catastrophic cognitions (beta=.22), and CAPS score (beta=.28). Further analysis revealed the effect of the CAPS score to be significantly mediated (Sobel test [Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182]) by both NP-associated flashbacks and catastrophic cognitions. In the care of traumatized Cambodian refugees, NP severity, as well as NP-associated flashbacks and catastrophic cognitions, should be specifically assessed and treated.

Modeling of time trends and interactions in vital rates using restricted regression splines.

PubMed

Heuer, C

1997-03-01

For the analysis of time trends in incidence and mortality rates, the age-period-cohort (apc) model has became a widely accepted method. The considered data are arranged in a two-way table by age group and calendar period, which are mostly subdivided into 5- or 10-year intervals. The disadvantage of this approach is the loss of information by data aggregation and the problems of estimating interactions in the two-way layout without replications. In this article we show how splines can be useful when yearly data, i.e., 1-year age groups and 1-year periods, are given. The estimated spline curves are still smooth and represent yearly changes in the time trends. Further, it is straightforward to include interaction terms by the tensor product of the spline functions. If the data are given in a nonrectangular table, e.g., 5-year age groups and 1-year periods, the period and cohort variables can be parameterized by splines, while the age variable is parameterized as fixed effect levels, which leads to a semiparametric apc model. An important methodological issue in developing the nonparametric and semiparametric models is stability of the estimated spline curve at the boundaries. Here cubic regression splines will be used, which are constrained to be linear in the tails. Another point of importance is the nonidentifiability problem due to the linear dependency of the three time variables. This will be handled by decomposing the basis of each spline by orthogonal projection into constant, linear, and nonlinear terms, as suggested by Holford (1983, Biometrics 39, 311-324) for the traditional apc model. The advantage of using splines for yearly data compared to the traditional approach for aggregated data is the more accurate curve estimation for the nonlinear trend changes and the simple way of modeling interactions between the time variables. The method will be demonstrated with hypothetical data as well as with cancer mortality data.

The use of generalised additive models (GAM) in dentistry.

PubMed

Helfenstein, U; Steiner, M; Menghini, G

1997-12-01

Ordinary multiple regression and logistic multiple regression are widely applied statistical methods which allow a researcher to 'explain' or 'predict' a response variable from a set of explanatory variables or predictors. In these models it is usually assumed that quantitative predictors such as age enter linearly into the model. During recent years these methods have been further developed to allow more flexibility in the way explanatory variables 'act' on a response variable. The methods are called 'generalised additive models' (GAM). The rigid linear terms characterising the association between response and predictors are replaced in an optimal way by flexible curved functions of the predictors (the 'profiles'). Plotting the 'profiles' allows the researcher to visualise easily the shape by which predictors 'act' over the whole range of values. The method facilitates detection of particular shapes such as 'bumps', 'U-shapes', 'J-shapes, 'threshold values' etc. Information about the shape of the association is not revealed by traditional methods. The shapes of the profiles may be checked by performing a Monte Carlo simulation ('bootstrapping'). After the presentation of the GAM a relevant case study is presented in order to demonstrate application and use of the method. The dependence of caries in primary teeth on a set of explanatory variables is investigated. Since GAMs may not be easily accessible to dentists, this article presents them in an introductory condensed form. It was thought that a nonmathematical summary and a worked example might encourage readers to consider the methods described. GAMs may be of great value to dentists in allowing visualisation of the shape by which predictors 'act' and obtaining a better understanding of the complex relationships between predictors and response.

Age, body mass, and gender as predictors of masters olympic weightlifting performance.

PubMed

Thé, Dwight J; Ploutz-Snyder, Lori

2003-07-01

The purpose of this study was to examine previously collected performance scores from the 2000 World Masters Weightlifting Championships to 1). determine the extent to which age and body mass are related to and predictive of indirect estimates of absolute and relative muscular power, and 2). assess possible gender differences in these associations. Dependent variables were absolute load (ABS = heaviest snatch [kg] + heaviest clean and jerk [kg]) and relative load (REL = ABS [kg]/body mass [kg]), representing indirect estimates of absolute and relative muscular power, respectively. Predictor variables were age (yr) and body mass (kg). Linear regression and various diagnostic procedures were used to analyze the data. The linear model provided an adequate fit for the data because no departures from the usual assumptions of normally distributed variables and homoscedastic error variance were observed. All predictor variables were significantly (P < 0.05) predictive of the dependent variables, but the magnitude of associations (e.g., R(ABS|BM) = 0.18 among females vs R(ABS|BM) = 0.57 among males) and extent of predictive ability (e.g., R(adj)2 for regression of ABS on age and body mass was 0.18-0.58 among females vs 0.74-0.83 among males) were significantly (P < 0.05) higher among males versus females. The extent to which age and body mass explain differences in muscular power differs between female and male masters weightlifters, but the rate of decline (%.yr-1) in power with advancing age is similar and is in agreement with previous reports for world record holders, other masters athletes, and healthy, untrained individuals, suggesting the importance of the aging process itself over physical activity history.

Modeling the microstructurally dependent mechanical properties of poly(ester-urethane-urea)s.

PubMed

Warren, P Daniel; Sycks, Dalton G; McGrath, Dominic V; Vande Geest, Jonathan P

2013-12-01

Poly(ester-urethane-urea) (PEUU) is one of many synthetic biodegradable elastomers under scrutiny for biomedical and soft tissue applications. The goal of this study was to investigate the effect of the experimental parameters on mechanical properties of PEUUs following exposure to different degrading environments, similar to that of the human body, using linear regression, producing one predictive model. The model utilizes two independent variables of poly(caprolactone) (PCL) type and copolymer crystallinity to predict the dependent variable of maximum tangential modulus (MTM). Results indicate that comparisons between PCLs at different degradation states are statistically different (p < 0.0003), while the difference between experimental and predicted average MTM is statistically negligible (p < 0.02). The linear correlation between experimental and predicted MTM values is R(2) = 0.75. Copyright © 2013 Wiley Periodicals, Inc., a Wiley Company.

Artificial neural networks environmental forecasting in comparison with multiple linear regression technique: From heavy metals to organic micropollutants screening in agricultural soils

NASA Astrophysics Data System (ADS)

Bonelli, Maria Grazia; Ferrini, Mauro; Manni, Andrea

2016-12-01

The assessment of metals and organic micropollutants contamination in agricultural soils is a difficult challenge due to the extensive area used to collect and analyze a very large number of samples. With Dioxins and dioxin-like PCBs measurement methods and subsequent the treatment of data, the European Community advises the develop low-cost and fast methods allowing routing analysis of a great number of samples, providing rapid measurement of these compounds in the environment, feeds and food. The aim of the present work has been to find a method suitable to describe the relations occurring between organic and inorganic contaminants and use the value of the latter in order to forecast the former. In practice, the use of a metal portable soil analyzer coupled with an efficient statistical procedure enables the required objective to be achieved. Compared to Multiple Linear Regression, the Artificial Neural Networks technique has shown to be an excellent forecasting method, though there is no linear correlation between the variables to be analyzed.

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Spatiotemporal Anopheles Population Dynamics, Response to Climatic Conditions: The Case of Chabahar, South Baluchistan, Iran.

PubMed

Farajzadeh, Manuchehr; Halimi, Mansour; Ghavidel, Yousef; Delavari, Mahdi

2015-01-01

An understanding of the factors that affect the abundance of Anopheline species provides an opportunity to better understand the dynamics of malaria transmission in different regions. Chabahar, located south east of Iran, is the most malarious region in the country. The main aim of this study was to quantify the spatiotemporal Anopheles population dynamics, response to climatic conditions in Chabahar. Satellite-based and land-based climatic data were used as explanatory variables. Monthly caught mosquitoes in 6 village sites of Chabahar were used as dependent variable. The spatiotemporal associations were first investigated by inspection of scatter plots and single variable regression analysis. A multivariate linear regression model was developed to reveal the association between environmental variables and the monthly mosquito abundance at a 95% confidence level (P ≤ 0.5). Results indicated that Anopheles mosquitoes can be found all year in Chabahar with 2 significant seasonal peaks from March to June (primary peak) and September to November (secondary peak). Results of the present study showed that 0.77 of yearly mosquito abundance emerges in the thermal range of 24°C to 30°C and the humidity range of 0.70 to 0.80 in Chabahar. According to the developed multivariate linear model, 0.88 of temporal variance of mosquito abundance, nighttime land surface temperature, and relative humidity of 15 Universal Time Coordinated (18.30 Iran) are the main drivers of mosquito population dynamics in Chabahar. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Inflammation, homocysteine and carotid intima-media thickness.

PubMed

Baptista, Alexandre P; Cacdocar, Sanjiva; Palmeiro, Hugo; Faísca, Marília; Carrasqueira, Herménio; Morgado, Elsa; Sampaio, Sandra; Cabrita, Ana; Silva, Ana Paula; Bernardo, Idalécio; Gome, Veloso; Neves, Pedro L

2008-01-01

Cardiovascular disease is the main cause of morbidity and mortality in chronic renal patients. Carotid intima-media thickness (CIMT) is one of the most accurate markers of atherosclerosis risk. In this study, the authors set out to evaluate a population of chronic renal patients to determine which factors are associated with an increase in intima-media thickness. We included 56 patients (F=22, M=34), with a mean age of 68.6 years, and an estimated glomerular filtration rate of 15.8 ml/min (calculated by the MDRD equation). Various laboratory and inflammatory parameters (hsCRP, IL-6 and TNF-alpha) were evaluated. All subjects underwent measurement of internal carotid artery intima-media thickness by high-resolution real-time B-mode ultrasonography using a 10 MHz linear transducer. Intima-media thickness was used as a dependent variable in a simple linear regression model, with the various laboratory parameters as independent variables. Only parameters showing a significant correlation with CIMT were evaluated in a multiple regression model: age (p=0.001), hemoglobin (p=00.3), logCRP (p=0.042), logIL-6 (p=0.004) and homocysteine (p=0.002). In the multiple regression model we found that age (p=0.001) and homocysteine (p=0.027) were independently correlated with CIMT. LogIL-6 did not reach statistical significance (p=0.057), probably due to the small population size. The authors conclude that age and homocysteine correlate with carotid intima-media thickness, and thus can be considered as markers/risk factors in chronic renal patients.

A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults

PubMed Central

Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A.; Aguiló, Antoni

2015-01-01

Background Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Methods Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. Results The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). Conclusions There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF. PMID:25821960

A comparison between multiple regression models and CUN-BAE equation to predict body fat in adults.

PubMed

Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A; Aguiló, Antoni

2015-01-01

Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF.

Gender and single nucleotide polymorphisms in MTHFR, BHMT, SPTLC1, CRBP2R, and SCARB1 are significant predictors of plasma homocysteine normalized by RBC folate in healthy adults.

USDA-ARS?s Scientific Manuscript database

Using linear regression models, we studied the main and two-way interaction effects of the predictor variables gender, age, BMI, and 64 folate/vitamin B-12/homocysteine/lipid/cholesterol-related single nucleotide polymorphisms (SNP) on log-transformed plasma homocysteine normalized by red blood cell...

Machine learning and linear regression models to predict catchment-level base cation weathering rates across the southern Appalachian Mountain region, USA

Treesearch

Nicholas A. Povak; Paul F. Hessburg; Todd C. McDonnell; Keith M. Reynolds; Timothy J. Sullivan; R. Brion Salter; Bernard J. Crosby

2014-01-01

Accurate estimates of soil mineral weathering are required for regional critical load (CL) modeling to identify ecosystems at risk of the deleterious effects from acidification. Within a correlative modeling framework, we used modeled catchment-level base cation weathering (BCw) as the response variable to identify key environmental correlates and predict a continuous...

The Heteroscedastic Graded Response Model with a Skewed Latent Trait: Testing Statistical and Substantive Hypotheses Related to Skewed Item Category Functions

ERIC Educational Resources Information Center

Molenaar, Dylan; Dolan, Conor V.; de Boeck, Paul

2012-01-01

The Graded Response Model (GRM; Samejima, "Estimation of ability using a response pattern of graded scores," Psychometric Monograph No. 17, Richmond, VA: The Psychometric Society, 1969) can be derived by assuming a linear regression of a continuous variable, Z, on the trait, [theta], to underlie the ordinal item scores (Takane & de Leeuw in…

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

PubMed Central

Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. PMID:28114334

Mercury, lead and cadmium in human milk in relation to diet, lifestyle habits and sociodemographic variables in Madrid (Spain).

PubMed

García-Esquinas, Esther; Pérez-Gómez, Beatriz; Fernández, Mario Antonio; Pérez-Meixeira, Ana María; Gil, Elisa; de Paz, Concha; Iriso, Andrés; Sanz, Juan Carlos; Astray, Jenaro; Cisneros, Margot; de Santos, Amparo; Asensio, Angel; García-Sagredo, José Miguel; García, José Frutos; Vioque, Jesus; Pollán, Marina; López-Abente, Gonzalo; González, Maria José; Martínez, Mercedes; Bohigas, Pedro Arias; Pastor, Roberto; Aragonés, Nuria

2011-09-01

Although breastfeeding is the ideal way of nurturing infants, it can be a source of exposure to toxicants. This study reports the concentration of Hg, Pb and Cd in breast milk from a sample of women drawn from the general population of the Madrid Region, and explores the association between metal levels and socio-demographic factors, lifestyle habits, diet and environmental exposures, including tobacco smoke, exposure at home and occupational exposures. Breast milk was obtained from 100 women (20 mL) at around the third week postpartum. Pb, Cd and Hg levels were determined using Atomic Absorption Spectrometry. Metal levels were log-transformed due to non-normal distribution. Their association with the variables collected by questionnaire was assessed using linear regression models. Separate models were fitted for Hg, Pb and Cd, using univariate linear regression in a first step. Secondly, multivariate linear regression models were adjusted introducing potential confounders specific for each metal. Finally, a test for trend was performed in order to evaluate possible dose-response relationships between metal levels and changes in variables categories. Geometric mean Hg, Pb and Cd content in milk were 0.53 μg L(-1), 15.56 μg L(-1), and 1.31 μg L(-1), respectively. Decreases in Hg levels in older women and in those with a previous history of pregnancies and lactations suggested clearance of this metal over lifetime, though differences were not statistically significant, probably due to limited sample size. Lead concentrations increased with greater exposure to motor vehicle traffic and higher potato consumption. Increased Cd levels were associated with type of lactation and tended to increase with tobacco smoking. Surveillance for the presence of heavy metals in human milk is needed. Smoking and dietary habits are the main factors linked to heavy metal levels in breast milk. Our results reinforce the need to strengthen national food safety programs and to further promote avoidance of unhealthy behaviors such as smoking during pregnancy. Copyright © 2011 Elsevier Ltd. All rights reserved.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

PubMed

Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods.

PubMed

Torija, Antonio J; Ruiz, Diego P

2015-02-01

The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among the multitude of variables involved in the characterization and modelling of environmental noise and environmental-noise magnitudes. Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Three machine-learning regression methods, which are considered very robust in solving non-linear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R(2)=0.94 and mean absolute error (MAE)=1.14-1.16 dB(A)). Copyright © 2014 Elsevier B.V. All rights reserved.

Regression Analysis of Top of Descent Location for Idle-thrust Descents

NASA Technical Reports Server (NTRS)

Stell, Laurel; Bronsvoort, Jesper; McDonald, Greg

2013-01-01

In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. The independent variables cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also recorded or computed post-operations. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajec- tory parameters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowl- edge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace. In particular, a model for TOD location that is linear in the independent variables would enable decision support tool human-machine interfaces for which a kinetic approach would be computationally too slow.

A comparative study of generalized linear mixed modelling and artificial neural network approach for the joint modelling of survival and incidence of Dengue patients in Sri Lanka

NASA Astrophysics Data System (ADS)

Hapugoda, J. C.; Sooriyarachchi, M. R.

2017-09-01

Survival time of patients with a disease and the incidence of that particular disease (count) is frequently observed in medical studies with the data of a clustered nature. In many cases, though, the survival times and the count can be correlated in a way that, diseases that occur rarely could have shorter survival times or vice versa. Due to this fact, joint modelling of these two variables will provide interesting and certainly improved results than modelling these separately. Authors have previously proposed a methodology using Generalized Linear Mixed Models (GLMM) by joining the Discrete Time Hazard model with the Poisson Regression model to jointly model survival and count model. As Aritificial Neural Network (ANN) has become a most powerful computational tool to model complex non-linear systems, it was proposed to develop a new joint model of survival and count of Dengue patients of Sri Lanka by using that approach. Thus, the objective of this study is to develop a model using ANN approach and compare the results with the previously developed GLMM model. As the response variables are continuous in nature, Generalized Regression Neural Network (GRNN) approach was adopted to model the data. To compare the model fit, measures such as root mean square error (RMSE), absolute mean error (AME) and correlation coefficient (R) were used. The measures indicate the GRNN model fits the data better than the GLMM model.

Prophylactic transabdominal amnioinfusion in oligohydramnios for preterm premature rupture of membranes: increase of amniotic fluid index during latency period.

PubMed

Garzetti, G G; Ciavattini, A; De Cristofaro, F; La Marca, N; Arduini, D

1997-01-01

This study was designed to: (i) evaluate the effect of amnioinfusion on the latency period in patients with oligohydramnios for preterm premature rupture of membranes, and (ii) to investigate the relationship between changes in the amniotic fluid index and fetal heart rate short-term variability by computerized Hewlett-Packard cardiotocography, longitudinally estimated before and after prophylactic amnioinfusion. All singleton pregnancies with prolonged premature rupture of membranes after 25 weeks of gestation and seen at the Institute of Obstetrics and Gynecology, University of Ancona (Italy), between January 1994 and June 1995 were included in the study. Transabdominal amnioinfusion with 150-350 ml warmed normal saline (25-50 ml/min) was performed at weekly intervals. Amniotic fluid volume was assessed ultrasonographically by means of the four-quadrant technique on a weekly basis before and after each amnioinfusion, as well as the short-term variability by a Hewlett-Packard computerized cardiotocographic system. 18 women were enrolled and underwent prophylactic transabdominal amnioinfusion at weekly intervals until delivery. Eighteen controls, who did not undergo prophylactic amnioinfusion, were recruited from our 1992-1993 series and included in the study. The median interval between premature rupture of membranes and delivery was 3.0 weeks (range 1-8 weeks), with an average delivery age of 33.0 weeks (range 27-36 weeks). The latency period was significantly longer in patients who underwent prophylactic amnioinfusion (mean +/- SD, 4.1 +/- 1.7 weeks) than in controls(1.7 +/- 1.0 weeks; p < 0.001). An increase in both the weekly amniotic fluid index (linear regression analysis r = 0.8, p = 0.03) and the weekly short-term variability (linear regression analysis r = 0.82, p = 0.02) was observed among patients who underwent prophylactic amnioinfusion. A direct relationship was observed between the amniotic fluid index and short-term variability (linear regression analysis r = 0.54, p = 0.04). The mean values of fetal movements recorded by computerized tomography during the 20 min of observation significantly increased after amnioinfusion in comparison with those before it (2.6 +/- 0.9 and 0.9 +/- 0.7 respectively; p = 0.001). The present study has shown a positive effect of prophylactic transabdominal amnioinfusion on the latency period in patients with preterm premature rupture of membranes and oligohydramnios. Among the patients who underwent amnioinfusion, an interesting improvement in fetal heart rate short-term variability was associated with the progressive increase in amniotic fluid volume, as an expression of fetal well-being.

Resilient Brain Aging: Characterization of Discordance between Alzheimer’s Disease Pathology and Cognition

PubMed Central

Negash, Selam; Wilson, Robert S.; Leurgans, Sue E.; Wolk, David A.; Schneider, Julie A.; Buchman, Aron S.; Bennett, David A.; Arnold, Steven. E.

2014-01-01

Background Although it is now evident that normal cognition can occur despite significant AD pathology, few studies have attempted to characterize this discordance, or examine factors that may contribute to resilient brain aging in the setting of AD pathology. Methods More than 2,000 older persons underwent annual evaluation as part of participation in the Religious Orders Study or Rush Memory Aging Project. A total of 966 subjects who had brain autopsy and comprehensive cognitive testing proximate to death were analyzed. Resilience was quantified as a continuous measure using linear regression modeling, where global cognition was entered as a dependent variable and global pathology was an independent variable. Studentized residuals generated from the model represented the discordance between cognition and pathology, and served as measure of resilience. The relation of resilience index to known risk factors for AD and related variables was examined. Results Multivariate regression models that adjusted for demographic variables revealed significant associations for early life socioeconomic status, reading ability, APOE-ε4 status, and past cognitive activity. A stepwise regression model retained reading level (estimate = 0.10, SE = 0.02; p < 0.0001) and past cognitive activity (estimate = 0.27, SE = 0.09; p = 0.002), suggesting the potential mediating role of these variables for resilience. Conclusions The construct of resilient brain aging can provide a framework for quantifying the discordance between cognition and pathology, and help identify factors that may mediate this relationship. PMID:23919768

Factors contributing to practice variation in post-stroke rehabilitation.

PubMed Central

Lee, A J; Huber, J H; Stason, W B

1997-01-01

OBJECTIVE: To analyze geographic variability in the utilization and cost of post-stroke medical care using multiple linear regression. DATA SOURCES/STUDY SETTING: A 20 percent random sample of Medicare beneficiaries with an admission to an acute care hospital for stroke during the first six months of 1991, supplemented by data from their Medicare claims and beneficiary records, the Medicare Cost Reports for hospitals and nursing homes, and the Area Resource File. STUDY DESIGN: Weighted least squares regression is used to analyze variations in post-stroke practice patterns across 151 MSAs (Metropolitan Statistical Areas). Average post-stroke costs, utilization rates, and facility lengths of stay are regressed on patient and market characteristics. DATA COLLECTION/EXTRACTION METHODS: For a six-month post-stroke interval, beneficiary-level post-stroke costs and service utilization are averaged by MSA. Variables describing market conditions are then added to these MSA-level records. PRINCIPAL FINDINGS: Patient variables rarely explain more than a third of practice variation, and often they explain substantially less than that. Market variables (with some exception) tend to be relatively less important. Finally, one-half to two-thirds of the practice variation across MSAs is unexplained by the patient and market factors measured in our data. CONCLUSIONS: A substantial portion of inter-MSA variability in utilization and intensity of post-stroke rehabilitation services cannot be explained by differences in patient characteristics. Given the large practice differences observed across MSAs, it seems unlikely that unmeasured patient differences can account for much more of the practice differences. PMID:9180616

Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment.

PubMed

Shi, Yuan; Lau, Kevin Ka-Lun; Ng, Edward

2017-08-01

Urban air quality serves as an important function of the quality of urban life. Land use regression (LUR) modelling of air quality is essential for conducting health impacts assessment but more challenging in mountainous high-density urban scenario due to the complexities of the urban environment. In this study, a total of 21 LUR models are developed for seven kinds of air pollutants (gaseous air pollutants CO, NO 2 , NO x , O 3 , SO 2 and particulate air pollutants PM 2.5 , PM 10 ) with reference to three different time periods (summertime, wintertime and annual average of 5-year long-term hourly monitoring data from local air quality monitoring network) in Hong Kong. Under the mountainous high-density urban scenario, we improved the traditional LUR modelling method by incorporating wind availability information into LUR modelling based on surface geomorphometrical analysis. As a result, 269 independent variables were examined to develop the LUR models by using the "ADDRESS" independent variable selection method and stepwise multiple linear regression (MLR). Cross validation has been performed for each resultant model. The results show that wind-related variables are included in most of the resultant models as statistically significant independent variables. Compared with the traditional method, a maximum increase of 20% was achieved in the prediction performance of annual averaged NO 2 concentration level by incorporating wind-related variables into LUR model development. Copyright © 2017 Elsevier Inc. All rights reserved.

Hierarchical multiple regression modelling on predictors of behavior and sexual practices at Takoradi Polytechnic, Ghana.

PubMed

Turkson, Anthony Joe; Otchey, James Eric

2015-01-14

Various psychosocial studies on health related lifestyles lay emphasis on the fact that the perception one has of himself as being at risk of HIV/AIDS infection was a necessary condition for preventive behaviors to be adopted. Hierarchical Multiple Regression models was used to examine the relationship between eight independent variables and one dependent variable to isolate predictors which have significant influence on behavior and sexual practices. A Cross-sectional design was used for the study. Structured close-ended interviewer-administered questionnaire was used to collect primary data. Multistage stratified technique was used to sample views from 380 students from Takoradi Polytechnic, Ghana. A Hierarchical multiple regression model was used to ascertain the significance of certain predictors of sexual behavior and practices. The variables that were extracted from the multiple regression were; for the constant; Beta=14.202, t=2.279, p=0.023, variable is significant; for the marital status; Beta=0.092, t=1.996, p<0.05, variable is significant; for the knowledge on AIDs; Beta=0.090, t=1.996, p<0.05, variable is significant; for the attitude towards HIV/AIDs; =0.486, t=10.575, p<0.001, variable is highly significant. Thus, the best fitting model for predicting behavior and sexual practices was a linear combination of the constant, one's marital status, knowledge on HIV/AIDs and Attitude towards HIV/AIDs., Y(Behavior and sexual practies)= Beta0+Beta1(Marital status)+Beta2(Knowledge on HIV/AIDs issues)+Beta3(Attitude towards HIV/AIDs issues) Beta0, Beta1, Beta2 and Beta3 are respectively 14.201, 2.038, 0.148 and 0.486; the higher the better. Attitude and behavior change education on HIV/AIDs should be intensified in the institution so that students could adopt better lifestyles.

Regression modeling of ground-water flow

USGS Publications Warehouse

Cooley, R.L.; Naff, R.L.

1985-01-01

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly

PubMed Central

2013-01-01

Background This study aims to improve accuracy of Bioelectrical Impedance Analysis (BIA) prediction equations for estimating fat free mass (FFM) of the elderly by using non-linear Back Propagation Artificial Neural Network (BP-ANN) model and to compare the predictive accuracy with the linear regression model by using energy dual X-ray absorptiometry (DXA) as reference method. Methods A total of 88 Taiwanese elderly adults were recruited in this study as subjects. Linear regression equations and BP-ANN prediction equation were developed using impedances and other anthropometrics for predicting the reference FFM measured by DXA (FFMDXA) in 36 male and 26 female Taiwanese elderly adults. The FFM estimated by BIA prediction equations using traditional linear regression model (FFMLR) and BP-ANN model (FFMANN) were compared to the FFMDXA. The measuring results of an additional 26 elderly adults were used to validate than accuracy of the predictive models. Results The results showed the significant predictors were impedance, gender, age, height and weight in developed FFMLR linear model (LR) for predicting FFM (coefficient of determination, r2 = 0.940; standard error of estimate (SEE) = 2.729 kg; root mean square error (RMSE) = 2.571kg, P < 0.001). The above predictors were set as the variables of the input layer by using five neurons in the BP-ANN model (r2 = 0.987 with a SD = 1.192 kg and relatively lower RMSE = 1.183 kg), which had greater (improved) accuracy for estimating FFM when compared with linear model. The results showed a better agreement existed between FFMANN and FFMDXA than that between FFMLR and FFMDXA. Conclusion When compared the performance of developed prediction equations for estimating reference FFMDXA, the linear model has lower r2 with a larger SD in predictive results than that of BP-ANN model, which indicated ANN model is more suitable for estimating FFM. PMID:23388042

SU-F-T-130: [18F]-FDG Uptake Dose Response in Lung Correlates Linearly with Proton Therapy Dose

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, D; Titt, U; Mirkovic, D

2016-06-15

Purpose: Analysis of clinical outcomes in lung cancer patients treated with protons using 18F-FDG uptake in lung as a measure of dose response. Methods: A test case lung cancer patient was selected in an unbiased way. The test patient’s treatment planning and post treatment positron emission tomography (PET) were collected from picture archiving and communication system at the UT M.D. Anderson Cancer Center. Average computerized tomography scan was registered with post PET/CT through both rigid and deformable registrations for selected region of interest (ROI) via VelocityAI imaging informatics software. For the voxels in the ROI, a system that extracts themore » Standard Uptake Value (SUV) from PET was developed, and the corresponding relative biological effectiveness (RBE) weighted (both variable and constant) dose was computed using the Monte Carlo (MC) methods. The treatment planning system (TPS) dose was also obtained. Using histogram analysis, the voxel average normalized SUV vs. 3 different doses was obtained and linear regression fit was performed. Results: From the registration process, there were some regions that showed significant artifacts near the diaphragm and heart region, which yielded poor r-squared values when the linear regression fit was performed on normalized SUV vs. dose. Excluding these values, TPS fit yielded mean r-squared value of 0.79 (range 0.61–0.95), constant RBE fit yielded 0.79 (range 0.52–0.94), and variable RBE fit yielded 0.80 (range 0.52–0.94). Conclusion: A system that extracts SUV from PET to correlate between normalized SUV and various dose calculations was developed. A linear relation between normalized SUV and all three different doses was found.« less

Predictive modeling of hazardous waste landfill total above-ground biomass using passive optical and LIDAR remotely sensed data

NASA Astrophysics Data System (ADS)

Hadley, Brian Christopher

This dissertation assessed remotely sensed data and geospatial modeling technique(s) to map the spatial distribution of total above-ground biomass present on the surface of the Savannah River National Laboratory's (SRNL) Mixed Waste Management Facility (MWMF) hazardous waste landfill. Ordinary least squares (OLS) regression, regression kriging, and tree-structured regression were employed to model the empirical relationship between in-situ measured Bahia (Paspalum notatum Flugge) and Centipede [Eremochloa ophiuroides (Munro) Hack.] grass biomass against an assortment of explanatory variables extracted from fine spatial resolution passive optical and LIDAR remotely sensed data. Explanatory variables included: (1) discrete channels of visible, near-infrared (NIR), and short-wave infrared (SWIR) reflectance, (2) spectral vegetation indices (SVI), (3) spectral mixture analysis (SMA) modeled fractions, (4) narrow-band derivative-based vegetation indices, and (5) LIDAR derived topographic variables (i.e. elevation, slope, and aspect). Results showed that a linear combination of the first- (1DZ_DGVI), second- (2DZ_DGVI), and third-derivative of green vegetation indices (3DZ_DGVI) calculated from hyperspectral data recorded over the 400--960 nm wavelengths of the electromagnetic spectrum explained the largest percentage of statistical variation (R2 = 0.5184) in the total above-ground biomass measurements. In general, the topographic variables did not correlate well with the MWMF biomass data, accounting for less than five percent of the statistical variation. It was concluded that tree-structured regression represented the optimum geospatial modeling technique due to a combination of model performance and efficiency/flexibility factors.

Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation

PubMed Central

MANEL, STÉPHANIE; GUGERLI, FELIX; THUILLER, WILFRIED; ALVAREZ, NADIR; LEGENDRE, PIERRE; HOLDEREGGER, ROLF; GIELLY, LUDOVIC; TABERLET, PIERRE

2014-01-01

Identifying adaptive genetic variation is a challenging task, in particular in non-model species for which genomic information is still limited or absent. Here, we studied distribution patterns of amplified fragment length polymorphisms (AFLPs) in response to environmental variation, in 13 alpine plant species consistently sampled across the entire European Alps. Multiple linear regressions were performed between AFLP allele frequencies per site as dependent variables and two categories of independent variables, namely Moran’s eigenvector map MEM variables (to account for spatial and unaccounted environmental variation, and historical demographic processes) and environmental variables. These associations allowed the identification of 153 loci of ecological relevance. Univariate regressions between allele frequency and each environmental factor further showed that loci of ecological relevance were mainly correlated with MEM variables. We found that precipitation and temperature were the best environmental predictors, whereas topographic factors were rarely involved in environmental associations. Climatic factors, subject to rapid variation as a result of the current global warming, are known to strongly influence the fate of alpine plants. Our study shows, for the first time for a large number of species, that the same environmental variables are drivers of plant adaptation at the scale of a whole biome, here the European Alps. PMID:22680783

Understanding multidecadal variability in ENSO amplitude

NASA Astrophysics Data System (ADS)

Russell, A.; Gnanadesikan, A.

2013-12-01

Sea surface temperatures (SSTs) in the tropical Pacific vary as a result of the coupling between the ocean and atmosphere driven largely by the El Niño - Southern Oscillation (ENSO). ENSO has a large impact on the local climate and hydrology of the tropical Pacific, as well as broad-reaching effects on global climate. ENSO amplitude is known to vary on long timescales, which makes it very difficult to quantify its response to climate change and constrain the physical processes that drive it. In order to assess the extent of unforced multidecadal changes in ENSO variability, a linear regression of local SST changes is applied to the GFDL CM2.1 model 4000-yr pre-industrial control run. The resulting regression coefficient strengths, which represent the sensitivity of SST changes to thermocline depth and zonal wind stress, vary by up to a factor of 2 on multi-decadal time scales. This long-term modulation in ocean-atmosphere coupling is highly correlated with ENSO variability, but do not explain the reasons for such variability. Variation in the relationship between SST changes and wind stress points to a role for changing stratification in the central equatorial Pacific in modulating ENSO amplitudes with stronger stratification reducing the response to winds. The main driving mechanism we have identified for higher ENSO variance are changes in the response of zonal winds to SST anomalies. The shifting convection and precipitation patterns associated with the changing state of the atmosphere also contribute to the variability of the regression coefficients. These mechanisms drive much of the variability in ENSO amplitude and hence ocean-atmosphere coupling in the tropical Pacific.

Trend Estimation and Regression Analysis in Climatological Time Series: An Application of Structural Time Series Models and the Kalman Filter.

NASA Astrophysics Data System (ADS)

Visser, H.; Molenaar, J.

1995-05-01

The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of the model is rather poor, and possible explanations are discussed.

Pseudo-second order models for the adsorption of safranin onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth

2007-04-02

Kinetic experiments were carried out for the sorption of safranin onto activated carbon particles. The kinetic data were fitted to pseudo-second order model of Ho, Sobkowsk and Czerwinski, Blanchard et al. and Ritchie by linear and non-linear regression methods. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo-second order models were the same. Non-linear regression analysis showed that both Blanchard et al. and Ho have similar ideas on the pseudo-second order model but with different assumptions. The best fit of experimental data in Ho's pseudo-second order expression by linear and non-linear regression method showed that Ho pseudo-second order model was a better kinetic expression when compared to other pseudo-second order kinetic expressions.

Changes in aerobic power of men, ages 25-70 yr

NASA Technical Reports Server (NTRS)

Jackson, A. S.; Beard, E. F.; Wier, L. T.; Ross, R. M.; Stuteville, J. E.; Blair, S. N.

1995-01-01

This study quantified and compared the cross-sectional and longitudinal influence of age, self-report physical activity (SR-PA), and body composition (%fat) on the decline of maximal aerobic power (VO2peak). The cross-sectional sample consisted of 1,499 healthy men ages 25-70 yr. The 156 men of the longitudinal sample were from the same population and examined twice, the mean time between tests was 4.1 (+/- 1.2) yr. Peak oxygen uptake was determined by indirect calorimetry during a maximal treadmill exercise test. The zero-order correlations between VO2peak and %fat (r = -0.62) and SR-PA (r = 0.58) were significantly (P < 0.05) higher that the age correlation (r = -0.45). Linear regression defined the cross-sectional age-related decline in VO2peak at 0.46 ml.kg-1.min-1.yr-1. Multiple regression analysis (R = 0.79) showed that nearly 50% of this cross-sectional decline was due to %fat and SR-PA, adding these lifestyle variables to the multiple regression model reduced the age regression weight to -0.26 ml.kg-1.min-1.yr-1. Statistically controlling for time differences between tests, general linear models analysis showed that longitudinal changes in aerobic power were due to independent changes in %fat and SR-PA, confirming the cross-sectional results.

Acoustic-articulatory mapping in vowels by locally weighted regression

PubMed Central

McGowan, Richard S.; Berger, Michael A.

2009-01-01

A method for mapping between simultaneously measured articulatory and acoustic data is proposed. The method uses principal components analysis on the articulatory and acoustic variables, and mapping between the domains by locally weighted linear regression, or loess [Cleveland, W. S. (1979). J. Am. Stat. Assoc. 74, 829–836]. The latter method permits local variation in the slopes of the linear regression, assuming that the function being approximated is smooth. The methodology is applied to vowels of four speakers in the Wisconsin X-ray Microbeam Speech Production Database, with formant analysis. Results are examined in terms of (1) examples of forward (articulation-to-acoustics) mappings and inverse mappings, (2) distributions of local slopes and constants, (3) examples of correlations among slopes and constants, (4) root-mean-square error, and (5) sensitivity of formant frequencies to articulatory change. It is shown that the results are qualitatively correct and that loess performs better than global regression. The forward mappings show different root-mean-square error properties than the inverse mappings indicating that this method is better suited for the forward mappings than the inverse mappings, at least for the data chosen for the current study. Some preliminary results on sensitivity of the first two formant frequencies to the two most important articulatory principal components are presented. PMID:19813812

Multiplicative Forests for Continuous-Time Processes

PubMed Central

Weiss, Jeremy C.; Natarajan, Sriraam; Page, David

2013-01-01

Learning temporal dependencies between variables over continuous time is an important and challenging task. Continuous-time Bayesian networks effectively model such processes but are limited by the number of conditional intensity matrices, which grows exponentially in the number of parents per variable. We develop a partition-based representation using regression trees and forests whose parameter spaces grow linearly in the number of node splits. Using a multiplicative assumption we show how to update the forest likelihood in closed form, producing efficient model updates. Our results show multiplicative forests can be learned from few temporal trajectories with large gains in performance and scalability. PMID:25284967

Multiplicative Forests for Continuous-Time Processes.

PubMed

Weiss, Jeremy C; Natarajan, Sriraam; Page, David

2012-01-01

Learning temporal dependencies between variables over continuous time is an important and challenging task. Continuous-time Bayesian networks effectively model such processes but are limited by the number of conditional intensity matrices, which grows exponentially in the number of parents per variable. We develop a partition-based representation using regression trees and forests whose parameter spaces grow linearly in the number of node splits. Using a multiplicative assumption we show how to update the forest likelihood in closed form, producing efficient model updates. Our results show multiplicative forests can be learned from few temporal trajectories with large gains in performance and scalability.

Meteorological adjustment of yearly mean values for air pollutant concentration comparison

NASA Technical Reports Server (NTRS)

Sidik, S. M.; Neustadter, H. E.

1976-01-01

Using multiple linear regression analysis, models which estimate mean concentrations of Total Suspended Particulate (TSP), sulfur dioxide, and nitrogen dioxide as a function of several meteorologic variables, two rough economic indicators, and a simple trend in time are studied. Meteorologic data were obtained and do not include inversion heights. The goodness of fit of the estimated models is partially reflected by the squared coefficient of multiple correlation which indicates that, at the various sampling stations, the models accounted for about 23 to 47 percent of the total variance of the observed TSP concentrations. If the resulting model equations are used in place of simple overall means of the observed concentrations, there is about a 20 percent improvement in either: (1) predicting mean concentrations for specified meteorological conditions; or (2) adjusting successive yearly averages to allow for comparisons devoid of meteorological effects. An application to source identification is presented using regression coefficients of wind velocity predictor variables.

Jobs and the resource curse in the sun: The effects of oil production on female labor force participation in California counties from 1980-2010

NASA Astrophysics Data System (ADS)

Zavala, Gabriel

This study aims to evaluate the relationship between oil income and the female labor force participation rate in California for the years of 1980, 1990, 2000 and 2010 using panel linear regression models. This study also aims to visualize the spatial patterns of both variables in California through Hot Spot analysis at the county level for the same years. The regression found no sign of a relationship between oil income and female labor force participation rate but did find evidence of a positive relationship between two income control variables and the female labor force participation rate. The hot spot analysis also found that female labor force participation cold spots are not spatially correlated with oil production hot spots. These findings contribute new methodologies at a finer scale to the very nuanced discussion of the resource curse in the United States.

Linear models for calculating digestibile energy for sheep diets.

PubMed

Fonnesbeck, P V; Christiansen, M L; Harris, L E

1981-05-01

Equations for estimating the digestible energy (DE) content of sheep diets were generated from the chemical contents and a factorial description of diets fed to lambs in digestion trials. The diet factors were two forages (alfalfa and grass hay), harvested at three stages of maturity (late vegetative, early bloom and full bloom), fed in two ingredient combinations (all hay or a 50:50 hay and corn grain mixture) and prepared by two forage texture processes (coarsely chopped or finely chopped and pelleted). The 2 x 3 x 2 x 2 factorial arrangement produced 24 diet treatments. These were replicated twice, for a total of 48 lamb digestion trials. In model 1 regression equations, DE was calculated directly from chemical composition of the diet. In model 2, regression equations predicted the percentage of digested nutrient from the chemical contents of the diet and then DE of the diet was calculated as the sum of the gross energy of the digested organic components. Expanded forms of model 1 and model 2 were also developed that included diet factors as qualitative indicator variables to adjust the regression constant and regression coefficients for the diet description. The expanded forms of the equations accounted for significantly more variation in DE than did the simple models and more accurately estimated DE of the diet. Information provided by the diet description proved as useful as chemical analyses for the prediction of digestibility of nutrients. The statistics indicate that, with model 1, neutral detergent fiber and plant cell wall analyses provided as much information for the estimation of DE as did model 2 with the combined information from crude protein, available carbohydrate, total lipid, cellulose and hemicellulose. Regression equations are presented for estimating DE with the most currently analyzed organic components, including linear and curvilinear variables and diet factors that significantly reduce the standard error of the estimate. To estimate De of a diet, the user utilizes the equation that uses the chemical analysis information and diet description most effectively.

A Bayesian methodological framework for accommodating interannual variability of nutrient loading with the SPARROW model

NASA Astrophysics Data System (ADS)

Wellen, Christopher; Arhonditsis, George B.; Labencki, Tanya; Boyd, Duncan

2012-10-01

Regression-type, hybrid empirical/process-based models (e.g., SPARROW, PolFlow) have assumed a prominent role in efforts to estimate the sources and transport of nutrient pollution at river basin scales. However, almost no attempts have been made to explicitly accommodate interannual nutrient loading variability in their structure, despite empirical and theoretical evidence indicating that the associated source/sink processes are quite variable at annual timescales. In this study, we present two methodological approaches to accommodate interannual variability with the Spatially Referenced Regressions on Watershed attributes (SPARROW) nonlinear regression model. The first strategy uses the SPARROW model to estimate a static baseline load and climatic variables (e.g., precipitation) to drive the interannual variability. The second approach allows the source/sink processes within the SPARROW model to vary at annual timescales using dynamic parameter estimation techniques akin to those used in dynamic linear models. Model parameterization is founded upon Bayesian inference techniques that explicitly consider calibration data and model uncertainty. Our case study is the Hamilton Harbor watershed, a mixed agricultural and urban residential area located at the western end of Lake Ontario, Canada. Our analysis suggests that dynamic parameter estimation is the more parsimonious of the two strategies tested and can offer insights into the temporal structural changes associated with watershed functioning. Consistent with empirical and theoretical work, model estimated annual in-stream attenuation rates varied inversely with annual discharge. Estimated phosphorus source areas were concentrated near the receiving water body during years of high in-stream attenuation and dispersed along the main stems of the streams during years of low attenuation, suggesting that nutrient source areas are subject to interannual variability.

Methodology for the development of normative data for Spanish-speaking pediatric populations.

PubMed

Rivera, D; Arango-Lasprilla, J C

2017-01-01

To describe the methodology utilized to calculate reliability and the generation of norms for 10 neuropsychological tests for children in Spanish-speaking countries. The study sample consisted of over 4,373 healthy children from nine countries in Latin America (Chile, Cuba, Ecuador, Guatemala, Honduras, Mexico, Paraguay, Peru, and Puerto Rico) and Spain. Inclusion criteria for all countries were to have between 6 to 17 years of age, an Intelligence Quotient of≥80 on the Test of Non-Verbal Intelligence (TONI-2), and score of <19 on the Children's Depression Inventory. Participants completed 10 neuropsychological tests. Reliability and norms were calculated for all tests. Test-retest analysis showed excellent or good- reliability on all tests (r's>0.55; p's<0.001) except M-WCST perseverative errors whose coefficient magnitude was fair. All scores were normed using multiple linear regressions and standard deviations of residual values. Age, age2, sex, and mean level of parental education (MLPE) were included as predictors in the models by country. The non-significant variables (p > 0.05) were removed and the analysis were run again. This is the largest Spanish-speaking children and adolescents normative study in the world. For the generation of normative data, the method based on linear regression models and the standard deviation of residual values was used. This method allows determination of the specific variables that predict test scores, helps identify and control for collinearity of predictive variables, and generates continuous and more reliable norms than those of traditional methods.

Climate patterns as predictors of amphibians species richness and indicators of potential stress

USGS Publications Warehouse

Battaglin, W.; Hay, L.; McCabe, G.; Nanjappa, P.; Gallant, Alisa L.

2005-01-01

Amphibians occupy a range of habitats throughout the world, but species richness is greatest in regions with moist, warm climates. We modeled the statistical relations of anuran and urodele species richness with mean annual climate for the conterminous United States, and compared the strength of these relations at national and regional levels. Model variables were calculated for county and subcounty mapping units, and included 40-year (1960-1999) annual mean and mean annual climate statistics, mapping unit average elevation, mapping unit land area, and estimates of anuran and urodele species richness. Climate data were derived from more than 7,500 first-order and cooperative meteorological stations and were interpolated to the mapping units using multiple linear regression models. Anuran and urodele species richness were calculated from the United States Geological Survey's Amphibian Research and Monitoring Initiative (ARMI) National Atlas for Amphibian Distributions. The national multivariate linear regression (MLR) model of anuran species richness had an adjusted coefficient of determination (R2) value of 0.64 and the national MLR model for urodele species richness had an R2 value of 0.45. Stratifying the United States by coarse-resolution ecological regions provided models for anUrans that ranged in R2 values from 0.15 to 0.78. Regional models for urodeles had R2 values. ranging from 0.27 to 0.74. In general, regional models for anurans were more strongly influenced by temperature variables, whereas precipitation variables had a larger influence on urodele models.

Obesity and severe obesity forecasts through 2030.

PubMed

Finkelstein, Eric A; Khavjou, Olga A; Thompson, Hope; Trogdon, Justin G; Pan, Liping; Sherry, Bettylou; Dietz, William

2012-06-01

Previous efforts to forecast future trends in obesity applied linear forecasts assuming that the rise in obesity would continue unabated. However, evidence suggests that obesity prevalence may be leveling off. This study presents estimates of adult obesity and severe obesity prevalence through 2030 based on nonlinear regression models. The forecasted results are then used to simulate the savings that could be achieved through modestly successful obesity prevention efforts. The study was conducted in 2009-2010 and used data from the 1990 through 2008 Behavioral Risk Factor Surveillance System (BRFSS). The analysis sample included nonpregnant adults aged ≥ 18 years. The individual-level BRFSS variables were supplemented with state-level variables from the U.S. Bureau of Labor Statistics, the American Chamber of Commerce Research Association, and the Census of Retail Trade. Future obesity and severe obesity prevalence were estimated through regression modeling by projecting trends in explanatory variables expected to influence obesity prevalence. Linear time trend forecasts suggest that by 2030, 51% of the population will be obese. The model estimates a much lower obesity prevalence of 42% and severe obesity prevalence of 11%. If obesity were to remain at 2010 levels, the combined savings in medical expenditures over the next 2 decades would be $549.5 billion. The study estimates a 33% increase in obesity prevalence and a 130% increase in severe obesity prevalence over the next 2 decades. If these forecasts prove accurate, this will further hinder efforts for healthcare cost containment. Copyright © 2012 Elsevier Inc. All rights reserved.

Impact of sociodemographic variables on executive functions.

PubMed

Campanholo, Kenia Repiso; Boa, Izadora Nogueira Fonte; Hodroj, Flávia Cristina da Silva Araujo; Guerra, Glaucia Rosana Benute; Miotto, Eliane Correa; de Lucia, Mara Cristina Souza

2017-01-01

Executive functions (EFs) regulate human behavior and allow individuals to interact and act in the world. EFs are sensitive to sociodemographic variables such as age, which promotes their decline, and to others that can exert a neuroprotective effect. To assess the predictive role of education, occupation and family income on decline in executive functions among a sample with a wide age range. A total of 925 participants aged 18-89 years with 1-28 years' education were submitted to assessment of executive functions using the Card Sorting Test (CST), Phonemic Verbal Fluency (FAS) Task and Semantic Verbal Fluency (SVF) Task. Data on income, occupation and educational level were collected for the sample. The data were analyzed using Linear Regression, as well as Pearson's and Spearman's Correlation. Age showed a significant negative correlation (p<0.001) with performance on the CST, FAS and SVF, whereas education, income and occupation were positively associated (p<0.001) with the tasks applied. After application of the multivariate linear regression model, a significant positive relationship with the FAS was maintained only for education (p<0.001) and income (p<0.001). The negative relationship of age (p<0.001) and positive relationship of both education (p<0.001) and income (p<0.001and p=0.003) were evident on the CST and SVF. Educational level and income positively influenced participants' results on executive function tests, attenuating expected decline for age. However, no relationship was found between occupation and the cognitive variables investigated.

Impact of sociodemographic variables on executive functions

PubMed Central

Campanholo, Kenia Repiso; Boa, Izadora Nogueira Fonte; Hodroj, Flávia Cristina da Silva Araujo; Guerra, Glaucia Rosana Benute; Miotto, Eliane Correa; de Lucia, Mara Cristina Souza

2017-01-01

Executive functions (EFs) regulate human behavior and allow individuals to interact and act in the world. EFs are sensitive to sociodemographic variables such as age, which promotes their decline, and to others that can exert a neuroprotective effect. Objective To assess the predictive role of education, occupation and family income on decline in executive functions among a sample with a wide age range. Methods A total of 925 participants aged 18-89 years with 1-28 years' education were submitted to assessment of executive functions using the Card Sorting Test (CST), Phonemic Verbal Fluency (FAS) Task and Semantic Verbal Fluency (SVF) Task. Data on income, occupation and educational level were collected for the sample. The data were analyzed using Linear Regression, as well as Pearson's and Spearman's Correlation. Results Age showed a significant negative correlation (p<0.001) with performance on the CST, FAS and SVF, whereas education, income and occupation were positively associated (p<0.001) with the tasks applied. After application of the multivariate linear regression model, a significant positive relationship with the FAS was maintained only for education (p<0.001) and income (p<0.001). The negative relationship of age (p<0.001) and positive relationship of both education (p<0.001) and income (p<0.001and p=0.003) were evident on the CST and SVF. Conclusion Educational level and income positively influenced participants' results on executive function tests, attenuating expected decline for age. However, no relationship was found between occupation and the cognitive variables investigated. PMID:29213495

[Mental Health Determines the Quality of Life in Patients With Cancer-Related Neuropathic Pain in Quito, Ecuador].

PubMed

Gordillo Altamirano, Fernando; Fierro Torres, María José; Cevallos Salas, Nelson; Cervantes Vélez, María Cristina

To identify the main factors determining the health related quality of life (HRQL) in patients with cancer-related neuropathic pain in a tertiary care hospital. A cross-sectional analytical study was performed on a sample of 237 patients meeting criteria for cancer-related neuropathic pain. Clinical and demographic variables were recorded including, cancer type, stage, time since diagnosis, pain intensity, physical functionality with the Palliative Performance Scale (PPS), and anxiety and depression with the Hospital Anxiety and Depression Scale (HADS). Their respective correlation coefficients (r) with HRQL assessed with the SF-36v2 Questionnaire were then calculated. Linear regression equations were then constructed with the variables that showed an r≥.5 with the HRQL. The HRQL scores of the sample were 39.3±9.1 (Physical Component) and 45.5±13.8 (Mental Component). Anxiety and depression strongly correlated with the mental component (r=-.641 and r=-.741, respectively) while PPS score correlated with the physical component (r=.617). The linear regression model that better explained the variance of the mental component was designed combining the Anxiety and Depression variables (R=77.3%; P<.001). The strong influence of psychiatric comorbidity on the HRQL of patients with cancer-related neuropathic pain makes an integral management plan essential for these patients to include interventions for its timely diagnosis and treatment. Copyright © 2016 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

A strategy for simultaneous determination of fatty acid composition, fatty acid position, and position-specific isotope contents in triacylglycerol matrices by 13C-NMR.

PubMed

Merchak, Noelle; Silvestre, Virginie; Loquet, Denis; Rizk, Toufic; Akoka, Serge; Bejjani, Joseph

2017-01-01

Triacylglycerols, which are quasi-universal components of food matrices, consist of complex mixtures of molecules. Their site-specific 13 C content, their fatty acid profile, and their position on the glycerol moiety may significantly vary with the geographical, botanical, or animal origin of the sample. Such variables are valuable tracers for food authentication issues. The main objective of this work was to develop a new method based on a rapid and precise 13 C-NMR spectroscopy (using a polarization transfer technique) coupled with multivariate linear regression analyses in order to quantify the whole set of individual fatty acids within triacylglycerols. In this respect, olive oil samples were analyzed by means of both adiabatic 13 C-INEPT sequence and gas chromatography (GC). For each fatty acid within the studied matrix and for squalene as well, a multivariate prediction model was constructed using the deconvoluted peak areas of 13 C-INEPT spectra as predictors, and the data obtained by GC as response variables. This 13 C-NMR-based strategy, tested on olive oil, could serve as an alternative to the gas chromatographic quantification of individual fatty acids in other matrices, while providing additional compositional and isotopic information. Graphical abstract A strategy based on the multivariate linear regression of variables obtained by a rapid 13 C-NMR technique was developed for the quantification of individual fatty acids within triacylglycerol matrices. The conceived strategy was tested on olive oil.

Exploratory study of the association between insight and Theory of Mind (ToM) in stable schizophrenia patients.

PubMed

Pousa, Esther; Duñó, Rosó; Blas Navarro, J; Ruiz, Ada I; Obiols, Jordi E; David, Anthony S

2008-05-01

Poor insight and impairment in Theory of Mind (ToM) reasoning are common in schizophrenia, predicting poorer clinical and functional outcomes. The present study aimed to explore the relationship between these phenomena. 61 individuals with a DSM-IV diagnosis of schizophrenia during a stable phase were included. ToM was assessed using a picture sequencing task developed by Langdon and Coltheart (1999), and insight with the Scale to Assess Unawareness of Mental Disorder (SUMD; Amador et al., 1993). Multivariate linear regression analysis was carried out to estimate the predictive value of insight on ToM, taking into account several possible confounders and interaction variables. No direct significant associations were found between any of the insight dimensions and ToM using bivariate analysis. However, a significant linear regression model which explained 48% of the variance in ToM was revealed in the multivariate analysis. This included the 5 insight dimensions and 3 interaction variables. Misattribution of symptoms--in aware patients with age at onset >20 years--and unawareness of need for medication--in patients with GAF >60--were significantly predictive of better ToM. Insight and ToM are two complex and distinct phenomena in schizophrenia. Relationships between them are mediated by psychosocial, clinical, and neurocognitive variables. Intact ToM may be a prerequisite for aware patients to attribute their symptoms to causes other than mental illness, which could in turn be associated with denial of need for medication.

Estuarine Sediment Deposition during Wetland Restoration: A GIS and Remote Sensing Modeling Approach

NASA Technical Reports Server (NTRS)

Newcomer, Michelle; Kuss, Amber; Kentron, Tyler; Remar, Alex; Choksi, Vivek; Skiles, J. W.

2011-01-01

Restoration of the industrial salt flats in the San Francisco Bay, California is an ongoing wetland rehabilitation project. Remote sensing maps of suspended sediment concentration, and other GIS predictor variables were used to model sediment deposition within these recently restored ponds. Suspended sediment concentrations were calibrated to reflectance values from Landsat TM 5 and ASTER using three statistical techniques -- linear regression, multivariate regression, and an Artificial Neural Network (ANN), to map suspended sediment concentrations. Multivariate and ANN regressions using ASTER proved to be the most accurate methods, yielding r2 values of 0.88 and 0.87, respectively. Predictor variables such as sediment grain size and tidal frequency were used in the Marsh Sedimentation (MARSED) model for predicting deposition rates for three years. MARSED results for a fully restored pond show a root mean square deviation (RMSD) of 66.8 mm (<1) between modeled and field observations. This model was further applied to a pond breached in November 2010 and indicated that the recently breached pond will reach equilibrium levels after 60 months of tidal inundation.

Regression equations for disinfection by-products for the Mississippi, Ohio and Missouri rivers

USGS Publications Warehouse

Rathbun, R.E.

1996-01-01

Trihalomethane and nonpurgeable total organic-halide formation potentials were determined for the chlorination of water samples from the Mississippi, Ohio and Missouri Rivers. Samples were collected during the summer and fall of 1991 and the spring of 1992 at twelve locations on the Mississippi from New Orleans to Minneapolis, and on the Ohio and Missouri 1.6 km upstream from their confluences with the Mississippi. Formation potentials were determined as a function of pH, initial free-chlorine concentration, and reaction time. Multiple linear regression analysis of the data indicated that pH, reaction time, and the dissolved organic carbon concentration and/or the ultraviolet absorbance of the water were the most significant variables. The initial free-chlorine concentration had less significance and bromide concentration had little or no significance. Analysis of combinations of the dissolved organic carbon concentration and the ultraviolet absorbance indicated that use of the ultraviolet absorbance alone provided the best prediction of the experimental data. Regression coefficients for the variables were generally comparable to coefficients previously presented in the literature for waters from other parts of the United States.

Comparison of machine-learning methods for above-ground biomass estimation based on Landsat imagery

NASA Astrophysics Data System (ADS)

Wu, Chaofan; Shen, Huanhuan; Shen, Aihua; Deng, Jinsong; Gan, Muye; Zhu, Jinxia; Xu, Hongwei; Wang, Ke

2016-07-01

Biomass is one significant biophysical parameter of a forest ecosystem, and accurate biomass estimation on the regional scale provides important information for carbon-cycle investigation and sustainable forest management. In this study, Landsat satellite imagery data combined with field-based measurements were integrated through comparisons of five regression approaches [stepwise linear regression, K-nearest neighbor, support vector regression, random forest (RF), and stochastic gradient boosting] with two different candidate variable strategies to implement the optimal spatial above-ground biomass (AGB) estimation. The results suggested that RF algorithm exhibited the best performance by 10-fold cross-validation with respect to R2 (0.63) and root-mean-square error (26.44 ton/ha). Consequently, the map of estimated AGB was generated with a mean value of 89.34 ton/ha in northwestern Zhejiang Province, China, with a similar pattern to the distribution mode of local forest species. This research indicates that machine-learning approaches associated with Landsat imagery provide an economical way for biomass estimation. Moreover, ensemble methods using all candidate variables, especially for Landsat images, provide an alternative for regional biomass simulation.

Estimating the impact of mineral aerosols on crop yields in food insecure regions using statistical crop models

NASA Astrophysics Data System (ADS)

Hoffman, A.; Forest, C. E.; Kemanian, A.

2016-12-01

A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with traditional crop models, but likely at the cost of removing climate information. Our random forest models consistently discover the positive trend without removing any additional data. The application of random forests as a statistical crop model provides insight into understanding the impact of dust on yields in marginal food producing regions.

Modeling brook trout presence and absence from landscape variables using four different analytical methods

USGS Publications Warehouse

Steen, Paul J.; Passino-Reader, Dora R.; Wiley, Michael J.

2006-01-01

As a part of the Great Lakes Regional Aquatic Gap Analysis Project, we evaluated methodologies for modeling associations between fish species and habitat characteristics at a landscape scale. To do this, we created brook trout Salvelinus fontinalis presence and absence models based on four different techniques: multiple linear regression, logistic regression, neural networks, and classification trees. The models were tested in two ways: by application to an independent validation database and cross-validation using the training data, and by visual comparison of statewide distribution maps with historically recorded occurrences from the Michigan Fish Atlas. Although differences in the accuracy of our models were slight, the logistic regression model predicted with the least error, followed by multiple regression, then classification trees, then the neural networks. These models will provide natural resource managers a way to identify habitats requiring protection for the conservation of fish species.

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

PubMed

Anderson, Carl A; McRae, Allan F; Visscher, Peter M

2006-07-01

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

Regression Models For Multivariate Count Data

PubMed Central

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2016-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data. PMID:28348500

Regression Models For Multivariate Count Data.

PubMed

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

Catalog of Air Force Weather Technical Documents, 1941-2006

DTIC Science & Technology

2006-05-19

radiosondes in current use in USA. Elementary discussion of statistical terms and concepts used for expressing accuracy or error is discussed. AWS TR 105...Techniques, Appendix B: Vorticity—An Elementary Discussion of the Concept, August 1956, 27pp. Formerly AWSM 105– 50/1A. Provides the necessary back...steps involved in ordinary multiple linear regression. Conditional probability is calculated using transnormalized variables in the multivariate normal

Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery

NASA Astrophysics Data System (ADS)

Beguet, Benoit; Guyon, Dominique; Boukir, Samia; Chehata, Nesrine

2014-10-01

The main goal of this study is to design a method to describe the structure of forest stands from Very High Resolution satellite imagery, relying on some typical variables such as crown diameter, tree height, trunk diameter, tree density and tree spacing. The emphasis is placed on the automatization of the process of identification of the most relevant image features for the forest structure retrieval task, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick's texture features. The main drawback of this well-known texture representation is the underlying parameters which are extremely difficult to set due to the spatial complexity of the forest structure. To tackle this major issue, an automated feature selection process is proposed which is based on statistical modeling, exploring a wide range of parameter values. It provides texture measures of diverse spatial parameters hence implicitly inducing a multi-scale texture analysis. A new feature selection technique, we called Random PRiF, is proposed. It relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables. Our automated forest variable estimation scheme was tested on Quickbird and Pléiades panchromatic and multispectral images, acquired at different periods on the maritime pine stands of two sites in South-Western France. It outperforms two well-established variable subset selection techniques. It has been successfully applied to identify the best texture features in modeling the five considered forest structure variables. The RMSE of all predicted forest variables is improved by combining multispectral and panchromatic texture features, with various parameterizations, highlighting the potential of a multi-resolution approach for retrieving forest structure variables from VHR satellite images. Thus an average prediction error of ˜ 1.1 m is expected on crown diameter, ˜ 0.9 m on tree spacing, ˜ 3 m on height and ˜ 0.06 m on diameter at breast height.

Patterns of shading tolerance determined from experimental ...

EPA Pesticide Factsheets

An extensive review of the experimental literature on seagrass shading evaluated the relationship between experimental light reductions, duration of experiment and seagrass response metrics to determine whether there were consistent statistical patterns. There were highly significant linear relationships of both percent biomass and percent shoot density reduction versus percent light reduction (versus controls), although unexplained variation in the data were high. Duration of exposure affected extent of response for both metrics, but was more clearly a factor in biomass response. Both biomass and shoot density showed linear responses to duration of light reduction for treatments 60%. Unexplained variation was again high, and greater for shoot density than biomass. With few exceptions, regressions of both biomass and shoot density on light reduction for individual species and for genera were statistically significant, but also tended to show high degrees of variability in data. Multivariate regressions that included both percent light reduction and duration of reduction as dependent variables increased the percentage of variation explained in almost every case. Analysis of response data by seagrass life history category (Colonizing, Opportunistic, Persistent) did not yield clearly separate response relationships in most cases. Biomass tended to show somewhat less variation in response to light reduction than shoot density, and of the two, may be the prefe

Decisional balance and self-efficacy of physical activity among the elderly in Rasht in 2013 based on the transtheoretical model

PubMed Central

Abbaspour, Seddigheh; Farmanbar, Rabiollah; Njafi, Fateme; Ghiasvand, Arezoo Mohamadkhani; Dehghankar, Leila

2017-01-01

Background Regular physical activity has been considered as health promotion, and identifying different effective psycho-social variables on physical has proven to be essential. Objective To identify the relationship between decisional balance and self-efficacy in physical activities using the transtheoretical model in the members of a retirement center in Rasht, Guillen. Methods A descriptive cross-sectional study was conducted in 2013 by using convenient sampling on 262 elderly people who are the members of retirement centers in Rasht. Data were collected using Stages of change, Decisional balance, Self-efficacy and Physical Activity Scale for the Elderly (PASE). Data was analyzed using SPSS-16 software, descriptive and analytic statistic (Pearson correlation, Spearman, ANOVA, HSD Tukey, linear and ordinal regression). Results The majority of participants were in maintenance stage. Mean and standard deviation physical activity for the elderly was 119.35±51.50. Stages of change and physical activities were significantly associated with decisional balance and self-efficacy (p<0.0001); however, cons had a significant and reverse association. According to linear and ordinal regression the only predicator variable of physical activity behavior was self-efficacy. Conclusion By increase in pros and self-efficacy on doing physical activity, it can be benefited in designing appropriate intervention programs. PMID:28713520

Demographic and clinical features related to perceived discrimination in schizophrenia.

PubMed

Fresán, Ana; Robles-García, Rebeca; Madrigal, Eduardo; Tovilla-Zarate, Carlos-Alfonso; Martínez-López, Nicolás; Arango de Montis, Iván

2018-04-01

Perceived discrimination contributes to the development of internalized stigma among those with schizophrenia. Evidence on demographic and clinical factors related to the perception of discrimination among this population is both contradictory and scarce in low- and middle-income countries. Accordingly, the main purpose of this study is to determine the demographic and clinical factors predicting the perception of discrimination among Mexican patients with schizophrenia. Two hundred and seventeen adults with paranoid schizophrenia completed an interview on their demographic status and clinical characteristics. Symptom severity was assessed using the Positive and Negative Syndrome Scale; and perceived discrimination using 13 items from the King's Internalized Stigma Scale. Bivariate linear associations were determined to identify the variables of interest to be included in a linear regression analysis. Years of education, age of illness onset and length of hospitalization were associated with discrimination. However, only age of illness onset and length of hospitalization emerged as predictors of perceived discrimination in the final regression analysis, with longer length of hospitalization being the independent variable with the greatest contribution. Fortunately, this is a modifiable factor regarding the perception of discrimination and self-stigma. Strategies for achieving this as part of community-based mental health care are also discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Linear regression crash prediction models : issues and proposed solutions.

DOT National Transportation Integrated Search

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

Bayesian Group Bridge for Bi-level Variable Selection.

PubMed

Mallick, Himel; Yi, Nengjun

2017-06-01

A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.

Comparison between Linear and Nonlinear Regression in a Laboratory Heat Transfer Experiment

ERIC Educational Resources Information Center

Gonçalves, Carine Messias; Schwaab, Marcio; Pinto, José Carlos

2013-01-01

In order to interpret laboratory experimental data, undergraduate students are used to perform linear regression through linearized versions of nonlinear models. However, the use of linearized models can lead to statistically biased parameter estimates. Even so, it is not an easy task to introduce nonlinear regression and show for the students…