Science.gov

Sample records for addition multiple regression

  1. A multiple additive regression tree analysis of three exposure measures during Hurricane Katrina.

    PubMed

    Curtis, Andrew; Li, Bin; Marx, Brian D; Mills, Jacqueline W; Pine, John

    2011-01-01

    This paper analyses structural and personal exposure to Hurricane Katrina. Structural exposure is measured by flood height and building damage; personal exposure is measured by the locations of 911 calls made during the response. Using these variables, this paper characterises the geography of exposure and also demonstrates the utility of a robust analytical approach in understanding health-related challenges to disadvantaged populations during recovery. Analysis is conducted using a contemporary statistical approach, a multiple additive regression tree (MART), which displays considerable improvement over traditional regression analysis. By using MART, the percentage of improvement in R-squares over standard multiple linear regression ranges from about 62 to more than 100 per cent. The most revealing finding is the modelled verification that African Americans experienced disproportionate exposure in both structural and personal contexts. Given the impact of exposure to health outcomes, this finding has implications for understanding the long-term health challenges facing this population.

  2. 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.

  3. Multiple Regression and Its Discontents

    ERIC Educational Resources Information Center

    Snell, Joel C.; Marsh, Mitchell

    2012-01-01

    Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.

  4. Multiple Regression: A Leisurely Primer.

    ERIC Educational Resources Information Center

    Daniel, Larry G.; Onwuegbuzie, Anthony J.

    Multiple regression is a useful statistical technique when the researcher is considering situations in which variables of interest are theorized to be multiply caused. It may also be useful in those situations in which the researchers is interested in studies of predictability of phenomena of interest. This paper provides an introduction to…

  5. Use of Multiple Correlation Analysis and Multiple Regression Analysis.

    ERIC Educational Resources Information Center

    Huberty, Carl J.; Petoskey, Martha D.

    1999-01-01

    Distinguishes between multiple correlation and multiple regression analysis. Illustrates suggested information reporting methods and reviews the use of regression methods when dealing with problems of missing data. (SK)

  6. Some Simple Computational Formulas for Multiple Regression

    ERIC Educational Resources Information Center

    Aiken, Lewis R., Jr.

    1974-01-01

    Short-cut formulas are presented for direct computation of the beta weights, the standard errors of the beta weights, and the multiple correlation coefficient for multiple regression problems involving three independent variables and one dependent variable. (Author)

  7. Multiple Instance Regression with Structured Data

    NASA Technical Reports Server (NTRS)

    Wagstaff, Kiri L.; Lane, Terran; Roper, Alex

    2008-01-01

    This slide presentation reviews the use of multiple instance regression with structured data from multiple and related data sets. It applies the concept to a practical problem, that of estimating crop yield using remote sensed country wide weekly observations.

  8. Practical Session: Multiple Linear Regression

    NASA Astrophysics Data System (ADS)

    Clausel, M.; Grégoire, G.

    2014-12-01

    Three exercises are proposed to illustrate the simple linear regression. In the first one investigates the influence of several factors on atmospheric pollution. It has been proposed by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr33.pdf) and is based on data coming from 20 cities of U.S. Exercise 2 is an introduction to model selection whereas Exercise 3 provides a first example of analysis of variance. Exercises 2 and 3 have been proposed by A. Dalalyan at ENPC (see Exercises 2 and 3 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_5.pdf).

  9. Subgroup finding via Bayesian additive regression trees.

    PubMed

    Sivaganesan, Siva; Müller, Peter; Huang, Bin

    2017-03-09

    We provide a Bayesian decision theoretic approach to finding subgroups that have elevated treatment effects. Our approach separates the modeling of the response variable from the task of subgroup finding and allows a flexible modeling of the response variable irrespective of potential subgroups of interest. We use Bayesian additive regression trees to model the response variable and use a utility function defined in terms of a candidate subgroup and the predicted response for that subgroup. Subgroups are identified by maximizing the expected utility where the expectation is taken with respect to the posterior predictive distribution of the response, and the maximization is carried out over an a priori specified set of candidate subgroups. Our approach allows subgroups based on both quantitative and categorical covariates. We illustrate the approach using simulated data set study and a real data set. Copyright © 2017 John Wiley & Sons, Ltd.

  10. The Geometry of Enhancement in Multiple Regression

    ERIC Educational Resources Information Center

    Waller, Niels G.

    2011-01-01

    In linear multiple regression, "enhancement" is said to occur when R[superscript 2] = b[prime]r greater than r[prime]r, where b is a p x 1 vector of standardized regression coefficients and r is a p x 1 vector of correlations between a criterion y and a set of standardized regressors, x. When p = 1 then b [is congruent to] r and…

  11. Multiple-Instance Regression with Structured Data

    NASA Technical Reports Server (NTRS)

    Wagstaff, Kiri L.; Lane, Terran; Roper, Alex

    2008-01-01

    We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.

  12. Computing aspects of power for multiple regression.

    PubMed

    Dunlap, William P; Xin, Xue; Myers, Leann

    2004-11-01

    Rules of thumb for power in multiple regression research abound. Most such rules dictate the necessary sample size, but they are based only upon the number of predictor variables, usually ignoring other critical factors necessary to compute power accurately. Other guides to power in multiple regression typically use approximate rather than precise equations for the underlying distribution; entail complex preparatory computations; require interpolation with tabular presentation formats; run only under software such as Mathmatica or SAS that may not be immediately available to the user; or are sold to the user as parts of power computation packages. In contrast, the program we offer herein is immediately downloadable at no charge, runs under Windows, is interactive, self-explanatory, flexible to fit the user's own regression problems, and is as accurate as single precision computation ordinarily permits.

  13. Categorical Variables in Multiple Regression: Some Cautions.

    ERIC Educational Resources Information Center

    O'Grady, Kevin E.; Medoff, Deborah R.

    1988-01-01

    Limitations of dummy coding and nonsense coding as methods of coding categorical variables for use as predictors in multiple regression analysis are discussed. The combination of these approaches often yields estimates and tests of significance that are not intended by researchers for inclusion in their models. (SLD)

  14. Assumptions of Multiple Regression: Correcting Two Misconceptions

    ERIC Educational Resources Information Center

    Williams, Matt N.; Gomez Grajales, Carlos Alberto; Kurkiewicz, Dason

    2013-01-01

    In 2002, an article entitled "Four assumptions of multiple regression that researchers should always test" by Osborne and Waters was published in "PARE." This article has gone on to be viewed more than 275,000 times (as of August 2013), and it is one of the first results displayed in a Google search for "regression…

  15. Salience Assignment for Multiple-Instance Regression

    NASA Technical Reports Server (NTRS)

    Wagstaff, Kiri L.; Lane, Terran

    2007-01-01

    We present a Multiple-Instance Learning (MIL) algorithm for determining the salience of each item in each bag with respect to the bag's real-valued label. We use an alternating-projections constrained optimization approach to simultaneously learn a regression model and estimate all salience values. We evaluate this algorithm on a significant real-world problem, crop yield modeling, and demonstrate that it provides more extensive, intuitive, and stable salience models than Primary-Instance Regression, which selects a single relevant item from each bag.

  16. Multiple linear regression for isotopic measurements

    NASA Astrophysics Data System (ADS)

    Garcia Alonso, J. I.

    2012-04-01

    There are two typical applications of isotopic measurements: the detection of natural variations in isotopic systems and the detection man-made variations using enriched isotopes as indicators. For both type of measurements accurate and precise isotope ratio measurements are required. For the so-called non-traditional stable isotopes, multicollector ICP-MS instruments are usually applied. In many cases, chemical separation procedures are required before accurate isotope measurements can be performed. The off-line separation of Rb and Sr or Nd and Sm is the classical procedure employed to eliminate isobaric interferences before multicollector ICP-MS measurement of Sr and Nd isotope ratios. Also, this procedure allows matrix separation for precise and accurate Sr and Nd isotope ratios to be obtained. In our laboratory we have evaluated the separation of Rb-Sr and Nd-Sm isobars by liquid chromatography and on-line multicollector ICP-MS detection. The combination of this chromatographic procedure with multiple linear regression of the raw chromatographic data resulted in Sr and Nd isotope ratios with precisions and accuracies typical of off-line sample preparation procedures. On the other hand, methods for the labelling of individual organisms (such as a given plant, fish or animal) are required for population studies. We have developed a dual isotope labelling procedure which can be unique for a given individual, can be inherited in living organisms and it is stable. The detection of the isotopic signature is based also on multiple linear regression. The labelling of fish and its detection in otoliths by Laser Ablation ICP-MS will be discussed using trout and salmon as examples. As a conclusion, isotope measurement procedures based on multiple linear regression can be a viable alternative in multicollector ICP-MS measurements.

  17. Direction of Effects in Multiple Linear Regression Models.

    PubMed

    Wiedermann, Wolfgang; von Eye, Alexander

    2015-01-01

    Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.

  18. Relationship between Multiple Regression and Selected Multivariable Methods.

    ERIC Educational Resources Information Center

    Schumacker, Randall E.

    The relationship of multiple linear regression to various multivariate statistical techniques is discussed. The importance of the standardized partial regression coefficient (beta weight) in multiple linear regression as it is applied in path, factor, LISREL, and discriminant analyses is emphasized. The multivariate methods discussed in this paper…

  19. Suppression Situations in Multiple Linear Regression

    ERIC Educational Resources Information Center

    Shieh, Gwowen

    2006-01-01

    This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

  20. A Constrained Linear Estimator for Multiple Regression

    ERIC Educational Resources Information Center

    Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.

    2010-01-01

    "Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…

  1. Sample Sizes when Using Multiple Linear Regression for Prediction

    ERIC Educational Resources Information Center

    Knofczynski, Gregory T.; Mundfrom, Daniel

    2008-01-01

    When using multiple regression for prediction purposes, the issue of minimum required sample size often needs to be addressed. Using a Monte Carlo simulation, models with varying numbers of independent variables were examined and minimum sample sizes were determined for multiple scenarios at each number of independent variables. The scenarios…

  2. Enhance-Synergism and Suppression Effects in Multiple Regression

    ERIC Educational Resources Information Center

    Lipovetsky, Stan; Conklin, W. Michael

    2004-01-01

    Relations between pairwise correlations and the coefficient of multiple determination in regression analysis are considered. The conditions for the occurrence of enhance-synergism and suppression effects when multiple determination becomes bigger than the total of squared correlations of the dependent variable with the regressors are discussed. It…

  3. A Multiple Regression Approach to Normalization of Spatiotemporal Gait Features.

    PubMed

    Wahid, Ferdous; Begg, Rezaul; Lythgo, Noel; Hass, Chris J; Halgamuge, Saman; Ackland, David C

    2016-04-01

    Normalization of gait data is performed to reduce the effects of intersubject variations due to physical characteristics. This study reports a multiple regression normalization approach for spatiotemporal gait data that takes into account intersubject variations in self-selected walking speed and physical properties including age, height, body mass, and sex. Spatiotemporal gait data including stride length, cadence, stance time, double support time, and stride time were obtained from healthy subjects including 782 children, 71 adults, 29 elderly subjects, and 28 elderly Parkinson's disease (PD) patients. Data were normalized using standard dimensionless equations, a detrending method, and a multiple regression approach. After normalization using dimensionless equations and the detrending method, weak to moderate correlations between walking speed, physical properties, and spatiotemporal gait features were observed (0.01 < |r| < 0.88), whereas normalization using the multiple regression method reduced these correlations to weak values (|r| <0.29). Data normalization using dimensionless equations and detrending resulted in significant differences in stride length and double support time of PD patients; however the multiple regression approach revealed significant differences in these features as well as in cadence, stance time, and stride time. The proposed multiple regression normalization may be useful in machine learning, gait classification, and clinical evaluation of pathological gait patterns.

  4. Hierarchical regression for epidemiologic analyses of multiple exposures

    SciTech Connect

    Greenland, S.

    1994-11-01

    Many epidemiologic investigations are designed to study the effects of multiple exposures. Most of these studies are analyzed either by fitting a risk-regression model with all exposures forced in the model, or by using a preliminary-testing algorithm, such as stepwise regression, to produce a smaller model. Research indicates that hierarchical modeling methods can outperform these conventional approaches. These methods are reviewed and compared to two hierarchical methods, empirical-Bayes regression and a variant here called {open_quotes}semi-Bayes{close_quotes} regression, to full-model maximum likelihood and to model reduction by preliminary testing. The performance of the methods in a problem of predicting neonatal-mortality rates are compared. Based on the literature to date, it is suggested that hierarchical methods should become part of the standard approaches to multiple-exposure studies. 35 refs., 1 fig., 1 tab.

  5. Multiple regression analyses in clinical child and adolescent psychology.

    PubMed

    Jaccard, James; Guilamo-Ramos, Vincent; Johansson, Margaret; Bouris, Alida

    2006-09-01

    A major form of data analysis in clinical child and adolescent psychology is multiple regression. This article reviews issues in the application of such methods in light of the research designs typical of this field. Issues addressed include controlling covariates, evaluation of predictor relevance, comparing predictors, analysis of moderation, analysis of mediation, assumption violations, outliers, limited dependent variables, and directed regression and its relation to structural equation modeling. Analytic guidelines are provided within each domain.

  6. Norming clinical questionnaires with multiple regression: the Pain Cognition List.

    PubMed

    Van Breukelen, Gerard J P; Vlaeyen, Johan W S

    2005-09-01

    Questionnaires for measuring patients' feelings or beliefs are commonly used in clinical settings for diagnostic purposes, clinical decision making, or treatment evaluation. Raw scores of a patient can be evaluated by comparing them with norms based on a reference population. Using the Pain Cognition List (PCL-2003) as an example, this article shows how clinical questionnaires can be normed with multiple regression of raw scores on demographic and other patient variables. Compared with traditional norm tables for subgroups based on age or gender, this approach offers 2 advantages. First, multiple regression allows determination of which patient variables are relevant to the norming and which are not (validity). Second, by using information from the entire sample, multiple regression leads to continuous and more stable norms for any subgroup defined in terms of prognostic variables (reliability).

  7. Omnibus hypothesis testing in dominance-based ordinal multiple regression.

    PubMed

    Long, Jeffrey D

    2005-09-01

    Often quantitative data in the social sciences have only ordinal justification. Problems of interpretation can arise when least squares multiple regression (LSMR) is used with ordinal data. Two ordinal alternatives are discussed, dominance-based ordinal multiple regression (DOMR) and proportional odds multiple regression. The Q2 statistic is introduced for testing the omnibus null hypothesis in DOMR. A simulation study is discussed that examines the actual Type I error rate and power of Q2 in comparison to the LSMR omnibus F test under normality and non-normality. Results suggest that Q2 has favorable sampling properties as long as the sample size-to-predictors ratio is not too small, and Q2 can be a good alternative to the omnibus F test when the response variable is non-normal.

  8. Strategies for Identification and Detection of Outliers in Multiple Regression.

    ERIC Educational Resources Information Center

    Vannoy, Martha

    Outliers are frequently found in data sets and can cause problems for researchers if not addressed. Failure to identify and deal with outliers in an appropriate manner may lead researchers to report erroneous results. Using a multiple regression context, this paper examines some of the reasons for the presence of outliers and simple methods for…

  9. Multiple Regression Analyses in Clinical Child and Adolescent Psychology

    ERIC Educational Resources Information Center

    Jaccard, James; Guilamo-Ramos, Vincent; Johansson, Margaret; Bouris, Alida

    2006-01-01

    A major form of data analysis in clinical child and adolescent psychology is multiple regression. This article reviews issues in the application of such methods in light of the research designs typical of this field. Issues addressed include controlling covariates, evaluation of predictor relevance, comparing predictors, analysis of moderation,…

  10. Interpreting Multiple Linear Regression: A Guidebook of Variable Importance

    ERIC Educational Resources Information Center

    Nathans, Laura L.; Oswald, Frederick L.; Nimon, Kim

    2012-01-01

    Multiple regression (MR) analyses are commonly employed in social science fields. It is also common for interpretation of results to typically reflect overreliance on beta weights, often resulting in very limited interpretations of variable importance. It appears that few researchers employ other methods to obtain a fuller understanding of what…

  11. Developing Multiplicative Thinking from Additive Reasoning

    ERIC Educational Resources Information Center

    Tobias, Jennifer M.; Andreasen, Janet B.

    2013-01-01

    As students progress through elementary school, they encounter mathematics concepts that shift from additive to multiplicative situations (NCTM 2000). When they encounter fraction problems that require multiplicative thinking, they tend to incorrectly extend additive properties from whole numbers (Post et al. 1985). As a result, topics such as …

  12. Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.

    PubMed

    Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao

    2016-04-01

    To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.

  13. Multiple imputation for cure rate quantile regression with censored data.

    PubMed

    Wu, Yuanshan; Yin, Guosheng

    2017-03-01

    The main challenge in the context of cure rate analysis is that one never knows whether censored subjects are cured or uncured, or whether they are susceptible or insusceptible to the event of interest. Considering the susceptible indicator as missing data, we propose a multiple imputation approach to cure rate quantile regression for censored data with a survival fraction. We develop an iterative algorithm to estimate the conditionally uncured probability for each subject. By utilizing this estimated probability and Bernoulli sample imputation, we can classify each subject as cured or uncured, and then employ the locally weighted method to estimate the quantile regression coefficients with only the uncured subjects. Repeating the imputation procedure multiple times and taking an average over the resultant estimators, we obtain consistent estimators for the quantile regression coefficients. Our approach relaxes the usual global linearity assumption, so that we can apply quantile regression to any particular quantile of interest. We establish asymptotic properties for the proposed estimators, including both consistency and asymptotic normality. We conduct simulation studies to assess the finite-sample performance of the proposed multiple imputation method and apply it to a lung cancer study as an illustration.

  14. Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method

    NASA Astrophysics Data System (ADS)

    Cheng, Anyu; Jiang, Xiao; Li, Yongfu; Zhang, Chao; Zhu, Hao

    2017-01-01

    This study proposes a multiple sources and multiple measures based traffic flow prediction algorithm using the chaos theory and support vector regression method. In particular, first, the chaotic characteristics of traffic flow associated with the speed, occupancy, and flow are identified using the maximum Lyapunov exponent. Then, the phase space of multiple measures chaotic time series are reconstructed based on the phase space reconstruction theory and fused into a same multi-dimensional phase space using the Bayesian estimation theory. In addition, the support vector regression (SVR) model is designed to predict the traffic flow. Numerical experiments are performed using the data from multiple sources. The results show that, compared with the single measure, the proposed method has better performance for the short-term traffic flow prediction in terms of the accuracy and timeliness.

  15. Teasing out the effect of tutorials via multiple regression

    NASA Astrophysics Data System (ADS)

    Chasteen, Stephanie V.

    2012-02-01

    We transformed an upper-division physics course using a variety of elements, including homework help sessions, tutorials, clicker questions with peer instruction, and explicit learning goals. Overall, the course transformations improved student learning, as measured by our conceptual assessment. Since these transformations were multi-faceted, we would like to understand the impact of individual course elements. Attendance at tutorials and homework help sessions was optional, and occurred outside the class environment. In order to identify the impact of these optional out-of-class sessions, given self-selection effects in student attendance, we performed a multiple regression analysis. Even when background variables are taken into account, tutorial attendance is positively correlated with student conceptual understanding of the material - though not with performance on course exams. Other elements that increase student time-on-task, such as homework help sessions and lectures, do not achieve the same impacts.

  16. Dissociating conflict adaptation from feature integration: a multiple regression approach.

    PubMed

    Notebaert, Wim; Verguts, Tom

    2007-10-01

    Congruency effects are typically smaller after incongruent than after congruent trials. One explanation is in terms of higher levels of cognitive control after detection of conflict (conflict adaptation; e.g., M. M. Botvinick, T. S. Braver, D. M. Barch, C. S. Carter, & J. D. Cohen, 2001). An alternative explanation for these results is based on feature repetition and/or integration effects (e.g., B. Hommel, R. W. Proctor, & K.-P. Vu, 2004; U. Mayr, E. Awh, & P. Laurey, 2003). Previous attempts to dissociate feature integration from conflict adaptation focused on a particular subset of the data in which feature transitions were held constant (J. G. Kerns et al., 2004) or in which congruency transitions were held constant (C. Akcay & E. Hazeltine, in press), but this has a number of disadvantages. In this article, the authors present a multiple regression solution for this problem and discuss its possibilities and pitfalls.

  17. Contiguous Uniform Deviation for Multiple Linear Regression in Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Andriana, A. S.; Prihatmanto, D.; Hidaya, E. M. I.; Supriana, I.; Machbub, C.

    2017-01-01

    Understanding images by recognizing its objects is still a challenging task. Face elements detection has been developed by researchers but not yet shows enough information (low resolution in information) needed for recognizing objects. Available face recognition methods still have error in classification and need a huge amount of examples which may still be incomplete. Another approach which is still rare in understanding images uses pattern structures or syntactic grammars describing shape detail features. Image pixel values are also processed as signal patterns which are approximated by mathematical function curve fitting. This paper attempts to add contiguous uniform deviation method to curve fitting algorithm to increase applicability in image recognition system related to object movement. The combination of multiple linear regression and contiguous uniform deviation method are applied to the function of image pixel values, and show results in higher resolution (more information) of visual object detail description in object movement.

  18. Modeling pan evaporation for Kuwait by multiple linear regression.

    PubMed

    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.

  19. Multiple regression analyses in the prediction of aerospace instrument costs

    NASA Astrophysics Data System (ADS)

    Tran, Linh

    The aerospace industry has been investing for decades in ways to improve its efficiency in estimating the project life cycle cost (LCC). One of the major focuses in the LCC is the cost/prediction of aerospace instruments done during the early conceptual design phase of the project. The accuracy of early cost predictions affects the project scheduling and funding, and it is often the major cause for project cost overruns. The prediction of instruments' cost is based on the statistical analysis of these independent variables: Mass (kg), Power (watts), Instrument Type, Technology Readiness Level (TRL), Destination: earth orbiting or planetary, Data rates (kbps), Number of bands, Number of channels, Design life (months), and Development duration (months). This author is proposing a cost prediction approach of aerospace instruments based on these statistical analyses: Clustering Analysis, Principle Components Analysis (PCA), Bootstrap, and multiple regressions (both linear and non-linear). In the proposed approach, the Cost Estimating Relationship (CER) will be developed for the dependent variable Instrument Cost by using a combination of multiple independent variables. "The Full Model" will be developed and executed to estimate the full set of nine variables. The SAS program, Excel, Automatic Cost Estimating Integrate Tool (ACEIT) and Minitab are the tools to aid the analysis. Through the analysis, the cost drivers will be identified which will help develop an ultimate cost estimating software tool for the Instrument Cost prediction and optimization of future missions.

  20. Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

    PubMed

    Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

    2016-01-01

    Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

  1. Additive hazards regression and partial likelihood estimation for ecological monitoring data across space.

    PubMed

    Lin, Feng-Chang; Zhu, Jun

    2012-01-01

    We develop continuous-time models for the analysis of environmental or ecological monitoring data such that subjects are observed at multiple monitoring time points across space. Of particular interest are additive hazards regression models where the baseline hazard function can take on flexible forms. We consider time-varying covariates and take into account spatial dependence via autoregression in space and time. We develop statistical inference for the regression coefficients via partial likelihood. Asymptotic properties, including consistency and asymptotic normality, are established for parameter estimates under suitable regularity conditions. Feasible algorithms utilizing existing statistical software packages are developed for computation. We also consider a simpler additive hazards model with homogeneous baseline hazard and develop hypothesis testing for homogeneity. A simulation study demonstrates that the statistical inference using partial likelihood has sound finite-sample properties and offers a viable alternative to maximum likelihood estimation. For illustration, we analyze data from an ecological study that monitors bark beetle colonization of red pines in a plantation of Wisconsin.

  2. 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

  3. Use of Multiple Regression in Counseling Psychology Research: A Flexible Data-Analytic Strategy.

    ERIC Educational Resources Information Center

    Wampold, Bruce E.; Freund, Richard D.

    1987-01-01

    Explains multiple regression, demonstrates its flexibility for analyzing data from various designs, and discusses interpretation of results from multiple regression analysis. Presents regression equations for single independent variable and for two or more independent variables, followed by a discussion of coefficients related to these. Compares…

  4. A note on permutation tests of significance for multiple regression coefficients.

    PubMed

    Long, Michael A; Berry, Kenneth J; Mielke, Paul W

    2007-04-01

    In the vast majority of psychological research utilizing multiple regression analysis, asymptotic probability values are reported. This paper demonstrates that asymptotic estimates of standard errors provided by multiple regression are not always accurate. A resampling permutation procedure is used to estimate the standard errors. In some cases the results differ substantially from the traditional least squares regression estimates.

  5. Multiple Regression in a Two-Way Layout.

    ERIC Educational Resources Information Center

    Lindley, Dennis V.

    This paper discusses Bayesian m-group regression where the groups are arranged in a two-way layout into m rows and n columns, there still being a regression of y on the x's within each group. The mathematical model is then provided as applied to the case where the rows correspond to high schools and the columns to colleges: the predictor variables…

  6. Regression Discontinuity Designs with Multiple Rating-Score Variables

    ERIC Educational Resources Information Center

    Reardon, Sean F.; Robinson, Joseph P.

    2012-01-01

    In the absence of a randomized control trial, regression discontinuity (RD) designs can produce plausible estimates of the treatment effect on an outcome for individuals near a cutoff score. In the standard RD design, individuals with rating scores higher than some exogenously determined cutoff score are assigned to one treatment condition; those…

  7. Using regression equations built from summary data in the psychological assessment of the individual case: extension to multiple regression.

    PubMed

    Crawford, John R; Garthwaite, Paul H; Denham, Annie K; Chelune, Gordon J

    2012-12-01

    Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because (a) not all psychologists are aware that regression equations can be built not only from raw data but also using only basic summary data for a sample, and (b) the computations involved are tedious and prone to error. In an attempt to overcome these barriers, Crawford and Garthwaite (2007) provided methods to build and apply simple linear regression models using summary statistics as data. In the present study, we extend this work to set out the steps required to build multiple regression models from sample summary statistics and the further steps required to compute the associated statistics for drawing inferences concerning an individual case. We also develop, describe, and make available a computer program that implements these methods. Although there are caveats associated with the use of the methods, these need to be balanced against pragmatic considerations and against the alternative of either entirely ignoring a pertinent data set or using it informally to provide a clinical "guesstimate." Upgraded versions of earlier programs for regression in the single case are also provided; these add the point and interval estimates of effect size developed in the present article.

  8. Analysis of aromatic constituents in multicomponent hydrocarbon mixtures by infrared spectroscopy using multiple linear regression

    NASA Astrophysics Data System (ADS)

    Vesnin, V. L.; Muradov, V. G.

    2012-09-01

    Absorption spectra of multicomponent hydrocarbon mixtures based on n-heptane and isooctane with addition of benzene (up to 1%) and toluene and o-xylene (up to 20%) were investigated experimentally in the region of the first overtones of the hydrocarbon groups (λ = 1620-1780 nm). It was shown that their concentrations could be determined separately by using a multiple linear regression method. The optimum result was obtained by including four wavelengths at 1671, 1680, 1685, and 1695 nm, which took into account absorption of CH groups in benzene, toluene, and o-xylene and CH3 groups, respectively.

  9. Beyond Multiple Regression: Using Commonality Analysis to Better Understand R[superscript 2] Results

    ERIC Educational Resources Information Center

    Warne, Russell T.

    2011-01-01

    Multiple regression is one of the most common statistical methods used in quantitative educational research. Despite the versatility and easy interpretability of multiple regression, it has some shortcomings in the detection of suppressor variables and for somewhat arbitrarily assigning values to the structure coefficients of correlated…

  10. Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

    ERIC Educational Resources Information Center

    Williams, Ryan T.

    2012-01-01

    Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…

  11. Estimating Driver Performance Using Multiple Electroencephalography (EEG)-Based Regression Algorithms

    DTIC Science & Technology

    2014-09-01

    Estimating Driver Performance Using Multiple Electroencephalography (EEG)-Based Regression Algorithms by Gregory Apker, Brent Lance, Scott...Proving Ground, MD 21005-5425 ARL-TR-7074 September 2014 Estimating Driver Performance Using Multiple Electroencephalography (EEG)-Based... Electroencephalography (EEG)- Based Regression Algorithms 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Gregory Apker

  12. False Positives in Multiple Regression: Unanticipated Consequences of Measurement Error in the Predictor Variables

    ERIC Educational Resources Information Center

    Shear, Benjamin R.; Zumbo, Bruno D.

    2013-01-01

    Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…

  13. 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.

  14. An application of a microcomputer compiler program to multiple logistic regression analysis.

    PubMed

    Sakai, R

    1988-01-01

    Microcomputer programs for multiple logistic regression analysis were written in BASIC language to determine the usefulness of microcomputers for multivariate analysis, which is an important method in epidemiological studies. The program, carried out by an interpreter system, required a comparatively long computing time for a small amount of data. For example, it took approximately thirty minutes to compute the data of 6 independent variables and 63 matched sets of case and controls (1:4). The majority of the calculation time was spent computing a matrix. The matrix computation time increased cumulatively in proportion to additions in the number of subjects, and increased exponentially with the number of variables. A BASIC compiler was utilized for the program of multiple logistic regression analysis. The compiled program carried out the same computations as above, but within 4 minutes. Therefore, it is evident that a compiler can be an extremely convenient tool for computing multivariate analysis. The two programs produced here were also easily linked with spreadsheet packages to enter data.

  15. MULTIPLE REGRESSION MODELS FOR HINDCASTING AND FORECASTING MIDSUMMER HYPOXIA IN THE GULF OF MEXICO

    EPA Science Inventory

    A new suite of multiple regression models were developed that describe the relationship between the area of bottom water hypoxia along the northern Gulf of Mexico and Mississippi-Atchafalaya River nitrate concentration, total phosphorus (TP) concentration, and discharge. Variabil...

  16. SOME STATISTICAL ISSUES RELATED TO MULTIPLE LINEAR REGRESSION MODELING OF BEACH BACTERIA CONCENTRATIONS

    EPA Science Inventory

    As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...

  17. Confidence Intervals for an Effect Size Measure in Multiple Linear Regression

    ERIC Educational Resources Information Center

    Algina, James; Keselman, H. J.; Penfield, Randall D.

    2007-01-01

    The increase in the squared multiple correlation coefficient ([Delta]R[squared]) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. The coverage probability that an asymptotic and percentile bootstrap confidence interval includes [Delta][rho][squared] was investigated. As expected,…

  18. Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

    PubMed

    Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

    2012-05-01

    Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation.

  19. An improved multiple linear regression and data analysis computer program package

    NASA Technical Reports Server (NTRS)

    Sidik, S. M.

    1972-01-01

    NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

  20. 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…

  1. Structured additive distributional regression for analysing landings per unit effort in fisheries research.

    PubMed

    Mamouridis, Valeria; Klein, Nadja; Kneib, Thomas; Cadarso Suarez, Carmen; Maynou, Francesc

    2017-01-01

    We analysed the landings per unit effort (LPUE) from the Barcelona trawl fleet targeting the red shrimp (Aristeus antennatus) using novel Bayesian structured additive distributional regression to gain a better understanding of the dynamics and determinants of variation in LPUE. The data set, covering a time span of 17 years, includes fleet-dependent variables (e.g. the number of trips performed by vessels), temporal variables (inter- and intra-annual variability) and environmental variables (the North Atlantic Oscillation index). Based on structured additive distributional regression, we evaluate (i) the gain in replacing purely linear predictors by additive predictors including nonlinear effects of continuous covariates, (ii) the inclusion of vessel-specific effects based on either fixed or random effects, (iii) different types of distributions for the response, and (iv) the potential gain in not only modelling the location but also the scale/shape parameter of these distributions. Our findings support that flexible model variants are indeed able to improve the fit considerably and that additional insights can be gained. Tools to select within several model specifications and assumptions are discussed in detail as well.

  2. Comparison of multiple regression to two latent variable techniques for estimation and prediction.

    PubMed

    Wall, Melanie M; Li, Ruifeng

    2003-12-15

    In the areas of epidemiology, psychology, sociology, and other social and behavioural sciences, researchers often encounter situations where there are not only many variables contributing to a particular phenomenon, but there are also strong relationships among many of the predictor variables of interest. By using the traditional multiple regression on all the predictor variables, it is possible to have problems with interpretation and multicollinearity. As an alternative to multiple regression, we explore the use of a latent variable model that can address the relationship among the predictor variables. We consider two different methods for estimation and prediction for this model: one that uses multiple regression on factor score estimates and the other that uses structural equation modelling. The first method uses multiple regression but on a set of predicted underlying factors (i.e. factor scores), and the second method is a full-information maximum-likelihood technique that incorporates the complete covariance structure of the data. In this tutorial, we will explain the model and each estimation method, including how to carry out prediction. A data example will be used for demonstration, where respiratory disease death rates by county in Minnesota are predicted by five county-level census variables. A simulation study is performed to evaluate the efficiency of prediction using the two latent variable modelling techniques compared to multiple regression.

  3. Modeling of retardance in ferrofluid with Taguchi-based multiple regression analysis

    NASA Astrophysics Data System (ADS)

    Lin, Jing-Fung; Wu, Jyh-Shyang; Sheu, Jer-Jia

    2015-03-01

    The citric acid (CA) coated Fe3O4 ferrofluids are prepared by a co-precipitation method and the magneto-optical retardance property is measured by a Stokes polarimeter. Optimization and multiple regression of retardance in ferrofluids are executed by combining Taguchi method and Excel. From the nine tests for four parameters, including pH of suspension, molar ratio of CA to Fe3O4, volume of CA, and coating temperature, influence sequence and excellent program are found. Multiple regression analysis and F-test on the significance of regression equation are performed. It is found that the model F value is much larger than Fcritical and significance level P <0.0001. So it can be concluded that the regression model has statistically significant predictive ability. Substituting excellent program into equation, retardance is obtained as 32.703°, higher than the highest value in tests by 11.4%.

  4. The development of a flyover noise prediction technique using multiple linear regression analysis

    NASA Astrophysics Data System (ADS)

    Rathgeber, R. K.

    1981-04-01

    At Cessna Aircraft Company, statistical analyses have been developed to define important trends in flyover noise data. Multiple regression techniques have provided the means to develop flyover noise prediction methods which have resulted in better accuracy than methods used in the past. Regression analyses have been conducted to determine the important relationship between propeller helical tip Mach number and the flyover noise level. Other variables have been included in the regression models either because the added variable contributed to reducing the remaining variation in the model or the variable appeared to be a strong causal agent of flyover noise.

  5. Improving the accuracies of bathymetric models based on multiple regression for calibration (case study: Sarca River, Italy)

    NASA Astrophysics Data System (ADS)

    Niroumand-Jadidi, Milad; Vitti, Alfonso

    2016-10-01

    The optical imagery has the potential for extraction of spatially and temporally explicit bathymetric information in inland/coastal waters. Lyzenga's model and optimal band ratio analysis (OBRA) are main bathymetric models which both provide linear relations with water depths. The former model is sensitive and the latter is quite robust to substrate variability. The simple regression is the widely used approach for calibration of bathymetric models either Lyzenga's model or OBRA model. In this research, a multiple regression is examined for empirical calibration of the models in order to take the advantage of all spectral channels of the imagery. This method is applied on both Lyzenga's model and OBRA model for the bathymetry of a shallow Alpine river in Italy, using WorldView-2 (WV-2) and GeoEye images. Insitu depths are recorded using RTK GPS in two reaches. One-half of the data is used for calibration of models and the remaining half as independent check-points for accuracy assessment. In addition, radiative transfer model is used to simulate a set of spectra in a range of depths, substrate types, and water column properties. The simulated spectra are convolved to the sensors' spectral bands for further bathymetric analysis. Investigating the simulated spectra, it is concluded that the multiple regression improves the robustness of the Lyzenga's model with respect to the substrate variability. The improvements of multiple regression approach are much more pronounced for the Lyzenga's model rather than the OBRA model. This is in line with findings from real imagery; for instance, the multiple regression applied for calibration of Lyzenga's and OBRA models demonstrated, respectively, 22% and 9% higher determination coefficients (R2) as well as 3 cm and 1 cm better RMSEs compared to the simple regression using the WV-2 image.

  6. Marginal regression approach for additive hazards models with clustered current status data.

    PubMed

    Su, Pei-Fang; Chi, Yunchan

    2014-01-15

    Current status data arise naturally from tumorigenicity experiments, epidemiology studies, biomedicine, econometrics and demographic and sociology studies. Moreover, clustered current status data may occur with animals from the same litter in tumorigenicity experiments or with subjects from the same family in epidemiology studies. Because the only information extracted from current status data is whether the survival times are before or after the monitoring or censoring times, the nonparametric maximum likelihood estimator of survival function converges at a rate of n(1/3) to a complicated limiting distribution. Hence, semiparametric regression models such as the additive hazards model have been extended for independent current status data to derive the test statistics, whose distributions converge at a rate of n(1/2) , for testing the regression parameters. However, a straightforward application of these statistical methods to clustered current status data is not appropriate because intracluster correlation needs to be taken into account. Therefore, this paper proposes two estimating functions for estimating the parameters in the additive hazards model for clustered current status data. The comparative results from simulation studies are presented, and the application of the proposed estimating functions to one real data set is illustrated.

  7. 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.

  8. Regression models for the analysis of longitudinal Gaussian data from multiple sources.

    PubMed

    O'Brien, Liam M; Fitzmaurice, Garrett M

    2005-06-15

    We present a regression model for the joint analysis of longitudinal multiple source Gaussian data. Longitudinal multiple source data arise when repeated measurements are taken from two or more sources, and each source provides a measure of the same underlying variable and on the same scale. This type of data generally produces a relatively large number of observations per subject; thus estimation of an unstructured covariance matrix often may not be possible. We consider two methods by which parsimonious models for the covariance can be obtained for longitudinal multiple source data. The methods are illustrated with an example of multiple informant data arising from a longitudinal interventional trial in psychiatry.

  9. Dissociated brain organization for single-digit addition and multiplication.

    PubMed

    Zhou, Xinlin; Chen, Chuansheng; Zang, Yufeng; Dong, Qi; Chen, Chunhui; Qiao, Sibing; Gong, Qiyong

    2007-04-01

    This study compared the patterns of brain activation elicited by single-digit addition and multiplication problems. 20 Chinese undergraduates were asked to verify whether arithmetic equations were true or false during functional magnetic resonance imaging. Results showed that both addition and multiplication were supported by a broad neural system that involved regions within SMA, precentral gyrus, intraparietal sulcus, occipital gyri, superior temporal gyrus, and middle frontal gyrus, as well as some subcortical structures. Nevertheless, addition problems elicited more activation in the intraparietal sulcus and middle occipital gyri at the right hemisphere, and superior occipital gyri at both hemispheres, whereas multiplication had more activation in precentral gyrus, supplementary motor areas, and posterior and anterior superior temporal gyrus at the left hemisphere. This pattern of dissociated activation supports our hypothesis that addition has greater reliance on visuospatial processing and multiplication on verbal processing.

  10. 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

  11. Regression analysis of mixed recurrent-event and panel-count data with additive rate models.

    PubMed

    Zhu, Liang; Zhao, Hui; Sun, Jianguo; Leisenring, Wendy; Robison, Leslie L

    2015-03-01

    Event-history studies of recurrent events are often conducted in fields such as demography, epidemiology, medicine, and social sciences (Cook and Lawless, 2007, The Statistical Analysis of Recurrent Events. New York: Springer-Verlag; Zhao et al., 2011, Test 20, 1-42). For such analysis, two types of data have been extensively investigated: recurrent-event data and panel-count data. However, in practice, one may face a third type of data, mixed recurrent-event and panel-count data or mixed event-history data. Such data occur if some study subjects are monitored or observed continuously and thus provide recurrent-event data, while the others are observed only at discrete times and hence give only panel-count data. A more general situation is that each subject is observed continuously over certain time periods but only at discrete times over other time periods. There exists little literature on the analysis of such mixed data except that published by Zhu et al. (2013, Statistics in Medicine 32, 1954-1963). In this article, we consider the regression analysis of mixed data using the additive rate model and develop some estimating equation-based approaches to estimate the regression parameters of interest. Both finite sample and asymptotic properties of the resulting estimators are established, and the numerical studies suggest that the proposed methodology works well for practical situations. The approach is applied to a Childhood Cancer Survivor Study that motivated this study.

  12. Using Robust Variance Estimation to Combine Multiple Regression Estimates with Meta-Analysis

    ERIC Educational Resources Information Center

    Williams, Ryan

    2013-01-01

    The purpose of this study was to explore the use of robust variance estimation for combining commonly specified multiple regression models and for combining sample-dependent focal slope estimates from diversely specified models. The proposed estimator obviates traditionally required information about the covariance structure of the dependent…

  13. A Modified Gauss-Jordan Procedure as an Alternative to Iterative Procedures in Multiple Regression.

    ERIC Educational Resources Information Center

    Roscoe, John T.; Kittleson, Howard M.

    Correlation matrices involving linear dependencies are common in educational research. In such matrices, there is no unique solution for the multiple regression coefficients. Although computer programs using iterative techniques are used to overcome this problem, these techniques possess certain disadvantages. Accordingly, a modified Gauss-Jordan…

  14. Use of Multiple Regression to Predict Academic Achievement at a Small Liberal Arts College.

    ERIC Educational Resources Information Center

    Hardesty, Larry

    The relationship between academic success at DePauw University and such commonly used predictors as tested ability and academic success in high school was examined. The various subtleties of the multiple regression research method were also examined. Subjects were 1758 students who entered DePauw University during the fall semester of 1973, 1974,…

  15. A Simple and Convenient Method of Multiple Linear Regression to Calculate Iodine Molecular Constants

    ERIC Educational Resources Information Center

    Cooper, Paul D.

    2010-01-01

    A new procedure using a student-friendly least-squares multiple linear-regression technique utilizing a function within Microsoft Excel is described that enables students to calculate molecular constants from the vibronic spectrum of iodine. This method is advantageous pedagogically as it calculates molecular constants for ground and excited…

  16. What Is Wrong with ANOVA and Multiple Regression? Analyzing Sentence Reading Times with Hierarchical Linear Models

    ERIC Educational Resources Information Center

    Richter, Tobias

    2006-01-01

    Most reading time studies using naturalistic texts yield data sets characterized by a multilevel structure: Sentences (sentence level) are nested within persons (person level). In contrast to analysis of variance and multiple regression techniques, hierarchical linear models take the multilevel structure of reading time data into account. They…

  17. Double Cross-Validation in Multiple Regression: A Method of Estimating the Stability of Results.

    ERIC Educational Resources Information Center

    Rowell, R. Kevin

    In multiple regression analysis, where resulting predictive equation effectiveness is subject to shrinkage, it is especially important to evaluate result replicability. Double cross-validation is an empirical method by which an estimate of invariance or stability can be obtained from research data. A procedure for double cross-validation is…

  18. Multiple Regression Analysis of Sib-Pair Data on Reading to Detect Quantitative Trait Loci.

    ERIC Educational Resources Information Center

    Fulker, D. W.; And Others

    1991-01-01

    Applies an extension of an earlier multiple regression model for twin analysis to the problem of detecting linkage in a quantitative trait. Detects a number of possible linkages, indicating that the approach is effective. Discusses detecting genotype-environment interaction and the issue of power. (RS)

  19. Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

    ERIC Educational Resources Information Center

    Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

    2006-01-01

    Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

  20. INTRODUCTION TO A COMBINED MULTIPLE LINEAR REGRESSION AND ARMA MODELING APPROACH FOR BEACH BACTERIA PREDICTION

    EPA Science Inventory

    Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...

  1. Multiple Regression Analysis of Factors that May Influence Middle School Science Scores

    ERIC Educational Resources Information Center

    Glover, Judith

    2012-01-01

    The purpose of this quantitative multiple regression study was to determine whether a relationship existed between Maryland State Assessment (MSA) reading scores, MSA math scores, gender, ethnicity, age, and MSA science scores. Also examined was if MSA reading scores, MSA math scores, gender, ethnicity, and age can be used in combination or alone…

  2. Testing Mediation Using Multiple Regression and Structural Equation Modeling Analyses in Secondary Data

    ERIC Educational Resources Information Center

    Li, Spencer D.

    2011-01-01

    Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…

  3. The Performance of the Full Information Maximum Likelihood Estimator in Multiple Regression Models with Missing Data.

    ERIC Educational Resources Information Center

    Enders, Craig K.

    2001-01-01

    Examined the performance of a recently available full information maximum likelihood (FIML) estimator in a multiple regression model with missing data using Monte Carlo simulation and considering the effects of four independent variables. Results indicate that FIML estimation was superior to that of three ad hoc techniques, with less bias and less…

  4. Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression

    ERIC Educational Resources Information Center

    Anderson, Joan L.

    2006-01-01

    Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models…

  5. Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure

    PubMed Central

    Li, Yanming; Zhu, Ji

    2015-01-01

    Summary We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functioning groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study. PMID:25732839

  6. Simultaneous Multiple Response Regression and Inverse Covariance Matrix Estimation via Penalized Gaussian Maximum Likelihood.

    PubMed

    Lee, Wonyul; Liu, Yufeng

    2012-10-01

    Multivariate regression is a common statistical tool for practical problems. Many multivariate regression techniques are designed for univariate response cases. For problems with multiple response variables available, one common approach is to apply the univariate response regression technique separately on each response variable. Although it is simple and popular, the univariate response approach ignores the joint information among response variables. In this paper, we propose three new methods for utilizing joint information among response variables. All methods are in a penalized likelihood framework with weighted L(1) regularization. The proposed methods provide sparse estimators of conditional inverse co-variance matrix of response vector given explanatory variables as well as sparse estimators of regression parameters. Our first approach is to estimate the regression coefficients with plug-in estimated inverse covariance matrices, and our second approach is to estimate the inverse covariance matrix with plug-in estimated regression parameters. Our third approach is to estimate both simultaneously. Asymptotic properties of these methods are explored. Our numerical examples demonstrate that the proposed methods perform competitively in terms of prediction, variable selection, as well as inverse covariance matrix estimation.

  7. Using Regression Equations Built from Summary Data in the Psychological Assessment of the Individual Case: Extension to Multiple Regression

    ERIC Educational Resources Information Center

    Crawford, John R.; Garthwaite, Paul H.; Denham, Annie K.; Chelune, Gordon J.

    2012-01-01

    Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because…

  8. Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping

    PubMed Central

    2013-01-01

    Background Complex binary traits are influenced by many factors including the main effects of many quantitative trait loci (QTLs), the epistatic effects involving more than one QTLs, environmental effects and the effects of gene-environment interactions. Although a number of QTL mapping methods for binary traits have been developed, there still lacks an efficient and powerful method that can handle both main and epistatic effects of a relatively large number of possible QTLs. Results In this paper, we use a Bayesian logistic regression model as the QTL model for binary traits that includes both main and epistatic effects. Our logistic regression model employs hierarchical priors for regression coefficients similar to the ones used in the Bayesian LASSO linear model for multiple QTL mapping for continuous traits. We develop efficient empirical Bayesian algorithms to infer the logistic regression model. Our simulation study shows that our algorithms can easily handle a QTL model with a large number of main and epistatic effects on a personal computer, and outperform five other methods examined including the LASSO, HyperLasso, BhGLM, RVM and the single-QTL mapping method based on logistic regression in terms of power of detection and false positive rate. The utility of our algorithms is also demonstrated through analysis of a real data set. A software package implementing the empirical Bayesian algorithms in this paper is freely available upon request. Conclusions The EBLASSO logistic regression method can handle a large number of effects possibly including the main and epistatic QTL effects, environmental effects and the effects of gene-environment interactions. It will be a very useful tool for multiple QTLs mapping for complex binary traits. PMID:23410082

  9. Boosted structured additive regression for Escherichia coli fed-batch fermentation modeling.

    PubMed

    Melcher, Michael; Scharl, Theresa; Luchner, Markus; Striedner, Gerald; Leisch, Friedrich

    2017-02-01

    The quality of biopharmaceuticals and patients' safety are of highest priority and there are tremendous efforts to replace empirical production process designs by knowledge-based approaches. Main challenge in this context is that real-time access to process variables related to product quality and quantity is severely limited. To date comprehensive on- and offline monitoring platforms are used to generate process data sets that allow for development of mechanistic and/or data driven models for real-time prediction of these important quantities. Ultimate goal is to implement model based feed-back control loops that facilitate online control of product quality. In this contribution, we explore structured additive regression (STAR) models in combination with boosting as a variable selection tool for modeling the cell dry mass, product concentration, and optical density on the basis of online available process variables and two-dimensional fluorescence spectroscopic data. STAR models are powerful extensions of linear models allowing for inclusion of smooth effects or interactions between predictors. Boosting constructs the final model in a stepwise manner and provides a variable importance measure via predictor selection frequencies. Our results show that the cell dry mass can be modeled with a relative error of about ±3%, the optical density with ±6%, the soluble protein with ±16%, and the insoluble product with an accuracy of ±12%. Biotechnol. Bioeng. 2017;114: 321-334. © 2016 Wiley Periodicals, Inc.

  10. Optimization of fixture layouts of glass laser optics using multiple kernel regression.

    PubMed

    Su, Jianhua; Cao, Enhua; Qiao, Hong

    2014-05-10

    We aim to build an integrated fixturing model to describe the structural properties and thermal properties of the support frame of glass laser optics. Therefore, (a) a near global optimal set of clamps can be computed to minimize the surface shape error of the glass laser optic based on the proposed model, and (b) a desired surface shape error can be obtained by adjusting the clamping forces under various environmental temperatures based on the model. To construct the model, we develop a new multiple kernel learning method and call it multiple kernel support vector functional regression. The proposed method uses two layer regressions to group and order the data sources by the weights of the kernels and the factors of the layers. Because of that, the influences of the clamps and the temperature can be evaluated by grouping them into different layers.

  11. Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design.

    PubMed

    Agha, Salah R; Alnahhal, Mohammed J

    2012-11-01

    The current study investigates the possibility of obtaining the anthropometric dimensions, critical to school furniture design, without measuring all of them. The study first selects some anthropometric dimensions that are easy to measure. Two methods are then used to check if these easy-to-measure dimensions can predict the dimensions critical to the furniture design. These methods are multiple linear regression and neural networks. Each dimension that is deemed necessary to ergonomically design school furniture is expressed as a function of some other measured anthropometric dimensions. Results show that out of the five dimensions needed for chair design, four can be related to other dimensions that can be measured while children are standing. Therefore, the method suggested here would definitely save time and effort and avoid the difficulty of dealing with students while measuring these dimensions. In general, it was found that neural networks perform better than multiple linear regression in the current study.

  12. User's Guide to the Weighted-Multiple-Linear Regression Program (WREG version 1.0)

    USGS Publications Warehouse

    Eng, Ken; Chen, Yin-Yu; Kiang, Julie.E.

    2009-01-01

    Streamflow is not measured at every location in a stream network. Yet hydrologists, State and local agencies, and the general public still seek to know streamflow characteristics, such as mean annual flow or flood flows with different exceedance probabilities, at ungaged basins. The goals of this guide are to introduce and familiarize the user with the weighted multiple-linear regression (WREG) program, and to also provide the theoretical background for program features. The program is intended to be used to develop a regional estimation equation for streamflow characteristics that can be applied at an ungaged basin, or to improve the corresponding estimate at continuous-record streamflow gages with short records. The regional estimation equation results from a multiple-linear regression that relates the observable basin characteristics, such as drainage area, to streamflow characteristics.

  13. Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems

    PubMed Central

    Zainudin, Suhaila; Arif, Shereena M.

    2017-01-01

    Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5. PMID:28250767

  14. Watershed Regressions for Pesticides (WARP) models for predicting stream concentrations of multiple pesticides

    USGS Publications Warehouse

    Stone, Wesley W.; Crawford, Charles G.; Gilliom, Robert J.

    2013-01-01

    Watershed Regressions for Pesticides for multiple pesticides (WARP-MP) are statistical models developed to predict concentration statistics for a wide range of pesticides in unmonitored streams. The WARP-MP models use the national atrazine WARP models in conjunction with an adjustment factor for each additional pesticide. The WARP-MP models perform best for pesticides with application timing and methods similar to those used with atrazine. For other pesticides, WARP-MP models tend to overpredict concentration statistics for the model development sites. For WARP and WARP-MP, the less-than-ideal sampling frequency for the model development sites leads to underestimation of the shorter-duration concentration; hence, the WARP models tend to underpredict 4- and 21-d maximum moving-average concentrations, with median errors ranging from 9 to 38% As a result of this sampling bias, pesticides that performed well with the model development sites are expected to have predictions that are biased low for these shorter-duration concentration statistics. The overprediction by WARP-MP apparent for some of the pesticides is variably offset by underestimation of the model development concentration statistics. Of the 112 pesticides used in the WARP-MP application to stream segments nationwide, 25 were predicted to have concentration statistics with a 50% or greater probability of exceeding one or more aquatic life benchmarks in one or more stream segments. Geographically, many of the modeled streams in the Corn Belt Region were predicted to have one or more pesticides that exceeded an aquatic life benchmark during 2009, indicating the potential vulnerability of streams in this region.

  15. Watershed Regressions for Pesticides (WARP) Models for Predicting Stream Concentrations of Multiple Pesticides.

    PubMed

    Stone, Wesley W; Crawford, Charles G; Gilliom, Robert J

    2013-11-01

    Watershed Regressions for Pesticides for multiple pesticides (WARP-MP) are statistical models developed to predict concentration statistics for a wide range of pesticides in unmonitored streams. The WARP-MP models use the national atrazine WARP models in conjunction with an adjustment factor for each additional pesticide. The WARP-MP models perform best for pesticides with application timing and methods similar to those used with atrazine. For other pesticides, WARP-MP models tend to overpredict concentration statistics for the model development sites. For WARP and WARP-MP, the less-than-ideal sampling frequency for the model development sites leads to underestimation of the shorter-duration concentration; hence, the WARP models tend to underpredict 4- and 21-d maximum moving-average concentrations, with median errors ranging from 9 to 38% As a result of this sampling bias, pesticides that performed well with the model development sites are expected to have predictions that are biased low for these shorter-duration concentration statistics. The overprediction by WARP-MP apparent for some of the pesticides is variably offset by underestimation of the model development concentration statistics. Of the 112 pesticides used in the WARP-MP application to stream segments nationwide, 25 were predicted to have concentration statistics with a 50% or greater probability of exceeding one or more aquatic life benchmarks in one or more stream segments. Geographically, many of the modeled streams in the Corn Belt Region were predicted to have one or more pesticides that exceeded an aquatic life benchmark during 2009, indicating the potential vulnerability of streams in this region.

  16. Interactions between cadmium and decabrominated diphenyl ether on blood cells count in rats-Multiple factorial regression analysis.

    PubMed

    Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana

    2017-02-01

    The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200-240g for 28days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight antagonism

  17. Predicting manual arm strength: A direct comparison between artificial neural network and multiple regression approaches.

    PubMed

    La Delfa, Nicholas J; Potvin, Jim R

    2016-02-29

    In ergonomics, strength prediction has typically been accomplished using linked-segment biomechanical models, and independent estimates of strength about each axis of the wrist, elbow and shoulder joints. It has recently been shown that multiple regression approaches, using the simple task-relevant inputs of hand location and force direction, may be a better method for predicting manual arm strength (MAS) capabilities. Artificial neural networks (ANNs) also serve as a powerful data fitting approach, but their application to occupational biomechanics and ergonomics is limited. Therefore, the purpose of this study was to perform a direct comparison between ANN and regression models, by evaluating their ability to predict MAS with identical sets of development and validation MAS data. Multi-directional MAS data were obtained from 95 healthy female participants at 36 hand locations within the reach envelope. ANN and regression models were developed using a random, but identical, sample of 85% of the MAS data (n=456). The remaining 15% of the data (n=80) were used to validate the two approaches. When compared to the development data, the ANN predictions had a much higher explained variance (90.2% vs. 66.5%) and much lower RMSD (9.3N vs. 17.2N), vs. the regression model. The ANN also performed better with the independent validation data (r(2)=78.6%, RMSD=15.1) compared to the regression approach (r(2)=65.3%, RMSD=18.6N). These results suggest that ANNs provide a more accurate and robust alternative to regression approaches, and should be considered more often in biomechanics and ergonomics evaluations.

  18. Research on the multiple linear regression in non-invasive blood glucose measurement.

    PubMed

    Zhu, Jianming; Chen, Zhencheng

    2015-01-01

    A non-invasive blood glucose measurement sensor and the data process algorithm based on the metabolic energy conservation (MEC) method are presented in this paper. The physiological parameters of human fingertip can be measured by various sensing modalities, and blood glucose value can be evaluated with the physiological parameters by the multiple linear regression analysis. Five methods such as enter, remove, forward, backward and stepwise in multiple linear regression were compared, and the backward method had the best performance. The best correlation coefficient was 0.876 with the standard error of the estimate 0.534, and the significance was 0.012 (sig. <0.05), which indicated the regression equation was valid. The Clarke error grid analysis was performed to compare the MEC method with the hexokinase method, using 200 data points. The correlation coefficient R was 0.867 and all of the points were located in Zone A and Zone B, which shows the MEC method provides a feasible and valid way for non-invasive blood glucose measurement.

  19. Multiple linear regression modeling of disinfection by-products formation in Istanbul drinking water reservoirs.

    PubMed

    Uyak, Vedat; Ozdemir, Kadir; Toroz, Ismail

    2007-06-01

    Oxidation of raw water with chlorine results in formation of trihalomethanes (THM) and haloacetic acids (HAA). Factors affecting their concentrations have been found to be organic matter type and concentration, pH, temperature, chlorine dose, contact time and bromide concentration, but the mechanisms of their formation are still under investigation. Within this scope, chlorination experiments have been conducted with water reservoirs from Terkos, Buyukcekmece and Omerli lakes, Istanbul, with different water quality regarding bromide concentration and organic matter content. The factors studied were pH, contact time, chlorine dose, and specific ultraviolet absorbance (SUVA). The determination of disinfection by-products (DBP) was carried out by gas chromatography techniques. Statistical analysis of the results was focused on the development of multiple regression models for predicting the concentrations of total THM and total HAA based on the use of pH, contact time, chlorine dose, and SUVA. The developed models provided satisfactory estimations of the concentrations of the DBP and the model regression coefficients of THM and HAA are 0.88 and 0.61, respectively. Further, the Durbin-Watson values confirm the reliability of the two models. The results indicate that under these experimental conditions which indicate the variations of pH, chlorine dosages, contact time, and SUVA values, the formation of THM and HAA in water can be described by the multiple linear regression technique.

  20. Replicating psychiatric ratings through multiple regression analysis: The Midtown Manhattan Restudy.

    PubMed

    Singer, E; Cohen, S M; Garfinkel, R; Srole, L

    1976-12-01

    This paper examines three related questions: First, can psychiatrists' judgments be successfully predicted by multiple regression techniques? Second, assuming that they can, are such ratings a valid measure of mental health for the same sample at a later point in time? Third, what is the relation between mental health ratings made in 1954 and such subsequently reported behavioral outcomes as nervous breakdown, mental hospitalization, or seeking professional help for emotional problems? The evidence presented warrants two conclusion. (1) The computer-derived mental health ratings are an adequate substitute for the original ratings. The regression equation accounts for 69 percent of the variance in those ratings; and the computer-derived ratings behave in the same way as the psychiatrists' ratings in relation to other variables. (2) However, neither the psychiatrists' ratings nor the computer-derived ratings are very accurate in predicting subsequent self-reported behavior indicative of mental impairment.

  1. [Correlation of dry granulation process parameters and granule quality based on multiple regression analysis].

    PubMed

    Cao, Han-Han; Du, Ruo-Fei; Yang, Jia-Ning; Feng, Yi

    2014-03-01

    In this paper, microcrystalline cellulose WJ101 was used as a model material to investigate the effect of various process parameters on granule yield and friability after dry granulation with a single factor and the effect of comprehensive inspection process parameters on the effect of granule yield and friability, then the correlation between process parameters and granule quality was established. The regress equation was established between process parameters and granule yield and friability by multiple regression analysis, the affecting the order of the size of the order of the process parameters on granule yield and friability was: rollers speed > rollers pressure > speed of horizontal feed. Granule yield was positively correlated with pressure and speed of horizontal feed and negatively correlated rollers speed, while friability was on the contrary. By comparison, fitted value and real value, fitted and real value are basically the same of no significant differences (P > 0.05) and with high precision and reliability.

  2. Multiple regression analyses in artificial-grammar learning: the importance of control groups.

    PubMed

    Lotz, Anja; Kinder, Annette; Lachnit, Harald

    2009-03-01

    In artificial-grammar learning, it is crucial to ensure that above-chance performance in the test stage is due to learning in the training stage but not due to judgemental biases. Here we argue that multiple regression analysis can be successfully combined with the use of control groups to assess whether participants were able to transfer knowledge acquired during training when making judgements about test stimuli. We compared the regression weights of judgements in a transfer condition (training and test strings were constructed by the same grammar but with different letters) with those in a control condition. Predictors were identical in both conditions-judgements of control participants were treated as if they were based on knowledge gained in a standard training stage. The results of this experiment as well as reanalyses of a former study support the usefulness of our approach.

  3. Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure

    PubMed Central

    Yoo, Yun Joo; Sun, Lei; Poirier, Julia G.; Paterson, Andrew D.

    2016-01-01

    ABSTRACT By jointly analyzing multiple variants within a gene, instead of one at a time, gene‐based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster‐specific effects in a quadratic sum of squares and cross‐products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well‐powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P‐value, variance‐component, and principal‐component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene‐specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome‐wide analysis. The cluster construction of the MLC test statistics helps reveal within‐gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations. PMID:27885705

  4. Further Insight and Additional Inference Methods for Polynomial Regression Applied to the Analysis of Congruence

    ERIC Educational Resources Information Center

    Cohen, Ayala; Nahum-Shani, Inbal; Doveh, Etti

    2010-01-01

    In their seminal paper, Edwards and Parry (1993) presented the polynomial regression as a better alternative to applying difference score in the study of congruence. Although this method is increasingly applied in congruence research, its complexity relative to other methods for assessing congruence (e.g., difference score methods) was one of the…

  5. Strengthening the Regression Discontinuity Design Using Additional Design Elements: A Within-Study Comparison

    ERIC Educational Resources Information Center

    Wing, Coady; Cook, Thomas D.

    2013-01-01

    The sharp regression discontinuity design (RDD) has three key weaknesses compared to the randomized clinical trial (RCT). It has lower statistical power, it is more dependent on statistical modeling assumptions, and its treatment effect estimates are limited to the narrow subpopulation of cases immediately around the cutoff, which is rarely of…

  6. An Additional Measure of Overall Effect Size for Logistic Regression Models

    ERIC Educational Resources Information Center

    Allen, Jeff; Le, Huy

    2008-01-01

    Users of logistic regression models often need to describe the overall predictive strength, or effect size, of the model's predictors. Analogs of R[superscript 2] have been developed, but none of these measures are interpretable on the same scale as effects of individual predictors. Furthermore, R[superscript 2] analogs are not invariant to the…

  7. Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?

    NASA Technical Reports Server (NTRS)

    Tian, Yudong; Huffman, George J.; Adler, Robert F.; Tang, Ling; Sapiano, Matthew; Maggioni, Viviana; Wu, Huan

    2013-01-01

    The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.

  8. Multiple regression approach to optimize drilling operations in the Arabian Gulf area

    SciTech Connect

    Al-Betairi, E.A.; Moussa, M.M.; Al-Otaibi, S.

    1988-03-01

    This paper reports a successful application of multiple regression analysis, supported by a detailed statistical study to verify the Bourgoyne and Young model. The model estimates the optimum penetration rate (ROP), weight on bit (WOB), and rotary speed under the effect of controllable and uncontrollable factors. Field data from three wells in the Arabian Gulf were used and emphasized the validity of this model. The model coefficients are sensitive to the number of points included. The correlation coefficients and multicollinearity sensitivity of each drilling parameter on the ROP are studied.

  9. Factors related to patients' general satisfaction with removable partial dentures: a stepwise multiple regression analysis.

    PubMed

    Zlatarić, Dubravka Knezović; Celebić, Asja

    2008-01-01

    This study aimed to analyze factors related to patients' general satisfaction with removable partial dentures (RPDs), such as esthetics, retention, speech, chewing, and comfort. A total of 103 patients with Kennedy Class I RPDs (34 to 82 years old; mean age: 63; 35 men, 68 women) assessed their satisfaction with dentures. Stepwise multiple regression analysis was used to evaluate the relationship among the factors. Significant correlations were found between general satisfaction and each of the individual components (P < .05). The patients' assessment of esthetics explained almost 50% of general satisfaction in both arches (P < .05). Esthetics, chewing, and speech had significant effects on the patients' general satisfaction with dentures.

  10. Accounting for Misclassified Outcomes in Binary Regression Models Using Multiple Imputation With Internal Validation Data

    PubMed Central

    Edwards, Jessie K.; Cole, Stephen R.; Troester, Melissa A.; Richardson, David B.

    2013-01-01

    Outcome misclassification is widespread in epidemiology, but methods to account for it are rarely used. We describe the use of multiple imputation to reduce bias when validation data are available for a subgroup of study participants. This approach is illustrated using data from 308 participants in the multicenter Herpetic Eye Disease Study between 1992 and 1998 (48% female; 85% white; median age, 49 years). The odds ratio comparing the acyclovir group with the placebo group on the gold-standard outcome (physician-diagnosed herpes simplex virus recurrence) was 0.62 (95% confidence interval (CI): 0.35, 1.09). We masked ourselves to physician diagnosis except for a 30% validation subgroup used to compare methods. Multiple imputation (odds ratio (OR) = 0.60; 95% CI: 0.24, 1.51) was compared with naive analysis using self-reported outcomes (OR = 0.90; 95% CI: 0.47, 1.73), analysis restricted to the validation subgroup (OR = 0.57; 95% CI: 0.20, 1.59), and direct maximum likelihood (OR = 0.62; 95% CI: 0.26, 1.53). In simulations, multiple imputation and direct maximum likelihood had greater statistical power than did analysis restricted to the validation subgroup, yet all 3 provided unbiased estimates of the odds ratio. The multiple-imputation approach was extended to estimate risk ratios using log-binomial regression. Multiple imputation has advantages regarding flexibility and ease of implementation for epidemiologists familiar with missing data methods. PMID:24627573

  11. Accounting for misclassified outcomes in binary regression models using multiple imputation with internal validation data.

    PubMed

    Edwards, Jessie K; Cole, Stephen R; Troester, Melissa A; Richardson, David B

    2013-05-01

    Outcome misclassification is widespread in epidemiology, but methods to account for it are rarely used. We describe the use of multiple imputation to reduce bias when validation data are available for a subgroup of study participants. This approach is illustrated using data from 308 participants in the multicenter Herpetic Eye Disease Study between 1992 and 1998 (48% female; 85% white; median age, 49 years). The odds ratio comparing the acyclovir group with the placebo group on the gold-standard outcome (physician-diagnosed herpes simplex virus recurrence) was 0.62 (95% confidence interval (CI): 0.35, 1.09). We masked ourselves to physician diagnosis except for a 30% validation subgroup used to compare methods. Multiple imputation (odds ratio (OR) = 0.60; 95% CI: 0.24, 1.51) was compared with naive analysis using self-reported outcomes (OR = 0.90; 95% CI: 0.47, 1.73), analysis restricted to the validation subgroup (OR = 0.57; 95% CI: 0.20, 1.59), and direct maximum likelihood (OR = 0.62; 95% CI: 0.26, 1.53). In simulations, multiple imputation and direct maximum likelihood had greater statistical power than did analysis restricted to the validation subgroup, yet all 3 provided unbiased estimates of the odds ratio. The multiple-imputation approach was extended to estimate risk ratios using log-binomial regression. Multiple imputation has advantages regarding flexibility and ease of implementation for epidemiologists familiar with missing data methods.

  12. Estimation of maturity of compost from food wastes and agro-residues by multiple regression analysis.

    PubMed

    Chikae, Miyuki; Ikeda, Ryuzoh; Kerman, Kagan; Morita, Yasutaka; Tamiya, Eiichi

    2006-11-01

    The composting process of food wastes and tree cuttings was examined on four composting types composed from two kinds of systems and added mixture of microorganisms. The time courses of 32 parameters in each composting type were observed. The efficient composting system was found to be the static aerated reactor system in comparison with the turning pile one. Using the multiple regression analysis of all the data (159 samples) obtained from this study, some parameters were selected to predict the germination index (GI) value, which was adopted as a marker of compost maturity. For example, using the regression model generated from pH, NH(4)(+) concentration, acid phosphatase activity, and esterase activity of water extracts of the compost, GI value was expressed by the multi-linear regression equation (p<0.0001). High correlations between the measured GI value and the predicted one were made in each type of compost. As a result of these observations, the compost maturity might be predicted by only sensing of the water extract at the composting site without any requirements for a large-size equipment and skill, and this prediction system could contribute to the production of a stable compost in wide-spread use for the recycling market.

  13. Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression.

    PubMed

    Beckstead, Jason W

    2012-03-30

    The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic strategy to isolate, examine, and remove suppression effects has been offered. In this article such an approach, rooted in confirmatory factor analysis theory and employing matrix algebra, is developed. Suppression is viewed as the result of criterion-irrelevant variance operating among predictors. Decomposition of predictor variables into criterion-relevant and criterion-irrelevant components using structural equation modeling permits derivation of regression weights with the effects of criterion-irrelevant variance omitted. Three examples with data from applied research are used to illustrate the approach: the first assesses child and parent characteristics to explain why some parents of children with obsessive-compulsive disorder accommodate their child's compulsions more so than do others, the second examines various dimensions of personal health to explain individual differences in global quality of life among patients following heart surgery, and the third deals with quantifying the relative importance of various aptitudes for explaining academic performance in a sample of nursing students. The approach is offered as an analytic tool for investigators interested in understanding predictor-criterion relationships when complex patterns of intercorrelation among predictors are present and is shown to augment dominance analysis.

  14. Removal of River-Stage Fluctuations from Well Response Using Multiple-Regression

    SciTech Connect

    Spane, Frank A.; Mackley, Rob D.

    2011-11-01

    Many contaminated unconfined aquifers are located in proximity to river systems. In groundwater studies, the physical presence of a river is commonly represented as a transient-head boundary that imposes hydrologic responses within the intersected unconfined aquifer. The periodic fluctuation of river-stage height at the boundary produces associated responses within the adjacent aquifer system, the magnitude of which is a function of the existing well, aquifer, boundary conditions, and river-stage fluctuation characteristics. The presence of well responses induced by the river stage can significantly limit characterization and monitoring of remedial activities within the stress-impacted area. This paper demonstrates the use of a time-domain, multiple-regression, convolution (superposition) method to develop well/aquifer river response function (RRF) relationships. Following RRF development, a multiple-regression deconvolution correction approach can be applied to remove river-stage effects from well water-level responses. Corrected well responses can then be analyzed to improve local aquifer characterization activities in support of optimizing remedial actions, assessing the area-of-influence of remediation activities, and determining mean groundwater flow and contaminant flux to the river system.

  15. A note on the use of multiple linear regression in molecular ecology.

    PubMed

    Frasier, Timothy R

    2016-03-01

    Multiple linear regression analyses (also often referred to as generalized linear models--GLMs, or generalized linear mixed models--GLMMs) are widely used in the analysis of data in molecular ecology, often to assess the relative effects of genetic characteristics on individual fitness or traits, or how environmental characteristics influence patterns of genetic differentiation. However, the coefficients resulting from multiple regression analyses are sometimes misinterpreted, which can lead to incorrect interpretations and conclusions within individual studies, and can propagate to wider-spread errors in the general understanding of a topic. The primary issue revolves around the interpretation of coefficients for independent variables when interaction terms are also included in the analyses. In this scenario, the coefficients associated with each independent variable are often interpreted as the independent effect of each predictor variable on the predicted variable. However, this interpretation is incorrect. The correct interpretation is that these coefficients represent the effect of each predictor variable on the predicted variable when all other predictor variables are zero. This difference may sound subtle, but the ramifications cannot be overstated. Here, my goals are to raise awareness of this issue, to demonstrate and emphasize the problems that can result and to provide alternative approaches for obtaining the desired information.

  16. Prediction of groundwater table and salinity fluctuations with a time series multiple regression technique

    NASA Astrophysics Data System (ADS)

    Seeboonruang, U.

    2013-12-01

    Time series techniques have been extensively applied to research works of many academic disciplines, particularly those concerned with economics and environment. This paper presents application of a time series multiple linear regression technique to a groundwater system to predict groundwater level and salinity fluctuations in a saline area in the northeastern part of Thailand. Surface and groundwater interaction is the major mechanism controlling the shallow subsurface system and salinity of the area. The basic technique is based on the lagged correlation between hydrologic, and hydrogeological and environmental parameters. As a result of a large irrigation project in the area, several regulating gates have been installed to control flooding to the downstream rivers and to provide the upstream areas with sufficient irrigating water. From the lagged correlation analysis, the shallow groundwater and groundwater salinity fluctuation in the irrigating area are shown to be dependent upon the surface water levels at the installed regulated gates and prior rainfall. A set of multiple linear regression equations with lagged time dependent function are then formulated. The dependent variables are groundwater level and groundwater salinity while the independent variables are rainfall rates and water levels measured at the regulating gates. After calibration and verification, the model, as an alternative to the conventional method which requires detailed and continuous variables and is costlier, can be used to forecast and manage future groundwater systems.

  17. Waste generated in high-rise buildings construction: a quantification model based on statistical multiple regression.

    PubMed

    Parisi Kern, Andrea; Ferreira Dias, Michele; Piva Kulakowski, Marlova; Paulo Gomes, Luciana

    2015-05-01

    Reducing construction waste is becoming a key environmental issue in the construction industry. The quantification of waste generation rates in the construction sector is an invaluable management tool in supporting mitigation actions. However, the quantification of waste can be a difficult process because of the specific characteristics and the wide range of materials used in different construction projects. Large variations are observed in the methods used to predict the amount of waste generated because of the range of variables involved in construction processes and the different contexts in which these methods are employed. This paper proposes a statistical model to determine the amount of waste generated in the construction of high-rise buildings by assessing the influence of design process and production system, often mentioned as the major culprits behind the generation of waste in construction. Multiple regression was used to conduct a case study based on multiple sources of data of eighteen residential buildings. The resulting statistical model produced dependent (i.e. amount of waste generated) and independent variables associated with the design and the production system used. The best regression model obtained from the sample data resulted in an adjusted R(2) value of 0.694, which means that it predicts approximately 69% of the factors involved in the generation of waste in similar constructions. Most independent variables showed a low determination coefficient when assessed in isolation, which emphasizes the importance of assessing their joint influence on the response (dependent) variable.

  18. Removal of river-stage fluctuations from well response using multiple regression.

    PubMed

    Spane, Frank A; Mackley, Rob D

    2011-01-01

    Many contaminated unconfined aquifers are located in proximity to river systems. In groundwater studies, the physical presence of a river is commonly represented as a transient-head boundary that imposes hydrologic responses within the intersected unconfined aquifer. The periodic fluctuation of river-stage height at the boundary produces associated responses within the adjacent aquifer system, the magnitude of which is a function of the existing well, aquifer, boundary conditions, and characteristics of river-stage fluctuations. The presence of well responses induced by the river stage can significantly limit characterization and monitoring of remedial activities within the stress-impacted area. This article demonstrates the use of a time-domain, multiple-regression, convolution (superposition) method to develop well/aquifer river response function (RRF) relationships. Following RRF development, a multiple-regression deconvolution correction approach can be applied to remove river-stage effects from well water-level responses. Corrected well responses can then be analyzed to improve local aquifer characterization activities in support of optimizing remedial actions, assessing the area-of-influence of remediation activities, and determining mean groundwater flow and contaminant flux to the river system.

  19. Performance Evaluation of Button Bits in Coal Measure Rocks by Using Multiple Regression Analyses

    NASA Astrophysics Data System (ADS)

    Su, Okan

    2016-02-01

    Electro-hydraulic and jumbo drills are commonly used for underground coal mines and tunnel drives for the purpose of blasthole drilling and rock bolt installations. Not only machine parameters but also environmental conditions have significant effects on drilling. This study characterizes the performance of button bits during blasthole drilling in coal measure rocks by using multiple regression analyses. The penetration rate of jumbo and electro-hydraulic drills was measured in the field by employing bits in different diameters and the specific energy of the drilling was calculated at various locations, including highway tunnels and underground roadways of coal mines. Large block samples were collected from each location at which in situ drilling measurements were performed. Then, the effects of rock properties and machine parameters on the drilling performance were examined. Multiple regression models were developed for the prediction of the specific energy of the drilling and the penetration rate. The results revealed that hole area, impact (blow) energy, blows per minute of the piston within the drill, and some rock properties, such as the uniaxial compressive strength (UCS) and the drilling rate index (DRI), influence the drill performance.

  20. Semiparametric Allelic Tests for Mapping Multiple Phenotypes: Binomial Regression and Mahalanobis Distance.

    PubMed

    Majumdar, Arunabha; Witte, John S; Ghosh, Saurabh

    2015-12-01

    Binary phenotypes commonly arise due to multiple underlying quantitative precursors and genetic variants may impact multiple traits in a pleiotropic manner. Hence, simultaneously analyzing such correlated traits may be more powerful than analyzing individual traits. Various genotype-level methods, e.g., MultiPhen (O'Reilly et al. []), have been developed to identify genetic factors underlying a multivariate phenotype. For univariate phenotypes, the usefulness and applicability of allele-level tests have been investigated. The test of allele frequency difference among cases and controls is commonly used for mapping case-control association. However, allelic methods for multivariate association mapping have not been studied much. In this article, we explore two allelic tests of multivariate association: one using a Binomial regression model based on inverted regression of genotype on phenotype (Binomial regression-based Association of Multivariate Phenotypes [BAMP]), and the other employing the Mahalanobis distance between two sample means of the multivariate phenotype vector for two alleles at a single-nucleotide polymorphism (Distance-based Association of Multivariate Phenotypes [DAMP]). These methods can incorporate both discrete and continuous phenotypes. Some theoretical properties for BAMP are studied. Using simulations, the power of the methods for detecting multivariate association is compared with the genotype-level test MultiPhen's. The allelic tests yield marginally higher power than MultiPhen for multivariate phenotypes. For one/two binary traits under recessive mode of inheritance, allelic tests are found to be substantially more powerful. All three tests are applied to two different real data and the results offer some support for the simulation study. We propose a hybrid approach for testing multivariate association that implements MultiPhen when Hardy-Weinberg Equilibrium (HWE) is violated and BAMP otherwise, because the allelic approaches assume HWE.

  1. Estimating leaf photosynthetic pigments information by stepwise multiple linear regression analysis and a leaf optical model

    NASA Astrophysics Data System (ADS)

    Liu, Pudong; Shi, Runhe; Wang, Hong; Bai, Kaixu; Gao, Wei

    2014-10-01

    Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.

  2. Addition of multiple limiting resources reduces grassland diversity.

    PubMed

    Harpole, W Stanley; Sullivan, Lauren L; Lind, Eric M; Firn, Jennifer; Adler, Peter B; Borer, Elizabeth T; Chase, Jonathan; Fay, Philip A; Hautier, Yann; Hillebrand, Helmut; MacDougall, Andrew S; Seabloom, Eric W; Williams, Ryan; Bakker, Jonathan D; Cadotte, Marc W; Chaneton, Enrique J; Chu, Chengjin; Cleland, Elsa E; D'Antonio, Carla; Davies, Kendi F; Gruner, Daniel S; Hagenah, Nicole; Kirkman, Kevin; Knops, Johannes M H; La Pierre, Kimberly J; McCulley, Rebecca L; Moore, Joslin L; Morgan, John W; Prober, Suzanne M; Risch, Anita C; Schuetz, Martin; Stevens, Carly J; Wragg, Peter D

    2016-09-01

    Niche dimensionality provides a general theoretical explanation for biodiversity-more niches, defined by more limiting factors, allow for more ways that species can coexist. Because plant species compete for the same set of limiting resources, theory predicts that addition of a limiting resource eliminates potential trade-offs, reducing the number of species that can coexist. Multiple nutrient limitation of plant production is common and therefore fertilization may reduce diversity by reducing the number or dimensionality of belowground limiting factors. At the same time, nutrient addition, by increasing biomass, should ultimately shift competition from belowground nutrients towards a one-dimensional competitive trade-off for light. Here we show that plant species diversity decreased when a greater number of limiting nutrients were added across 45 grassland sites from a multi-continent experimental network. The number of added nutrients predicted diversity loss, even after controlling for effects of plant biomass, and even where biomass production was not nutrient-limited. We found that elevated resource supply reduced niche dimensionality and diversity and increased both productivity and compositional turnover. Our results point to the importance of understanding dimensionality in ecological systems that are undergoing diversity loss in response to multiple global change factors.

  3. Correlation between mRNA and protein abundance in Desulfovibrio vulgaris: A multiple regression to identify sources of variations

    SciTech Connect

    Nie, Lei; Wu, G; Zhang, Weiwen

    2006-01-13

    Using whole-genome microarray and LC-MC/MS proteomic data collected from Desulfovibrio vulgaris grown under three different conditions, we systematically investigate the relationship between mRNA and protein abundunce by a multiple regression approach.

  4. Determining the Spatial and Seasonal Variability in OM/OC Ratios across the U.S. Using Multiple Regression

    EPA Science Inventory

    Data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network are used to estimate organic mass to organic carbon (OM/OC) ratios across the United States by extending previously published multiple regression techniques. Our new methodology addresses com...

  5. Using the Coefficient of Determination "R"[superscript 2] to Test the Significance of Multiple Linear Regression

    ERIC Educational Resources Information Center

    Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.

    2013-01-01

    This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)

  6. Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

    NASA Astrophysics Data System (ADS)

    Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

    2013-06-01

    This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

  7. Additional results on 'Reducing geometric dilution of precision using ridge regression'

    NASA Astrophysics Data System (ADS)

    Kelly, Robert J.

    1990-07-01

    Kelly (1990) presented preliminary results on the feasibility of using ridge regression (RR) to reduce the effects of geometric dilution of precision (GDOP) error inflation in position-fix navigation systems. Recent results indicate that RR will not reduce GDOP bias inflation when biaslike measurement errors last much longer than the aircraft guidance-loop response time. This conclusion precludes the use of RR on navigation systems whose dominant error sources are biaslike; e.g., the GPS selective-availability error source. The simulation results given by Kelly are, however, valid for the conditions defined. Although RR has not yielded a satisfactory solution to the general GDOP problem, it has illuminated the role that multicollinearity plays in navigation signal processors such as the Kalman filter. Bias inflation, initial position guess errors, ridge-parameter selection methodology, and the recursive ridge filter are discussed.

  8. How to retrieve additional information from the multiplicity distributions

    NASA Astrophysics Data System (ADS)

    Wilk, Grzegorz; Włodarczyk, Zbigniew

    2017-01-01

    Multiplicity distributions (MDs) P(N) measured in multiparticle production processes are most frequently described by the negative binomial distribution (NBD). However, with increasing collision energy some systematic discrepancies have become more and more apparent. They are usually attributed to the possible multi-source structure of the production process and described using a multi-NBD form of the MD. We investigate the possibility of keeping a single NBD but with its parameters depending on the multiplicity N. This is done by modifying the widely known clan model of particle production leading to the NBD form of P(N). This is then confronted with the approach based on the so-called cascade-stochastic formalism which is based on different types of recurrence relations defining P(N). We demonstrate that a combination of both approaches allows the retrieval of additional valuable information from the MDs, namely the oscillatory behavior of the counting statistics apparently visible in the high energy data.

  9. Melanin and blood concentration in human skin studied by multiple regression analysis: experiments.

    PubMed

    Shimada, M; Yamada, Y; Itoh, M; Yatagai, T

    2001-09-01

    Knowledge of the mechanism of human skin colour and measurement of melanin and blood concentration in human skin are needed in the medical and cosmetic fields. The absorbance spectrum from reflectance at the visible wavelength of human skin increases under several conditions such as a sunburn or scalding. The change of the absorbance spectrum from reflectance including the scattering effect does not correspond to the molar absorption spectrum of melanin and blood. The modified Beer-Lambert law is applied to the change in the absorbance spectrum from reflectance of human skin as the change in melanin and blood is assumed to be small. The concentration of melanin and blood was estimated from the absorbance spectrum reflectance of human skin using multiple regression analysis. Estimated concentrations were compared with the measured one in a phantom experiment and this method was applied to in vivo skin.

  10. Transformation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques.

    PubMed

    Ghazali, Nurul Adyani; Ramli, Nor Azam; Yahaya, Ahmad Shukri; Yusof, Noor Faizah Fitri M D; Sansuddin, Nurulilyana; Al Madhoun, Wesam Ahmed

    2010-06-01

    Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. This study examines transformation of nitrogen dioxide (NO(2)) into ozone (O(3)) at urban environment using time series plot. Data on the concentration of environmental pollutants and meteorological variables were employed to predict the concentration of O(3) in the atmosphere. Possibility of employing multiple linear regression models as a tool for prediction of O(3) concentration was tested. Results indicated that the presence of NO(2) and sunshine influence the concentration of O(3) in Malaysia. The influence of the previous hour ozone on the next hour concentrations was also demonstrated.

  11. Melanin and blood concentration in human skin studied by multiple regression analysis: experiments

    NASA Astrophysics Data System (ADS)

    Shimada, M.; Yamada, Y.; Itoh, M.; Yatagai, T.

    2001-09-01

    Knowledge of the mechanism of human skin colour and measurement of melanin and blood concentration in human skin are needed in the medical and cosmetic fields. The absorbance spectrum from reflectance at the visible wavelength of human skin increases under several conditions such as a sunburn or scalding. The change of the absorbance spectrum from reflectance including the scattering effect does not correspond to the molar absorption spectrum of melanin and blood. The modified Beer-Lambert law is applied to the change in the absorbance spectrum from reflectance of human skin as the change in melanin and blood is assumed to be small. The concentration of melanin and blood was estimated from the absorbance spectrum reflectance of human skin using multiple regression analysis. Estimated concentrations were compared with the measured one in a phantom experiment and this method was applied to in vivo skin.

  12. Relationship between academic stress and suicidal ideation: testing for depression as a mediator using multiple regression.

    PubMed

    Ang, Rebecca P; Huan, Vivien S

    2006-01-01

    Relations among academic stress, depression, and suicidal ideation were examined in 1,108 Asian adolescents 12-18 years old from a secondary school in Singapore. Using Baron and Kenny's [J Pers Soc Psychol 51:1173-1192, 1986] framework, this study tested the prediction that adolescent depression mediated the relationship between academic stress and suicidal ideation in a four-step process. The previously significant relationship between academic stress and suicidal ideation was significantly reduced in magnitude when depression was included in the model providing evidence in this sample that adolescent depression was a partial mediator. The applied and practical implications for intervention and prevention work in schools are discussed. The present investigation also served as a demonstration to illustrate how multiple regression analyses can be used as one possible method for testing mediation effects within child psychology and psychiatry.

  13. Spontaneous Regression of Multiple Pulmonary Metastases After Radiofrequency Ablation of a Single Metastasis

    SciTech Connect

    Rao, Pramod; Escudier, Bernard; Baere, Thierry de

    2011-04-15

    We report two cases of spontaneous regression of multiple pulmonary metastases occurring after radiofrequency ablation (RFA) of a single lung metastasis. To the best of our knowledge, these are the first such cases reported. These two patients presented with lung metastases progressive despite treatment with interleukin-2, interferon, or sorafenib but were safely ablated with percutaneous RFA under computed tomography guidance. Percutaneous RFA allowed control of the targeted tumors for >1 year. Distant lung metastases presented an objective response despite the fact that they received no targeted local treatment. Local ablative techniques, such as RFA, induce the release of tumor-degradation product, which is probably responsible for an immunologic reaction that is able to produce a response in distant tumors.

  14. Multiple linear and principal component regressions for modelling ecotoxicity bioassay response.

    PubMed

    Gomes, Ana I; Pires, José C M; Figueiredo, Sónia A; Boaventura, Rui A R

    2014-01-01

    The ecotoxicological response of the living organisms in an aquatic system depends on the physical, chemical and bacteriological variables, as well as the interactions between them. An important challenge to scientists is to understand the interaction and behaviour of factors involved in a multidimensional process such as the ecotoxicological response. With this aim, multiple linear regression (MLR) and principal component regression were applied to the ecotoxicity bioassay response of Chlorella vulgaris and Vibrio fischeri in water collected at seven sites of Leça river during five monitoring campaigns (February, May, June, August and September of 2006). The river water characterization included the analysis of 22 physicochemical and 3 microbiological parameters. The model that best fitted the data was MLR, which shows: (i) a negative correlation with dissolved organic carbon, zinc and manganese, and a positive one with turbidity and arsenic, regarding C. vulgaris toxic response; (ii) a negative correlation with conductivity and turbidity and a positive one with phosphorus, hardness, iron, mercury, arsenic and faecal coliforms, concerning V. fischeri toxic response. This integrated assessment may allow the evaluation of the effect of future pollution abatement measures over the water quality of Leça River.

  15. Genetic-algorithm-based multiple regression with fuzzy inference system for detection of nocturnal hypoglycemic episodes.

    PubMed

    Ling, Steve S H; Nguyen, Hung T

    2011-03-01

    Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures, and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemic monitor is a noninvasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients (T1DM). Based on heart rate (HR), corrected QT interval of the ECG signal, change of HR, and the change of corrected QT interval, we develop a genetic algorithm (GA)-based multiple regression with fuzzy inference system (FIS) to classify the presence of hypoglycemic episodes. GA is used to find the optimal fuzzy rules and membership functions of FIS and the model parameters of regression method. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes is associated with HRs and corrected QT intervals. The overall data were organized into a training set (eight patients) and a testing set (another eight patients) randomly selected. The results show that the proposed algorithm performs a good sensitivity with an acceptable specificity.

  16. Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland.

    PubMed

    Kolasa-Wiecek, Alicja

    2015-04-01

    The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO2 emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH4 emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (-0.64) as the most important variables. The adjusted coefficient is suitable and equals R2=0.90. For N2O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N2O emission is the peat and wood fuel consumption.

  17. Evaluation of dominance-based ordinal multiple regression for variables with few categories.

    PubMed

    Woods, Carol M

    2013-02-01

    Dominance-based ordinal multiple regression (DOR) is designed to answer ordinal questions about relationships among ordinal variables. Only one parameter per predictor is estimated, and the number of parameters is constant for any number of outcome levels. The majority of existing simulation evaluations of DOR use predictors that are continuous or ordinal with many categories, so the performance of the method is not well understood for ordinal variables with few categories. This research evaluates DOR in simulations using three-category ordinal variables for the outcome and predictors, with a comparison to the cumulative logits proportional odds model (POC). Although ordinary least squares (OLS) regression is inapplicable for theoretical reasons, it was also included in the simulations because of its popularity in the social sciences. Most simulation outcomes indicated that DOR performs well for variables with few categories, and is preferable to the POC for smaller samples and when the proportional odds assumption is violated. Nevertheless, confidence interval coverage for DOR was not flawless and possibilities for improvement are suggested.

  18. Modeling the Philippines' real gross domestic product: A normal estimation equation for multiple linear regression

    NASA Astrophysics Data System (ADS)

    Urrutia, Jackie D.; Tampis, Razzcelle L.; Mercado, Joseph; Baygan, Aaron Vito M.; Baccay, Edcon B.

    2016-02-01

    The objective of this research is to formulate a mathematical model for the Philippines' Real Gross Domestic Product (Real GDP). The following factors are considered: Consumers' Spending (x1), Government's Spending (x2), Capital Formation (x3) and Imports (x4) as the Independent Variables that can actually influence in the Real GDP in the Philippines (y). The researchers used a Normal Estimation Equation using Matrices to create the model for Real GDP and used α = 0.01.The researchers analyzed quarterly data from 1990 to 2013. The data were acquired from the National Statistical Coordination Board (NSCB) resulting to a total of 96 observations for each variable. The data have undergone a logarithmic transformation particularly the Dependent Variable (y) to satisfy all the assumptions of the Multiple Linear Regression Analysis. The mathematical model for Real GDP was formulated using Matrices through MATLAB. Based on the results, only three of the Independent Variables are significant to the Dependent Variable namely: Consumers' Spending (x1), Capital Formation (x3) and Imports (x4), hence, can actually predict Real GDP (y). The regression analysis displays that 98.7% (coefficient of determination) of the Independent Variables can actually predict the Dependent Variable. With 97.6% of the result in Paired T-Test, the Predicted Values obtained from the model showed no significant difference from the Actual Values of Real GDP. This research will be essential in appraising the forthcoming changes to aid the Government in implementing policies for the development of the economy.

  19. Multiple Regression (MR) and Artificial Neural Network (ANN) models for prediction of soil suction

    NASA Astrophysics Data System (ADS)

    Erzin, Yusuf; Yilmaz, Isik

    2010-05-01

    This article presents a comparison of multiple regression (MR) and artificial neural network (ANN) model for prediction of soil suction of clayey soils. The results of the soil suction tests utilizing thermocouple psychrometers on statically compacted specimens of Bentonite-Kaolinite clay mixtures with varying soil properties were used to develope the models. The results obtained from both models were then compared with the experimental results. The performance indices such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and variance account for (VAF) were used to control the performance of the prediction capacity of the models developed in this study. ANN model has shown higher prediction performance than regression model according to the performance indices. It is shown that ANN models provide significant improvements in prediction accuracy over statistical models. The potential benefits of soft computing models extend beyond the high computation rates. Higher performances of the soft computing models were sourced from greater degree of robustness and fault tolerance than traditional statistical models because there are many more processing neurons, each with primarily local connections. It appears that there is a possibility of estimating soil suction by using the proposed empirical relationships and soft computing models. The population of the analyzed data is relatively limited in this study. Therefore, the practical outcome of the proposed equations and models could be used, with acceptable accuracy.

  20. Multiple regression based imputation for individualizing template human model from a small number of measured dimensions.

    PubMed

    Nohara, Ryuki; Endo, Yui; Murai, Akihiko; Takemura, Hiroshi; Kouchi, Makiko; Tada, Mitsunori

    2016-08-01

    Individual human models are usually created by direct 3D scanning or deforming a template model according to the measured dimensions. In this paper, we propose a method to estimate all the necessary dimensions (full set) for the human model individualization from a small number of measured dimensions (subset) and human dimension database. For this purpose, we solved multiple regression equation from the dimension database given full set dimensions as the objective variable and subset dimensions as the explanatory variables. Thus, the full set dimensions are obtained by simply multiplying the subset dimensions to the coefficient matrix of the regression equation. We verified the accuracy of our method by imputing hand, foot, and whole body dimensions from their dimension database. The leave-one-out cross validation is employed in this evaluation. The mean absolute errors (MAE) between the measured and the estimated dimensions computed from 4 dimensions (hand length, breadth, middle finger breadth at proximal, and middle finger depth at proximal) in the hand, 2 dimensions (foot length, breadth, and lateral malleolus height) in the foot, and 1 dimension (height) and weight in the whole body are computed. The average MAE of non-measured dimensions were 4.58% in the hand, 4.42% in the foot, and 3.54% in the whole body, while that of measured dimensions were 0.00%.

  1. 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.

  2. A Meta-Regression Method for Studying Etiological Heterogeneity Across Disease Subtypes Classified by Multiple Biomarkers

    PubMed Central

    Wang, Molin; Kuchiba, Aya; Ogino, Shuji

    2015-01-01

    In interdisciplinary biomedical, epidemiologic, and population research, it is increasingly necessary to consider pathogenesis and inherent heterogeneity of any given health condition and outcome. As the unique disease principle implies, no single biomarker can perfectly define disease subtypes. The complex nature of molecular pathology and biology necessitates biostatistical methodologies to simultaneously analyze multiple biomarkers and subtypes. To analyze and test for heterogeneity hypotheses across subtypes defined by multiple categorical and/or ordinal markers, we developed a meta-regression method that can utilize existing statistical software for mixed-model analysis. This method can be used to assess whether the exposure-subtype associations are different across subtypes defined by 1 marker while controlling for other markers and to evaluate whether the difference in exposure-subtype association across subtypes defined by 1 marker depends on any other markers. To illustrate this method in molecular pathological epidemiology research, we examined the associations between smoking status and colorectal cancer subtypes defined by 3 correlated tumor molecular characteristics (CpG island methylator phenotype, microsatellite instability, and the B-Raf protooncogene, serine/threonine kinase (BRAF), mutation) in the Nurses' Health Study (1980–2010) and the Health Professionals Follow-up Study (1986–2010). This method can be widely useful as molecular diagnostics and genomic technologies become routine in clinical medicine and public health. PMID:26116215

  3. The Addition of Low-dose Thalidomide to Bortezomib and Dexamethasone for Refractory Multiple Myeloma

    PubMed Central

    Hashimoto, Shigeo; Kuroha, Takashi; Yano, Toshio; Sato, Naoko; Furukawa, Tatsuo

    2016-01-01

    Five cases were treated by adding daily low-dose thalidomide (50 mg) to bortezomib and dexamethasone therapy for refractory multiple myeloma. This therapy was effective in four cases, with an improvement of bone pain and regression of M-protein. One case was treated with cyclophosphamide, thalidomide, and dexamethasone, adding bortezomib after starting the three-drug combination therapy. This patient has remained in a stable disease state since the beginning of this therapy. Regarding the other four cases, a partial response and a prolonged survival for approximately one year were noted. Peripheral neuropathy did not increase after thalidomide addition. Adding low-dose thalidomide may safely improve the responses for multiple myeloma refractory to bortezomib and dexamethasone. PMID:27746443

  4. The Addition of Low-dose Thalidomide to Bortezomib and Dexamethasone for Refractory Multiple Myeloma.

    PubMed

    Hashimoto, Shigeo; Kuroha, Takashi; Yano, Toshio; Sato, Naoko; Furukawa, Tatsuo

    Five cases were treated by adding daily low-dose thalidomide (50 mg) to bortezomib and dexamethasone therapy for refractory multiple myeloma. This therapy was effective in four cases, with an improvement of bone pain and regression of M-protein. One case was treated with cyclophosphamide, thalidomide, and dexamethasone, adding bortezomib after starting the three-drug combination therapy. This patient has remained in a stable disease state since the beginning of this therapy. Regarding the other four cases, a partial response and a prolonged survival for approximately one year were noted. Peripheral neuropathy did not increase after thalidomide addition. Adding low-dose thalidomide may safely improve the responses for multiple myeloma refractory to bortezomib and dexamethasone.

  5. A New Measurement Equivalence Technique Based on Latent Class Regression as Compared with Multiple Indicators Multiple Causes

    PubMed Central

    Jamali, Jamshid; Ayatollahi, Seyyed Mohammad Taghi; Jafari, Peyman

    2016-01-01

    Background: Measurement equivalence is an essential prerequisite for making valid comparisons in mental health questionnaires across groups. In most methods used for assessing measurement equivalence, which is known as Differential Item Functioning (DIF), latent variables are assumed to be continuous. Objective: To compare a new method called Latent Class Regression (LCR) designed for discrete latent variable with the multiple indicators multiple cause (MIMIC) as a continuous latent variable technique to assess the measurement equivalence of the 12-item General Health Questionnaire (GHQ-12), which is a cross deferent subgroup of Iranian nurses. Methods: A cross-sectional survey was conducted in 2014 among 771 nurses working in the hospitals of Fars and Bushehr provinces of southern Iran. To identify the Minor Psychiatric Disorders (MPD), the nurses completed self-report GHQ-12 questionnaires and sociodemographic questions. Two uniform-DIF detection methods, LCR and MIMIC, were applied for comparability when the GHQ-12 score was assumed to be discrete and continuous, respectively. Results: The result of fitting LCR with 2 classes indicated that 27.4% of the nurses had MPD. Gender was identified as an influential factor of the level of MPD.LCR and MIMIC agree with detection of DIF and DIF-free items by gender, age, education and marital status in 83.3, 100.0, 91.7 and 83.3% cases, respectively. Conclusions: The results indicated that the GHQ-12 is to a great degree, an invariant measure for the assessment of MPD among nurses. High convergence between the two methods suggests using the LCR approach in cases of discrete latent variable, e.g. GHQ-12 and adequate sample size. PMID:27482129

  6. Statistical Downscaling: A Comparison of Multiple Linear Regression and k-Nearest Neighbor Approaches

    NASA Astrophysics Data System (ADS)

    Gangopadhyay, S.; Clark, M. P.; Rajagopalan, B.

    2002-12-01

    The success of short term (days to fortnight) streamflow forecasting largely depends on the skill of surface climate (e.g., precipitation and temperature) forecasts at local scales in the individual river basins. The surface climate forecasts are used to drive the hydrologic models for streamflow forecasting. Typically, Medium Range Forecast (MRF) models provide forecasts of large scale circulation variables (e.g. pressures, wind speed, relative humidity etc.) at different levels in the atmosphere on a regular grid - which are then used to "downscale" to the surface climate at locations within the model grid box. Several statistical and dynamical methods are available for downscaling. This paper compares the utility of two statistical downscaling methodologies: (1) multiple linear regression (MLR) and (2) a nonparametric approach based on k-nearest neighbor (k-NN) bootstrap method, in providing local-scale information of precipitation and temperature at a network of stations in the Upper Colorado River Basin. Downscaling to the stations is based on output of large scale circulation variables (i.e. predictors) from the NCEP Medium Range Forecast (MRF) database. Fourteen-day six hourly forecasts are developed using these two approaches, and their forecast skill evaluated. A stepwise regression is performed at each location to select the predictors for the MLR. The k-NN bootstrap technique resamples historical data based on their "nearness" to the current pattern in the predictor space. Prior to resampling a Principal Component Analysis (PCA) is performed on the predictor set to identify a small subset of predictors. Preliminary results using the MLR technique indicate a significant value in the downscaled MRF output in predicting runoff in the Upper Colorado Basin. It is expected that the k-NN approach will match the skill of the MLR approach at individual stations, and will have the added advantage of preserving the spatial co-variability between stations, capturing

  7. Brain networks of temporal preparation: A multiple regression analysis of neuropsychological data.

    PubMed

    Triviño, Mónica; Correa, Ángel; Lupiáñez, Juan; Funes, María Jesús; Catena, Andrés; He, Xun; Humphreys, Glyn W

    2016-11-15

    There are only a few studies on the brain networks involved in the ability to prepare in time, and most of them followed a correlational rather than a neuropsychological approach. The present neuropsychological study performed multiple regression analysis to address the relationship between both grey and white matter (measured by magnetic resonance imaging in patients with brain lesion) and different effects in temporal preparation (Temporal orienting, Foreperiod and Sequential effects). Two versions of a temporal preparation task were administered to a group of 23 patients with acquired brain injury. In one task, the cue presented (a red versus green square) to inform participants about the time of appearance (early versus late) of a target stimulus was blocked, while in the other task the cue was manipulated on a trial-by-trial basis. The duration of the cue-target time intervals (400 versus 1400ms) was always manipulated within blocks in both tasks. Regression analysis were conducted between either the grey matter lesion size or the white matter tracts disconnection and the three temporal preparation effects separately. The main finding was that each temporal preparation effect was predicted by a different network of structures, depending on cue expectancy. Specifically, the Temporal orienting effect was related to both prefrontal and temporal brain areas. The Foreperiod effect was related to right and left prefrontal structures. Sequential effects were predicted by both parietal cortex and left subcortical structures. These findings show a clear dissociation of brain circuits involved in the different ways to prepare in time, showing for the first time the involvement of temporal areas in the Temporal orienting effect, as well as the parietal cortex in the Sequential effects.

  8. 10 km running performance predicted by a multiple linear regression model with allometrically adjusted variables

    PubMed Central

    Abad, Cesar C. C.; Barros, Ronaldo V.; Bertuzzi, Romulo; Gagliardi, João F. L.; Lima-Silva, Adriano E.; Lambert, Mike I.

    2016-01-01

    Abstract The aim of this study was to verify the power of VO2max, peak treadmill running velocity (PTV), and running economy (RE), unadjusted or allometrically adjusted, in predicting 10 km running performance. Eighteen male endurance runners performed: 1) an incremental test to exhaustion to determine VO2max and PTV; 2) a constant submaximal run at 12 km·h−1 on an outdoor track for RE determination; and 3) a 10 km running race. Unadjusted (VO2max, PTV and RE) and adjusted variables (VO2max0.72, PTV0.72 and RE0.60) were investigated through independent multiple regression models to predict 10 km running race time. There were no significant correlations between 10 km running time and either the adjusted or unadjusted VO2max. Significant correlations (p < 0.01) were found between 10 km running time and adjusted and unadjusted RE and PTV, providing models with effect size > 0.84 and power > 0.88. The allometrically adjusted predictive model was composed of PTV0.72 and RE0.60 and explained 83% of the variance in 10 km running time with a standard error of the estimate (SEE) of 1.5 min. The unadjusted model composed of a single PVT accounted for 72% of the variance in 10 km running time (SEE of 1.9 min). Both regression models provided powerful estimates of 10 km running time; however, the unadjusted PTV may provide an uncomplicated estimation. PMID:28149382

  9. Illustrating the Use of Nonparametric Regression To Assess Differential Item and Bundle Functioning among Multiple Groups.

    ERIC Educational Resources Information Center

    Gierl, Mark J.; Bolt, Daniel M.

    2001-01-01

    Presents an overview of nonparametric regression as it allies to differential item functioning analysis and then provides three examples to illustrate how nonparametric regression can be applied to multilingual, multicultural data to study group differences. (SLD)

  10. PARAMETRIC AND NON PARAMETRIC (MARS: MULTIVARIATE ADDITIVE REGRESSION SPLINES) LOGISTIC REGRESSIONS FOR PREDICTION OF A DICHOTOMOUS RESPONSE VARIABLE WITH AN EXAMPLE FOR PRESENCE/ABSENCE OF AMPHIBIANS

    EPA Science Inventory

    The purpose of this report is to provide a reference manual that could be used by investigators for making informed use of logistic regression using two methods (standard logistic regression and MARS). The details for analyses of relationships between a dependent binary response ...

  11. Optimization of end-members used in multiple linear regression geochemical mixing models

    NASA Astrophysics Data System (ADS)

    Dunlea, Ann G.; Murray, Richard W.

    2015-11-01

    Tracking marine sediment provenance (e.g., of dust, ash, hydrothermal material, etc.) provides insight into contemporary ocean processes and helps construct paleoceanographic records. In a simple system with only a few end-members that can be easily quantified by a unique chemical or isotopic signal, chemical ratios and normative calculations can help quantify the flux of sediment from the few sources. In a more complex system (e.g., each element comes from multiple sources), more sophisticated mixing models are required. MATLAB codes published in Pisias et al. solidified the foundation for application of a Constrained Least Squares (CLS) multiple linear regression technique that can use many elements and several end-members in a mixing model. However, rigorous sensitivity testing to check the robustness of the CLS model is time and labor intensive. MATLAB codes provided in this paper reduce the time and labor involved and facilitate finding a robust and stable CLS model. By quickly comparing the goodness of fit between thousands of different end-member combinations, users are able to identify trends in the results that reveal the CLS solution uniqueness and the end-member composition precision required for a good fit. Users can also rapidly check that they have the appropriate number and type of end-members in their model. In the end, these codes improve the user's confidence that the final CLS model(s) they select are the most reliable solutions. These advantages are demonstrated by application of the codes in two case studies of well-studied datasets (Nazca Plate and South Pacific Gyre).

  12. A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression

    PubMed Central

    Nicolaou, Nicoletta; Constandinou, Timothy G.

    2016-01-01

    Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, CNPMR, Autoregressive modeling is replaced by Nonparametric Multiplicative Regression (NPMR). NPMR quantifies interactions between a response variable (effect) and a set of predictor variables (cause); here, we modified NPMR for model prediction. We also demonstrate how a particular measure, the sensitivity Q, could be used to reveal the structure of the underlying causal relationships. We apply CNPMR on artificial data with known ground truth (5 datasets), as well as physiological data (2 datasets). CNPMR correctly identifies both linear and nonlinear causal connections that are present in the artificial data, as well as physiologically relevant connectivity in the real data, and does not seem to be affected by filtering. The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. CNPMR is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations without any modifications to the main method. Its nonpametric nature, its ability to capture nonlinear relationships and its robustness to filtering make it appealing for a number of applications. PMID:27378901

  13. Multiple regression method to determine aerosol optical depth in atmospheric column in Penang, Malaysia

    NASA Astrophysics Data System (ADS)

    Tan, F.; Lim, H. S.; Abdullah, K.; Yoon, T. L.; Zubir Matjafri, M.; Holben, B.

    2014-02-01

    Aerosol optical depth (AOD) from AERONET data has a very fine resolution but air pollution index (API), visibility and relative humidity from the ground truth measurements are coarse. To obtain the local AOD in the atmosphere, the relationship between these three parameters was determined using multiple regression analysis. The data of southwest monsoon period (August to September, 2012) taken in Penang, Malaysia, was used to establish a quantitative relationship in which the AOD is modeled as a function of API, relative humidity, and visibility. The highest correlated model was used to predict AOD values during southwest monsoon period. When aerosol is not uniformly distributed in the atmosphere then the predicted AOD can be highly deviated from the measured values. Therefore these deviated data can be removed by comparing between the predicted AOD values and the actual AERONET data which help to investigate whether the non uniform source of the aerosol is from the ground surface or from higher altitude level. This model can accurately predict AOD if only the aerosol is uniformly distributed in the atmosphere. However, further study is needed to determine this model is suitable to use for AOD predicting not only in Penang, but also other state in Malaysia or even global.

  14. [Clinical research XX. From clinical judgment to multiple logistic regression model].

    PubMed

    Berea-Baltierra, Ricardo; Rivas-Ruiz, Rodolfo; Pérez-Rodríguez, Marcela; Palacios-Cruz, Lino; Moreno, Jorge; Talavera, Juan O

    2014-01-01

    The complexity of the causality phenomenon in clinical practice implies that the result of a maneuver is not solely caused by the maneuver, but by the interaction among the maneuver and other baseline factors or variables occurring during the maneuver. This requires methodological designs that allow the evaluation of these variables. When the outcome is a binary variable, we use the multiple logistic regression model (MLRM). This multivariate model is useful when we want to predict or explain, adjusting due to the effect of several risk factors, the effect of a maneuver or exposition over the outcome. In order to perform an MLRM, the outcome or dependent variable must be a binary variable and both categories must mutually exclude each other (i.e. live/death, healthy/ill); on the other hand, independent variables or risk factors may be either qualitative or quantitative. The effect measure obtained from this model is the odds ratio (OR) with 95 % confidence intervals (CI), from which we can estimate the proportion of the outcome's variability explained through the risk factors. For these reasons, the MLRM is used in clinical research, since one of the main objectives in clinical practice comprises the ability to predict or explain an event where different risk or prognostic factors are taken into account.

  15. Multiple regression analysis in modeling of columnar ozone in Peninsular Malaysia.

    PubMed

    Tan, K C; Lim, H S; Mat Jafri, M Z

    2014-06-01

    This study aimed to predict monthly columnar ozone (O3) in Peninsular Malaysia by using data on the concentration of environmental pollutants. Data (2003-2008) on five atmospheric pollutant gases (CO2, O3, CH4, NO2, and H2O vapor) retrieved from the satellite Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) were employed to develop a model that predicts columnar ozone through multiple linear regression. In the entire period, the pollutants were highly correlated (R = 0.811 for the southwest monsoon, R = 0.803 for the northeast monsoon) with predicted columnar ozone. The results of the validation of columnar ozone with column ozone from SCIAMACHY showed a high correlation coefficient (R = 0.752-0.802), indicating the model's accuracy and efficiency. Statistical analysis was utilized to determine the effects of each atmospheric pollutant on columnar ozone. A model that can retrieve columnar ozone in Peninsular Malaysia was developed to provide air quality information. These results are encouraging and accurate and can be used in early warning of the population to comply with air quality standards.

  16. Current misuses of multiple regression for investigating bivariate hypotheses: an example from the organizational domain.

    PubMed

    O'Neill, Thomas A; McLarnon, Matthew J W; Schneider, Travis J; Gardner, Robert C

    2014-09-01

    By definition, multiple regression (MR) considers more than one predictor variable, and each variable's beta will depend on both its correlation with the criterion and its correlation with the other predictor(s). Despite ad nauseam coverage of this characteristic in organizational psychology and statistical texts, researchers' applications of MR in bivariate hypothesis testing has been the subject of recent and renewed interest. Accordingly, we conducted a targeted survey of the literature by coding articles, covering a five-year span from two top-tier organizational journals, that employed MR for testing bivariate relations. The results suggest that MR coefficients, rather than correlation coefficients, were most common for testing hypotheses of bivariate relations, yet supporting theoretical rationales were rarely offered. Regarding the potential impact on scientific advancement, in almost half of the articles reviewed (44 %), at least one conclusion of each study (i.e., that the hypothesis was or was not supported) would have been different, depending on the author's use of correlation or beta to test the bivariate hypothesis. It follows that inappropriate decisions to interpret the correlation versus the beta will affect the accumulation of consistent and replicable scientific evidence. We conclude with recommendations for improving bivariate hypothesis testing.

  17. A simplified calculation procedure for mass isotopomer distribution analysis (MIDA) based on multiple linear regression.

    PubMed

    Fernández-Fernández, Mario; Rodríguez-González, Pablo; García Alonso, J Ignacio

    2016-10-01

    We have developed a novel, rapid and easy calculation procedure for Mass Isotopomer Distribution Analysis based on multiple linear regression which allows the simultaneous calculation of the precursor pool enrichment and the fraction of newly synthesized labelled proteins (fractional synthesis) using linear algebra. To test this approach, we used the peptide RGGGLK as a model tryptic peptide containing three subunits of glycine. We selected glycine labelled in two (13) C atoms ((13) C2 -glycine) as labelled amino acid to demonstrate that spectral overlap is not a problem in the proposed methodology. The developed methodology was tested first in vitro by changing the precursor pool enrichment from 10 to 40% of (13) C2 -glycine. Secondly, a simulated in vivo synthesis of proteins was designed by combining the natural abundance RGGGLK peptide and 10 or 20% (13) C2 -glycine at 1 : 1, 1 : 3 and 3 : 1 ratios. Precursor pool enrichments and fractional synthesis values were calculated with satisfactory precision and accuracy using a simple spreadsheet. This novel approach can provide a relatively rapid and easy means to measure protein turnover based on stable isotope tracers. Copyright © 2016 John Wiley & Sons, Ltd.

  18. A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression.

    PubMed

    Nicolaou, Nicoletta; Constandinou, Timothy G

    2016-01-01

    Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, C NPMR , Autoregressive modeling is replaced by Nonparametric Multiplicative Regression (NPMR). NPMR quantifies interactions between a response variable (effect) and a set of predictor variables (cause); here, we modified NPMR for model prediction. We also demonstrate how a particular measure, the sensitivity Q, could be used to reveal the structure of the underlying causal relationships. We apply C NPMR on artificial data with known ground truth (5 datasets), as well as physiological data (2 datasets). C NPMR correctly identifies both linear and nonlinear causal connections that are present in the artificial data, as well as physiologically relevant connectivity in the real data, and does not seem to be affected by filtering. The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. C NPMR is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations without any modifications to the main method. Its nonpametric nature, its ability to capture nonlinear relationships and its robustness to filtering make it appealing for a number of applications.

  19. Sagittal and Vertical Craniofacial Growth Pattern and Timing of Circumpubertal Skeletal Maturation: A Multiple Regression Study

    PubMed Central

    Rosso, Luigi; Riatti, Riccardo

    2016-01-01

    The knowledge of the associations between the timing of skeletal maturation and craniofacial growth is of primary importance when planning a functional treatment for most of the skeletal malocclusions. This cross-sectional study was thus aimed at evaluating whether sagittal and vertical craniofacial growth has an association with the timing of circumpubertal skeletal maturation. A total of 320 subjects (160 females and 160 males) were included in the study (mean age, 12.3 ± 1.7 years; range, 7.6–16.7 years). These subjects were equally distributed in the circumpubertal cervical vertebral maturation (CVM) stages 2 to 5. Each CVM stage group also had equal number of females and males. Multiple regression models were run for each CVM stage group to assess the significance of the association of cephalometric parameters (ANB, SN/MP, and NSBa angles) with age of attainment of the corresponding CVM stage (in months). Significant associations were seen only for stage 3, where the SN/MP angle was negatively associated with age (β coefficient, −0.7). These results show that hyperdivergent and hypodivergent subjects may have an anticipated and delayed attainment of the pubertal CVM stage 3, respectively. However, such association remains of little entity and it would become clinically relevant only in extreme cases. PMID:27995136

  20. Multiple Additive Regression Trees a Methodology for Predictive Data Mining for Fraud Detection

    DTIC Science & Technology

    2002-09-01

    5000 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) Operation Mongoose / DFAS 400 Gigling...The Defense Finance Accounting Service DFAS-Operation Mongoose (Internal Review - Seaside) is using new and innovative techniques for fraud detection...v ABSTRACT The Defense Finance Accounting Service DFAS-Operation Mongoose (Internal Review - Seaside) is using new

  1. Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings.

    PubMed

    Choi, Jae-Seok; Kim, Munchurl

    2017-03-01

    Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between Peak-Signal-to-Noise Ratio (PSNR) performances and computational complexity. However, since SI only utilizes simple linear mappings, it may fail to precisely reconstruct HR patches with complex texture. In this paper, we present a novel SR method, which inherits the large-to-small patch conversion scheme from SI but uses global regression based on local linear mappings (GLM). Thus, our new SR method is called GLM-SI. In GLM-SI, each LR input patch is divided into 25 overlapped subpatches. Next, based on the local properties of these subpatches, 25 different local linear mappings are applied to the current LR input patch to generate 25 HR patch candidates, which are then regressed into one final HR patch using a global regressor. The local linear mappings are learned cluster-wise in our off-line training phase. The main contribution of this paper is as follows: Previously, linear-mapping-based conventional SR methods, including SI only used one simple yet coarse linear mapping to each patch to reconstruct its HR version. On the contrary, for each LR input patch, our GLM-SI is the first to apply a combination of multiple local linear mappings, where each local linear mapping is found according to local properties of the current LR patch. Therefore, it can better approximate nonlinear LR-to-HR mappings for HR patches with complex texture. Experiment results show that the proposed GLM-SI method outperforms most of the state-of-the-art methods, and shows comparable PSNR performance with much lower

  2. Energy production through organic fraction of municipal solid waste-A multiple regression modeling approach.

    PubMed

    Ramesh, N; Ramesh, S; Vennila, G; Abdul Bari, J; MageshKumar, P

    2016-12-01

    In the 21st century, people migrated from rural to urban areas for several reasons. As a result, the populations of Indian cities are increasing day by day. On one hand, the country is developing in the field of science and technology and on the other hand, it is encountering a serious problem called 'Environmental degradation'. Due to increase in population, the generation of solid waste is also increased and is being disposed in open dumps and landfills which lead to air and land pollution. This study is attempted to generate energy out of organic solid waste by the bio- fermentation process. The study was conducted for a period of 7 months at Erode, Tamilnadu and the reading on various parameters like Hydraulic retention time, organic loading rate, sludge loading rate, influent pH, effluent pH, inlet volatile acids, out let volatile fatty acids, inlet VSS/TS ratio, outlet VSS/TS ratio, influent COD, effluent COD and % of COD removal are recorded for every 10 days. The aim of the present study is to develop a model through multiple linear regression analysis with COD as dependent variable and various parameters like HRT, OLR, SLR, influent, effluent, VSS/TS ratio, influent COD, effluent COD, etc as independent variables and to analyze the impact of these parameters on COD. The results of the model developed through step-wise regression method revealed that only four parameters Influent COD, effluent COD, VSS/TS and Influent/pH were main influencers of COD removal. The parameters influent COD and VSS/TS have positive impact on COD removal and the parameters effluent COD and Influent/pH have negative impact. The parameter Influent COD has the highest order of impact, followed by effluent COD, VSS/TS and influent pH. The other parameters HRT, OLR, SLR, INLET VFA and OUTLET VFA were not significantly contributing to the removal of COD. The implementation of the process suggested through this study might bring in dual benefit to the community, viz treatment of solid

  3. A Multiple Linear Regression Model For Estimation of Flood Peaks In Baden-wuerttemberg/germany

    NASA Astrophysics Data System (ADS)

    Casper, M.; Krieger, S.; Ihringer, J.

    In water resources planning good estimations of flood peaks are necessary for con- struction planning, for the estimation of the existing risk potential and for the valida- tion of rainfall-runoff models. Generally these indexes are only available through statistical analysis for gauged sites. Furthermore the reliability of the underlying time series can often not be proven be- cause they are too short or of bad quality. Therefore a spatial adjustment of all gauge indexes was conducted before a linear multiple regression model was applied. It now enable us to estimate flood peaks for almost any ungauged site of the study area. The model bases on 8 parameters describing the catchment properties. 7 parameters can be derived directly from digital data including a digital elevation model (catch- ment size, maximum flowlength, center flowlength, weighted slope, annual rainfall, portion of urban resp. forested area). The last parameter is an empirical landscape fac- tor, which allows to consider the regional differences in flood generation. The spatial distribution of this factor has been linked in a first approach to the hydro-geological map of Baden-Wuerttemberg. The overall performance of the model is very good. But for some areas, the determination of the landscape factor is difficult. Further investigations indicated that a more process based approach allows to im- prove the fit of this landscape factor and also the quality of the regionalisation model. By integrating detailed soil information (which is available area wide) some hydro- geological classes could be subdivided in subclasses. By replacing the parameter "weighted slope" by a parameter which better describes the driving forces of flood generation, the model performance could be improved significantly.

  4. The utility of regression-based norms in interpreting the minimal assessment of cognitive function in multiple sclerosis (MACFIMS).

    PubMed

    Parmenter, Brett A; Testa, S Marc; Schretlen, David J; Weinstock-Guttman, Bianca; Benedict, Ralph H B

    2010-01-01

    The Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) is a consensus neuropsychological battery with established reliability and validity. One of the difficulties in implementing the MACFIMS in clinical settings is the reliance on manualized norms from disparate sources. In this study, we derived regression-based norms for the MACFIMS, using a unique data set to control for standard demographic variables (i.e., age, age2, sex, education). Multiple sclerosis (MS) patients (n = 395) and healthy volunteers (n = 100) did not differ in age, level of education, sex, or race. Multiple regression analyses were conducted on the performance of the healthy adults, and the resulting models were used to predict MS performance on the MACFIMS battery. This regression-based approach identified higher rates of impairment than manualized norms for many of the MACFIMS measures. These findings suggest that there are advantages to developing new norms from a single sample using the regression-based approach. We conclude that the regression-based norms presented here provide a valid alternative to identifying cognitive impairment as measured by the MACFIMS.

  5. Analyzing Regression-Discontinuity Designs with Multiple Assignment Variables: A Comparative Study of Four Estimation Methods

    ERIC Educational Resources Information Center

    Wong, Vivian C.; Steiner, Peter M.; Cook, Thomas D.

    2013-01-01

    In a traditional regression-discontinuity design (RDD), units are assigned to treatment on the basis of a cutoff score and a continuous assignment variable. The treatment effect is measured at a single cutoff location along the assignment variable. This article introduces the multivariate regression-discontinuity design (MRDD), where multiple…

  6. The Use of Multiple Regression Models to Determine if Conjoint Analysis Should Be Conducted on Aggregate Data.

    ERIC Educational Resources Information Center

    Fraas, John W.; Newman, Isadore

    1996-01-01

    In a conjoint-analysis consumer-preference study, researchers must determine whether the product factor estimates, which measure consumer preferences, should be calculated and interpreted for each respondent or collectively. Multiple regression models can determine whether to aggregate data by examining factor-respondent interaction effects. This…

  7. The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

    ERIC Educational Resources Information Center

    Le, Huy; Marcus, Justin

    2012-01-01

    This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…

  8. A Study of the Use of Multiple Regression with Dummy Variables to Identify Instructor Contribution to Student Achievement.

    ERIC Educational Resources Information Center

    Greenberg, Barry; Mejias, Ricardo

    This study utilized linear multiple regression analysis with dummy variables to isolate one component of an educational accountability system, the contribution of the individual teacher to student achievement. Independent variables consisted of measures of each student's past academic record, the size of the class and a dummy variable to represent…

  9. Regression-based norms improve the sensitivity of the National MS Society Consensus Neuropsychological Battery for Pediatric Multiple Sclerosis (NBPMS).

    PubMed

    Smerbeck, A M; Parrish, J; Yeh, E A; Weinstock-Guttman, B; Hoogs, M; Serafin, D; Krupp, L; Benedict, R H B

    2012-01-01

    The National Multiple Sclerosis Society Consensus Neuropsychological Battery for Pediatric Multiple Sclerosis (NBPMS) was designed to detect cognitive impairment in children and adolescents with multiple sclerosis. One weakness of the battery is the reliance on published manual-based normative samples varying in size and quality. These primary sources base interpretation on discrete age bands, a practice which may be particularly problematic during periods of rapid development in childhood and adolescence. A further impediment to valid NBPMS interpretation is the lack of control for demographic factors other than age. We endeavored to develop regression-based norms for the NBPMS by gathering a demographically balanced sample of 102 healthy control children and using their performance to derive normalization, controlling for multiple demographic variables (i.e., age, age(2), gender, parent education). The regression-based normative equations were applied to the performance of 51 children with MS. For many of the major test scores, the regression-based norms more readily detected impairment. As in the case of adult MS, these results indicate that regression-based norms offer interpretive benefits over their manual-based counterparts.

  10. Physical and Cognitive-Affective Factors Associated with Fatigue in Individuals with Fibromyalgia: A Multiple Regression Analysis

    ERIC Educational Resources Information Center

    Muller, Veronica; Brooks, Jessica; Tu, Wei-Mo; Moser, Erin; Lo, Chu-Ling; Chan, Fong

    2015-01-01

    Purpose: The main objective of this study was to determine the extent to which physical and cognitive-affective factors are associated with fibromyalgia (FM) fatigue. Method: A quantitative descriptive design using correlation techniques and multiple regression analysis. The participants consisted of 302 members of the National Fibromyalgia &…

  11. Relationship of push-ups and sit-ups tests to selected anthropometric variables and performance results: a multiple regression study.

    PubMed

    Esco, Michael R; Olson, Michele S; Williford, Henry

    2008-11-01

    The purpose of this study was to explore whether selected anthropometric measures such as specific skinfold sites, along with weight, height, body mass index (BMI), waist and hip circumferences, and waist/hip ratio (WHR) were associated with sit-ups (SU) and push-ups (PU) performance, and to build a regression model for SU and PU tests. One hundred apparently healthy adults (40 men and 60 women) served as the subjects for test validation. The subjects performed 60-second SU and PU tests. The variables analyzed via multiple regression included weight, height, BMI, hip and waist circumferences, WHR, skinfolds at the abdomen (SFAB), thigh (SFTH), and subscapularis (SFSS), and sex. An additional cohort of 40 subjects (17 men and 23 women) was used to cross-validate the regression models. Validity was confirmed by correlation and paired t-tests. The regression analysis yielded a four-variable (PU, height, SFAB, and SFTH) multiple regression equation for estimating SU (R2 = 0.64, SEE = 7.5 repetitions). For PU, only SU was loaded into the regression equation (R2 = 0.43, SEE = 9.4 repetitions). Thus, the variables in the regression models accounted for 64% and 43% of the variation in SU and PU, respectively. The cross-validation sample elicited a high correlation for SU (r = 0.87) and PU (r = 0.79) scores. Moreover, paired-samples t-tests revealed that there were no significant differences between actual and predicted SU and PU scores. Therefore, this study shows that there are a number of selected, health-related anthropometric variables that account significantly for, and are predictive of, SU and PU tests.

  12. Seasonal Variability of Aragonite Saturation State in the North Pacific Ocean Predicted by Multiple Linear Regression

    NASA Astrophysics Data System (ADS)

    Kim, T. W.; Park, G. H.

    2014-12-01

    Seasonal variation of aragonite saturation state (Ωarag) in the North Pacific Ocean (NPO) was investigated, using multiple linear regression (MLR) models produced from the PACIFICA (Pacific Ocean interior carbon) dataset. Data within depth ranges of 50-1200m were used to derive MLR models, and three parameters (potential temperature, nitrate, and apparent oxygen utilization (AOU)) were chosen as predictor variables because these parameters are associated with vertical mixing, DIC (dissolved inorganic carbon) removal and release which all affect Ωarag in water column directly or indirectly. The PACIFICA dataset was divided into 5° × 5° grids, and a MLR model was produced in each grid, giving total 145 independent MLR models over the NPO. Mean RMSE (root mean square error) and r2 (coefficient of determination) of all derived MLR models were approximately 0.09 and 0.96, respectively. Then the obtained MLR coefficients for each of predictor variables and an intercept were interpolated over the study area, thereby making possible to allocate MLR coefficients to data-sparse ocean regions. Predictability from the interpolated coefficients was evaluated using Hawaiian time-series data, and as a result mean residual between measured and predicted Ωarag values was approximately 0.08, which is less than the mean RMSE of our MLR models. The interpolated MLR coefficients were combined with seasonal climatology of World Ocean Atlas 2013 (1° × 1°) to produce seasonal Ωarag distributions over various depths. Large seasonal variability in Ωarag was manifested in the mid-latitude Western NPO (24-40°N, 130-180°E) and low-latitude Eastern NPO (0-12°N, 115-150°W). In the Western NPO, seasonal fluctuations of water column stratification appeared to be responsible for the seasonal variation in Ωarag (~ 0.5 at 50 m) because it closely followed temperature variations in a layer of 0-75 m. In contrast, remineralization of organic matter was the main cause for the seasonal

  13. Multiple linear regression to estimate time-frequency electrophysiological responses in single trials.

    PubMed

    Hu, L; Zhang, Z G; Mouraux, A; Iannetti, G D

    2015-05-01

    Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical

  14. Integrative analysis of multiple diverse omics datasets by sparse group multitask regression

    PubMed Central

    Lin, Dongdong; Zhang, Jigang; Li, Jingyao; He, Hao; Deng, Hong-Wen; Wang, Yu-Ping

    2014-01-01

    A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms, and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: (1) treat the biomarker identification in each single study as a task and then combine them by multitask learning; (2) group variables from all studies for identifying significant genes; (3) enforce sparse constraint on groups of variables to overcome the “small sample, but large variables” problem. We introduce two sparse group penalties: sparse group lasso and sparse group ridge in our multitask model, and provide an effective algorithm for each model. In addition, we propose a significance test for the identification of potential risk genes. Two simulation studies are performed to evaluate the performance of our integrative method by comparing it with conventional meta-analysis method. The results show that our sparse group multitask method outperforms meta-analysis method significantly. In an application to our osteoporosis studies, 7 genes are identified as significant genes by our method and are found to have significant effects in other three independent studies for validation. The most significant gene SOD2 has been identified in our previous osteoporosis study involving the same expression dataset. Several other genes such as TREML2, HTR1E, and GLO1 are shown to be novel susceptible genes for osteoporosis, as confirmed from other

  15. Development of a modified factor analysis/multiple regression model to apportion suspended particulate matter in a complex urban airshed

    NASA Astrophysics Data System (ADS)

    Morandi, Maria T.; Daisey, Joan M.; Lioy, Paul J.

    A modified factor analysis/multiple regression (FA/MR) receptor-oriented source apportionment model has been developed which permits application of FA/MR statistical methods when some of the tracers are not unique to an individual source type. The new method uses factor and regression analyses to apportion non-unique tracer ambient concentrations in situations where there are unique tracers for all sources contributing to the non-unique tracer except one, and ascribes the residual concentration to that source. This value is then used as the source tracer in the final FA/MR apportionment model for ambient paniculate matter. In addition, factor analyses results are complemented with examination of regression residuals in order to optimize the number of identifiable sources. The new method has been applied to identify and apportion the sources of inhalable particulate matter (IPM; D5015 μm), Pb and Fe at a site in Newark, NJ. The model indicated that sulfate/secondary aerosol contributed an average of 25.8 μ -3 (48%) to IPM concentrations, followed by soil resuspension (8.2 μ -3 or 15%), paint spraying/paint pigment (6.7/gmm -3or 13%), fuel oil burning/space heating (4.3 μ -3 or 8 %), industrial emissions (3.6 μm -3 or 7 %) and motor vehicle exhaust (2.7 μ -3 or 15 %). Contributions to ambient Pb concentrations were: motor vehicle exhaust (0.16μm -3or 36%), soil resuspension (0.10μm -3 or 24%), fuel oil burning/space heating (0.08μm -3or 18%), industrial emissions (0.07 μ -3 or 17 %), paint spraying/paint pigment (0.036 μm -3or 9 %) and zinc related sources (0.022 μ -3 or 5 %). Contributions to ambient Fe concentrations were: soil resuspension (0.43μ -3or 51%), paint spraying/paint pigment (0.28 μm -3or 33 %) and industrial emissions (0.15 μ -3or 18 %). The models were validated by comparing partial source profiles calculated from modeling results with the corresponding published source emissions composition.

  16. Modeling lactation curves and estimation of genetic parameters in Holstein cows using multiple-trait random regression models.

    PubMed

    Kheirabadi, Khabat; Rashidi, Amir; Alijani, Sadegh; Imumorin, Ikhide

    2014-11-01

    We compared the goodness of fit of three mathematical functions (including: Legendre polynomials, Lidauer-Mäntysaari function and Wilmink function) for describing the lactation curve of primiparous Iranian Holstein cows by using multiple-trait random regression models (MT-RRM). Lactational submodels provided the largest daily additive genetic (AG) and permanent environmental (PE) variance estimates at the end and at the onset of lactation, respectively, as well as low genetic correlations between peripheral test-day records. For all models, heritability estimates were highest at the end of lactation (245 to 305 days) and ranged from 0.05 to 0.26, 0.03 to 0.12 and 0.04 to 0.24 for milk, fat and protein yields, respectively. Generally, the genetic correlations between traits depend on how far apart they are or whether they are on the same day in any two traits. On average, genetic correlations between milk and fat were the lowest and those between fat and protein were intermediate, while those between milk and protein were the highest. Results from all criteria (Akaike's and Schwarz's Bayesian information criterion, and -2*logarithm of the likelihood function) suggested that a model with 2 and 5 coefficients of Legendre polynomials for AG and PE effects, respectively, was the most adequate for fitting the data.

  17. Multiple regression modelling of mineral base oil biodegradability based on their physical properties and overall chemical composition.

    PubMed

    Haus, Frédérique; Boissel, Olivier; Junter, Guy Alain

    2003-02-01

    A set of 38 mineral base oils was characterized by a number of chemical (i.e., overall chemical composition) and physical parameters used routinely in industry. Their primary biodegradability was evaluated using the CEC L-33-A-93 test. Multiple (stepwise) linear regression (MLR) analyses were performed to describe the relationships between the biodegradability values and the chemical or physical properties of oils. Chemical, physical, and both types of parameters were successively used as independent variables. Using chemical descriptors as variables, a four-variable model equation was obtained that explained only 68.2% (adjusted R-squared statistic=68.2%) of the variability in biodegradability. The fitting was improved by using either the physical or the whole parameters as variables. MLR analyses led to three-descriptor model equations involving kinematic viscosity (as log), Noack volatility (as log) and either the viscosity index (pure physical model) or the paraffinic carbon percentage (mixed chemical-physical model). These two models displayed very similar adjusted R-squared statistics, of approximately 91%. Their predicting ability was verified using 25 additional base oils or oil blends. For 80% of oils on a total of 63, the absolute percentage error on biodegradability predicted by either model was lower than 20%. Kinematic viscosity was by far the most influential parameter in the two models.

  18. An additional monogenic disorder that masquerades as multiple sclerosis

    SciTech Connect

    Vahedi, K.; Tournier-Lasserve, E.; Vahedi, K.

    1996-11-11

    In their comprehensive differential diagnosis of monogenic diseases that can mimic multiple sclerosis, Natowicz and Bejjani did not include a newly recognized monogenic disorder known under the acronym of CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy); this disorder can mimic MS clinically and radiologically to a remarkable extent. The underlying histopathological lesion of CADASIL is a non-atherosclerotic, non-amyloid arteriopathy affecting mainly the penetrating medullary arteries to the subcortical white matter and basal ganglia. Electron microscopy shows an abnormal deposit of granular osmiophilic material in the arterial wall. These arterial changes are observed in various tissues even though clinical manifestations seem to be restricted to the central nervous system. The CADASIL gene was mapped recently to chromosome 19 and gene identification is ongoing. 6 refs., 1 fig.

  19. Additive effects prevail: The response of biota to multiple stressors in an intensively monitored watershed.

    PubMed

    Gieswein, Alexander; Hering, Daniel; Feld, Christian K

    2017-03-21

    Freshwater ecosystems are impacted by a range of stressors arising from diverse human-caused land and water uses. Identifying the relative importance of single stressors and understanding how multiple stressors interact and jointly affect biology is crucial for River Basin Management. This study addressed multiple human-induced stressors and their effects on the aquatic flora and fauna based on data from standard WFD monitoring schemes. For altogether 1095 sites within a mountainous catchment, we used 12 stressor variables covering three different stressor groups: riparian land use, physical habitat quality and nutrient enrichment. Twenty-one biological metrics calculated from taxa lists of three organism groups (fish, benthic invertebrates and aquatic macrophytes) served as response variables. Stressor and response variables were subjected to Boosted Regression Tree (BRT) analysis to identify stressor hierarchy and stressor interactions and subsequently to Generalised Linear Regression Modelling (GLM) to quantify the stressors standardised effect size. Our results show that riverine habitat degradation was the dominant stressor group for the river fauna, notably the bed physical habitat structure. Overall, the explained variation in benthic invertebrate metrics was higher than it was in fish and macrophyte metrics. In particular, general integrative (aggregate) metrics such as % Ephemeroptera, Plecoptera and Trichoptera (EPT) taxa performed better than ecological traits (e.g. % feeding types). Overall, additive stressor effects dominated, while significant and meaningful stressor interactions were generally rare and weak. We concluded that given the type of stressor and ecological response variables addressed in this study, river basin managers do not need to bother much about complex stressor interactions, but can focus on the prevailing stressors according to the hierarchy identified.

  20. Estimation of early postmortem intervals by a multiple regression analysis using rectal temperature and non-temperature based postmortem changes.

    PubMed

    Honjyo, Kohji; Yonemitsu, Kosei; Tsunenari, Shigeyuki

    2005-10-01

    Five general methods based on rectal temperature and a multiple regression analysis using rectal temperature and non-temperature based postmortem changes were applied to 212 postmortem cases of within 24h postmortem (PM) intervals. Non-temperature based postmortem changes of rigidity, hypostasis and corneal turbidity were numerically categorized and used with rectal temperatures as four statistical variables in the multiple regression analysis. The correlation coefficient values between true and calculated postmortem intervals were 0.78-0.82 in the five general methods based on rectal temperature. The multiple regression analysis produced a multiple correlation coefficient value of 0.89 and according to the error ranges of the PM intervals, 72% of the cases were estimated within the error of +/-1.0 h and 92% within +/-5.0 h. Although assessments of non-temperature based PM changes are mostly subjective and have a wide variation, the present study demonstrated a usefulness of non-temperature based PM changes in the estimation of PM intervals.

  1. Spontaneous Regression of Hepatocellular Carcinoma with Multiple Lung Metastases: A Case Report and Review of the Literature.

    PubMed

    Pectasides, Eirini; Miksad, Rebecca; Pyatibrat, Sergey; Srivastava, Amogh; Bullock, Andrea

    2016-09-01

    Spontaneous regression of hepatocellular carcinoma (HCC) is a rare event. Here we present a case of spontaneous regression of metastatic HCC. A 53-year-old man with hepatitis C and alcoholic cirrhosis was found to have a large liver mass consistent with HCC based on its radiographic features. Imaging also revealed left portal and hepatic vein thrombosis, as well as multiple lung nodules concerning for metastases. Approximately 2 months after the initial diagnosis, both the primary liver lesion and the lung metastases decreased in size and eventually resolved without any intervention. Thereafter, the left hepatic vein thrombus progressed into the inferior vena cava and the right atrium, and the patient died due to right heart failure. In this case report and literature review, we discuss the potential mechanisms for and review the literature on spontaneous regression of metastatic HCC.

  2. Analyzing Regression-Discontinuity Designs with Multiple Assignment Variables: A Comparative Study of Four Estimation Methods

    ERIC Educational Resources Information Center

    Wong, Vivian C.; Steiner, Peter M.; Cook, Thomas D.

    2012-01-01

    In a traditional regression-discontinuity design (RDD), units are assigned to treatment and comparison conditions solely on the basis of a single cutoff score on a continuous assignment variable. The discontinuity in the functional form of the outcome at the cutoff represents the treatment effect, or the average treatment effect at the cutoff.…

  3. Multiple regression analysis in modelling of carbon dioxide emissions by energy consumption use in Malaysia

    NASA Astrophysics Data System (ADS)

    Keat, Sim Chong; Chun, Beh Boon; San, Lim Hwee; Jafri, Mohd Zubir Mat

    2015-04-01

    Climate change due to carbon dioxide (CO2) emissions is one of the most complex challenges threatening our planet. This issue considered as a great and international concern that primary attributed from different fossil fuels. In this paper, regression model is used for analyzing the causal relationship among CO2 emissions based on the energy consumption in Malaysia using time series data for the period of 1980-2010. The equations were developed using regression model based on the eight major sources that contribute to the CO2 emissions such as non energy, Liquefied Petroleum Gas (LPG), diesel, kerosene, refinery gas, Aviation Turbine Fuel (ATF) and Aviation Gasoline (AV Gas), fuel oil and motor petrol. The related data partly used for predict the regression model (1980-2000) and partly used for validate the regression model (2001-2010). The results of the prediction model with the measured data showed a high correlation coefficient (R2=0.9544), indicating the model's accuracy and efficiency. These results are accurate and can be used in early warning of the population to comply with air quality standards.

  4. Multiple Logistic Regression Analysis of Cigarette Use among High School Students

    ERIC Educational Resources Information Center

    Adwere-Boamah, Joseph

    2011-01-01

    A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…

  5. Multiple regression methods show great potential for rare variant association tests.

    PubMed

    Xu, ChangJiang; Ladouceur, Martin; Dastani, Zari; Richards, J Brent; Ciampi, Antonio; Greenwood, Celia M T

    2012-01-01

    The investigation of associations between rare genetic variants and diseases or phenotypes has two goals. Firstly, the identification of which genes or genomic regions are associated, and secondly, discrimination of associated variants from background noise within each region. Over the last few years, many new methods have been developed which associate genomic regions with phenotypes. However, classical methods for high-dimensional data have received little attention. Here we investigate whether several classical statistical methods for high-dimensional data: ridge regression (RR), principal components regression (PCR), partial least squares regression (PLS), a sparse version of PLS (SPLS), and the LASSO are able to detect associations with rare genetic variants. These approaches have been extensively used in statistics to identify the true associations in data sets containing many predictor variables. Using genetic variants identified in three genes that were Sanger sequenced in 1998 individuals, we simulated continuous phenotypes under several different models, and we show that these feature selection and feature extraction methods can substantially outperform several popular methods for rare variant analysis. Furthermore, these approaches can identify which variants are contributing most to the model fit, and therefore both goals of rare variant analysis can be achieved simultaneously with the use of regression regularization methods. These methods are briefly illustrated with an analysis of adiponectin levels and variants in the ADIPOQ gene.

  6. The Generalized Regression Discontinuity Design: Using Multiple Assignment Variables and Cutoffs to Estimate Treatment Effects

    ERIC Educational Resources Information Center

    Wong, Vivian C.; Steiner, Peter M.; Cook, Thomas D.

    2009-01-01

    This paper introduces a generalization of the regression-discontinuity design (RDD). Traditionally, RDD is considered in a two-dimensional framework, with a single assignment variable and cutoff. Treatment effects are measured at a single location along the assignment variable. However, this represents a specialized (and straight-forward)…

  7. Hierarchical Multiple Regression in Counseling Research: Common Problems and Possible Remedies.

    ERIC Educational Resources Information Center

    Petrocelli, John V.

    2003-01-01

    A brief content analysis was conducted on the use of hierarchical regression in counseling research published in the "Journal of Counseling Psychology" and the "Journal of Counseling & Development" during the years 1997-2001. Common problems are cited and possible remedies are described. (Contains 43 references and 3 tables.) (Author)

  8. Prediction of the Rock Mass Diggability Index by Using Fuzzy Clustering-Based, ANN and Multiple Regression Methods

    NASA Astrophysics Data System (ADS)

    Saeidi, Omid; Torabi, Seyed Rahman; Ataei, Mohammad

    2014-03-01

    Rock mass classification systems are one of the most common ways of determining rock mass excavatability and related equipment assessment. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This causes particular ambiguities, leading to the misuse of such classifications in practical applications. Recently, intelligence system approaches such as artificial neural networks (ANNs) and neuro-fuzzy methods, along with multiple regression models, have been used successfully to overcome such uncertainties. The purpose of the present study is the construction of several models by using an adaptive neuro-fuzzy inference system (ANFIS) method with two data clustering approaches, including fuzzy c-means (FCM) clustering and subtractive clustering, an ANN and non-linear multiple regression to estimate the basic rock mass diggability index. A set of data from several case studies was used to obtain the real rock mass diggability index and compared to the predicted values by the constructed models. In conclusion, it was observed that ANFIS based on the FCM model shows higher accuracy and correlation with actual data compared to that of the ANN and multiple regression. As a result, one can use the assimilation of ANNs with fuzzy clustering-based models to construct such rigorous predictor tools.

  9. Quantifying components of the hydrologic cycle in Virginia using chemical hydrograph separation and multiple regression analysis

    USGS Publications Warehouse

    Sanford, Ward E.; Nelms, David L.; Pope, Jason P.; Selnick, David L.

    2012-01-01

    This study by the U.S. Geological Survey, prepared in cooperation with the Virginia Department of Environmental Quality, quantifies the components of the hydrologic cycle across the Commonwealth of Virginia. Long-term, mean fluxes were calculated for precipitation, surface runoff, infiltration, total evapotranspiration (ET), riparian ET, recharge, base flow (or groundwater discharge) and net total outflow. Fluxes of these components were first estimated on a number of real-time-gaged watersheds across Virginia. Specific conductance was used to distinguish and separate surface runoff from base flow. Specific-conductance data were collected every 15 minutes at 75 real-time gages for approximately 18 months between March 2007 and August 2008. Precipitation was estimated for 1971–2000 using PRISM climate data. Precipitation and temperature from the PRISM data were used to develop a regression-based relation to estimate total ET. The proportion of watershed precipitation that becomes surface runoff was related to physiographic province and rock type in a runoff regression equation. Component flux estimates from the watersheds were transferred to flux estimates for counties and independent cities using the ET and runoff regression equations. Only 48 of the 75 watersheds yielded sufficient data, and data from these 48 were used in the final runoff regression equation. The base-flow proportion for the 48 watersheds averaged 72 percent using specific conductance, a value that was substantially higher than the 61 percent average calculated using a graphical-separation technique (the USGS program PART). Final results for the study are presented as component flux estimates for all counties and independent cities in Virginia.

  10. A methodology for the design of experiments in computational intelligence with multiple regression models.

    PubMed

    Fernandez-Lozano, Carlos; Gestal, Marcos; Munteanu, Cristian R; Dorado, Julian; Pazos, Alejandro

    2016-01-01

    The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

  11. A methodology for the design of experiments in computational intelligence with multiple regression models

    PubMed Central

    Gestal, Marcos; Munteanu, Cristian R.; Dorado, Julian; Pazos, Alejandro

    2016-01-01

    The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable. PMID:27920952

  12. Multiple regression models of δ13C and δ15N for fish populations in the eastern Gulf of Mexico

    NASA Astrophysics Data System (ADS)

    Radabaugh, Kara R.; Peebles, Ernst B.

    2014-08-01

    Multiple regression models were created to explain spatial and temporal variation in the δ13C and δ15N values of fish populations on the West Florida Shelf (eastern Gulf of Mexico, USA). Extensive trawl surveys from three time periods were used to acquire muscle samples from seven groundfish species. Isotopic variation (δ13Cvar and δ15Nvar) was calculated as the deviation from the isotopic mean of each fish species. Static spatial data and dynamic water quality parameters were used to create models predicting δ13Cvar and δ15Nvar in three fish species that were caught in the summers of 2009 and 2010. Additional data sets were then used to determine the accuracy of the models for predicting isotopic variation (1) in a different time period (fall 2010) and (2) among four entirely different fish species that were collected during summer 2009. The δ15Nvar model was relatively stable and could be applied to different time periods and species with similar accuracy (mean absolute errors 0.31-0.33‰). The δ13Cvar model had a lower predictive capability and mean absolute errors ranged from 0.42 to 0.48‰. δ15N trends are likely linked to gradients in nitrogen fixation and Mississippi River influence on the West Florida Shelf, while δ13C trends may be linked to changes in algal species, photosynthetic fractionation, and abundance of benthic vs. planktonic basal resources. These models of isotopic variability may be useful for future stable isotope investigations of trophic level, basal resource use, and animal migration on the West Florida Shelf.

  13. Multivariate linear regression of high-dimensional fMRI data with multiple target variables.

    PubMed

    Valente, Giancarlo; Castellanos, Agustin Lage; Vanacore, Gianluca; Formisano, Elia

    2014-05-01

    Multivariate regression is increasingly used to study the relation between fMRI spatial activation patterns and experimental stimuli or behavioral ratings. With linear models, informative brain locations are identified by mapping the model coefficients. This is a central aspect in neuroimaging, as it provides the sought-after link between the activity of neuronal populations and subject's perception, cognition or behavior. Here, we show that mapping of informative brain locations using multivariate linear regression (MLR) may lead to incorrect conclusions and interpretations. MLR algorithms for high dimensional data are designed to deal with targets (stimuli or behavioral ratings, in fMRI) separately, and the predictive map of a model integrates information deriving from both neural activity patterns and experimental design. Not accounting explicitly for the presence of other targets whose associated activity spatially overlaps with the one of interest may lead to predictive maps of troublesome interpretation. We propose a new model that can correctly identify the spatial patterns associated with a target while achieving good generalization. For each target, the training is based on an augmented dataset, which includes all remaining targets. The estimation on such datasets produces both maps and interaction coefficients, which are then used to generalize. The proposed formulation is independent of the regression algorithm employed. We validate this model on simulated fMRI data and on a publicly available dataset. Results indicate that our method achieves high spatial sensitivity and good generalization and that it helps disentangle specific neural effects from interaction with predictive maps associated with other targets.

  14. Violence against Chinese female sex workers from their stable partners: a hierarchical multiple regression analysis.

    PubMed

    Zhang, Chen; Li, Xiaoming; Su, Shaobing; Hong, Yan; Zhou, Yuejiao; Tang, Zhenzhu; Shen, Zhiyong

    2015-01-01

    Limited data are available regarding risk factors that are related to intimate partner violence (IPV) against female sex workers (FSWs) in the context of stable partnerships. Out of the 1,022 FSWs, 743 reported ever having a stable partnership and 430 (more than half) of those reported experiencing IPV. Hierarchical multivariate regression revealed that some characteristics of stable partners (e.g., low education, alcohol use) and relationship stressors (e.g., frequent friction, concurrent partnerships) were independently predictive of IPV against FSWs. Public health professionals who design future violence prevention interventions targeting FSWs need to consider the influence of their stable partners.

  15. [Inversion of the lake total nitrogen concentration by multiple regression kriging model based on hyperspectral data of HJ-1A].

    PubMed

    Pan, Bang-long; Yi, Wei-ning; Wang, Xian-hua; Qin, Hui-ping; Wang, Jia-cheng; Qiao, Yan-li

    2011-07-01

    The content of total nitrogen in the waters is an important index to measure lake water quality, and the technique of remote sensing plays a large role in quantitatively monitoring the dynamic change and timely grasping the status of lake pollution. Taking Chaohu as an example, quantitative inversion models of total nitrogen were established by multivariable regression Kriging under analyzing of an correlation between total nitrogen and chlorophyll-a or suspended solids by HIS hyperspectral remote sensing data of HJ-1A satellite. The result shows that the correlation of 0.76 was discovered between total nitrogen and the multiple combination with band 72, band 79 and band 97, while the correlation could be increased to 0.83 by applying combined model of multiple linear regression and ordinary Kriging. The optimization of the residuals of the conventional regression model can improve the accuracy of the inversion effectively. These results also provide useful exploration for further establishing a common model of quantitative inversion of lake total nitrogen concentration.

  16. 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).

  17. Full-waveform associated identification method of ATEM 3D anomalies based on multiple linear regression analysis

    NASA Astrophysics Data System (ADS)

    Ji, Yanju; Huang, Wanyu; Yu, Mingmei; Guan, Shanshan; Wang, Yuan; Zhu, Yu

    2017-01-01

    This article studies full-waveform associated identification method of airborne time-domain electromagnetic method (ATEM) 3-d anomalies based on multiple linear regression analysis method. By using convolution algorithm, full-waveform theoretical responses are computed to derive sample library including switch-off-time period responses and off-time period responses. Extract full-waveform attributes from theoretical responses to derive linear regression equations which are used to identify the geological parameters. In order to improve the precision ulteriorly, we optimize the identification method by separating the sample library into different groups and identify the parameter respectively. Performance of full-waveform associated identification method with field data of wire-loop test experiments with ATEM system in Daedao of Changchun proves that the full-waveform associated identification method is feasible practically.

  18. Detection of interactions between a dichotomous moderator and a continuous predictor in moderated multiple regression with heterogeneous error variance.

    PubMed

    Shieh, Gwowen

    2009-02-01

    Moderated multiple regression (MMR) has been widely used to investigate the interaction or moderating effects of a categorical moderator across a variety of subdisciplines in the behavioral and social sciences. In view of the frequent violation of the homogeneity of error variance assumption in MMR applications, the weighted least squares (WLS) approach has been proposed as one of the alternatives to the ordinary least squares method for the detection of the interaction effect between a dichotomous moderator and a continuous predictor. Although the existing result is informative in assuring the statistical accuracy and computational ease of the WLS-based method, no explicit algebraic formulation and underlying distributional details are available. This article aims to delineate the fundamental properties of the WLS test in connection with the well-known Welch procedure for regression slope homogeneity under error variance heterogeneity. With elaborately systematic derivation and analytic assessment, it is shown that the notion of WLS is implicitly embedded in the Welch approach. More importantly, extensive simulation study is conducted to demonstrate the conditions in which the Welch test will substantially outperform the WLS method; they may yield different conclusions. Welch's solution to the Behrens-Fisher problem is so entrenched that the use of its direct extension within the linear regression framework can arguably be recommended. In order to facilitate the application of Welch's procedure, the SAS and R computing algorithms are presented. The study contributes to the understanding of methodological variants for detecting the effect of a dichotomous moderator in the context of moderated multiple regression. Supplemental materials for this article may be downloaded from brm.psychonomic-journals.org/content/supplemental.

  19. Contextual Atlas Regression Forests: Multiple-Atlas-Based Automated Dose Prediction in Radiation Therapy.

    PubMed

    McIntosh, Chris; Purdie, Thomas G

    2016-04-01

    Radiation therapy is an integral part of cancer treatment, but to date it remains highly manual. Plans are created through optimization of dose volume objectives that specify intent to minimize, maximize, or achieve a prescribed dose level to clinical targets and organs. Optimization is NP-hard, requiring highly iterative and manual initialization procedures. We present a proof-of-concept for a method to automatically infer the radiation dose directly from the patient's treatment planning image based on a database of previous patients with corresponding clinical treatment plans. Our method uses regression forests augmented with density estimation over the most informative features to learn an automatic atlas-selection metric that is tailored to dose prediction. We validate our approach on 276 patients from 3 clinical treatment plan sites (whole breast, breast cavity, and prostate), with an overall dose prediction accuracies of 78.68%, 64.76%, 86.83% under the Gamma metric.

  20. 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

  1. Ca analysis: an Excel based program for the analysis of intracellular calcium transients including multiple, simultaneous regression analysis.

    PubMed

    Greensmith, David J

    2014-01-01

    Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow.

  2. Screening houses for vapor intrusion risks: a multiple regression analysis approach.

    PubMed

    Johnston, Jill E; Gibson, Jacqueline MacDonald

    2013-06-04

    The migration of chlorinated volatile organic compounds from groundwater to indoor air-known as vapor intrusion-can be an important exposure pathway at hazardous waste sites. Because sampling indoor air at every potentially affected home is often logistically infeasible, screening tools are needed to help identify at-risk homes. Currently, the U.S. Environmental Protection Agency (EPA) uses a simple screening approach that employs a generic vapor "attenuation factor," the ratio of the indoor air pollutant concentration to the pollutant concentration in the soil gas directly above the groundwater table. At every potentially affected home above contaminated groundwater, the EPA assumes the vapor attenuation factor is less than 1/1000--that is, that the indoor air concentration will not exceed 1/1000 times the soil-gas concentration immediately above groundwater. This paper reports on a screening-level model that improves on the EPA approach by considering environmental, contaminant, and household characteristics. The model is based on an analysis of the EPA's vapor intrusion database, which contains almost 2,400 indoor air and corresponding subsurface concentration samples collected in 15 states. We use the site data to develop a multilevel regression model for predicting the vapor attenuation factor. We find that the attenuation factor varies significantly with soil type, depth to groundwater, season, household foundation type, and contaminant molecular weight. The resulting model decreases the rate of false negatives compared to EPA's screening approach.

  3. Establishment of In Silico Prediction Models for CYP3A4 and CYP2B6 Induction in Human Hepatocytes by Multiple Regression Analysis Using Azole Compounds.

    PubMed

    Nagai, Mika; Konno, Yoshihiro; Satsukawa, Masahiro; Yamashita, Shinji; Yoshinari, Kouichi

    2016-08-01

    Drug-drug interactions (DDIs) via cytochrome P450 (P450) induction are one clinical problem leading to increased risk of adverse effects and the need for dosage adjustments and additional therapeutic monitoring. In silico models for predicting P450 induction are useful for avoiding DDI risk. In this study, we have established regression models for CYP3A4 and CYP2B6 induction in human hepatocytes using several physicochemical parameters for a set of azole compounds with different P450 induction as characteristics as model compounds. To obtain a well-correlated regression model, the compounds for CYP3A4 or CYP2B6 induction were independently selected from the tested azole compounds using principal component analysis with fold-induction data. Both of the multiple linear regression models obtained for CYP3A4 and CYP2B6 induction are represented by different sets of physicochemical parameters. The adjusted coefficients of determination for these models were of 0.8 and 0.9, respectively. The fold-induction of the validation compounds, another set of 12 azole-containing compounds, were predicted within twofold limits for both CYP3A4 and CYP2B6. The concordance for the prediction of CYP3A4 induction was 87% with another validation set, 23 marketed drugs. However, the prediction of CYP2B6 induction tended to be overestimated for these marketed drugs. The regression models show that lipophilicity mostly contributes to CYP3A4 induction, whereas not only the lipophilicity but also the molecular polarity is important for CYP2B6 induction. Our regression models, especially that for CYP3A4 induction, might provide useful methods to avoid potent CYP3A4 or CYP2B6 inducers during the lead optimization stage without performing induction assays in human hepatocytes.

  4. Multiple logistic regression model of signalling practices of drivers on urban highways

    NASA Astrophysics Data System (ADS)

    Puan, Othman Che; Ibrahim, Muttaka Na'iya; Zakaria, Rozana

    2015-05-01

    Giving signal is a way of informing other road users, especially to the conflicting drivers, the intention of a driver to change his/her movement course. Other users are exposed to hazard situation and risks of accident if the driver who changes his/her course failed to give signal as required. This paper describes the application of logistic regression model for the analysis of driver's signalling practices on multilane highways based on possible factors affecting driver's decision such as driver's gender, vehicle's type, vehicle's speed and traffic flow intensity. Data pertaining to the analysis of such factors were collected manually. More than 2000 drivers who have performed a lane changing manoeuvre while driving on two sections of multilane highways were observed. Finding from the study shows that relatively a large proportion of drivers failed to give any signals when changing lane. The result of the analysis indicates that although the proportion of the drivers who failed to provide signal prior to lane changing manoeuvre is high, the degree of compliances of the female drivers is better than the male drivers. A binary logistic model was developed to represent the probability of a driver to provide signal indication prior to lane changing manoeuvre. The model indicates that driver's gender, type of vehicle's driven, speed of vehicle and traffic volume influence the driver's decision to provide a signal indication prior to a lane changing manoeuvre on a multilane urban highway. In terms of types of vehicles driven, about 97% of motorcyclists failed to comply with the signal indication requirement. The proportion of non-compliance drivers under stable traffic flow conditions is much higher than when the flow is relatively heavy. This is consistent with the data which indicates a high degree of non-compliances when the average speed of the traffic stream is relatively high.

  5. Peer Rated Therapeutic Talent and Affective Sensitivity: A Multiple Regression Approach.

    ERIC Educational Resources Information Center

    Jackson, Eugene

    1985-01-01

    Used peer rated measures of Warmth, Understanding and Openness to predict scores on the Kagan Affective Sensitivity Scale-E80 among 66 undergraduates who had participated in interpersonal skills training groups. Results indicated that, as an additively composite index of Therapeutic Talent, they were positively correlated with affective…

  6. Combined genetic algorithm and multiple linear regression (GA-MLR) optimizer: Application to multi-exponential fluorescence decay surface.

    PubMed

    Fisz, Jacek J

    2006-12-07

    The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi

  7. Estimation of nutrients and organic matter in Korean swine slurry using multiple regression analysis of physical and chemical properties.

    PubMed

    Suresh, Arumuganainar; Choi, Hong Lim

    2011-10-01

    Swine waste land application has increased due to organic fertilization, but excess application in an arable system can cause environmental risk. Therefore, in situ characterizations of such resources are important prior to application. To explore this, 41 swine slurry samples were collected from Korea, and wide differences were observed in the physico-biochemical properties. However, significant (P<0.001) multiple property correlations (R²) were obtained between nutrients with specific gravity (SG), electrical conductivity (EC), total solids (TS) and pH. The different combinations of hydrometer, EC meter, drying oven and pH meter were found useful to estimate Mn, Fe, Ca, K, Al, Na, N and 5-day biochemical oxygen demands (BOD₅) at improved R² values of 0.83, 0.82, 0.77, 0.75, 0.67, 0.47, 0.88 and 0.70, respectively. The results from this study suggest that multiple property regressions can facilitate the prediction of micronutrients and organic matter much better than a single property regression for livestock waste.

  8. Sequential Processing and the Matching-Stimulus Interval Effect in ERP Components: An Exploration of the Mechanism Using Multiple Regression

    PubMed Central

    Steiner, Genevieve Z.; Barry, Robert J.; Gonsalvez, Craig J.

    2016-01-01

    In oddball tasks, increasing the time between stimuli within a particular condition (target-to-target interval, TTI; nontarget-to-nontarget interval, NNI) systematically enhances N1, P2, and P300 event-related potential (ERP) component amplitudes. This study examined the mechanism underpinning these effects in ERP components recorded from 28 adults who completed a conventional three-tone oddball task. Bivariate correlations, partial correlations and multiple regression explored component changes due to preceding ERP component amplitudes and intervals found within the stimulus series, rather than constraining the task with experimentally constructed intervals, which has been adequately explored in prior studies. Multiple regression showed that for targets, N1 and TTI predicted N2, TTI predicted P3a and P3b, and Processing Negativity (PN), P3b, and TTI predicted reaction time. For rare nontargets, P1 predicted N1, NNI predicted N2, and N1 predicted Slow Wave (SW). Findings show that the mechanism is operating on separate stages of stimulus-processing, suggestive of either increased activation within a number of stimulus-specific pathways, or very long component generator recovery cycles. These results demonstrate the extent to which matching-stimulus intervals influence ERP component amplitudes and behavior in a three-tone oddball task, and should be taken into account when designing similar studies. PMID:27445774

  9. A Rapid Model Adaptation Technique for Emotional Speech Recognition with Style Estimation Based on Multiple-Regression HMM

    NASA Astrophysics Data System (ADS)

    Ijima, Yusuke; Nose, Takashi; Tachibana, Makoto; Kobayashi, Takao

    In this paper, we propose a rapid model adaptation technique for emotional speech recognition which enables us to extract paralinguistic information as well as linguistic information contained in speech signals. This technique is based on style estimation and style adaptation using a multiple-regression HMM (MRHMM). In the MRHMM, the mean parameters of the output probability density function are controlled by a low-dimensional parameter vector, called a style vector, which corresponds to a set of the explanatory variables of the multiple regression. The recognition process consists of two stages. In the first stage, the style vector that represents the emotional expression category and the intensity of its expressiveness for the input speech is estimated on a sentence-by-sentence basis. Next, the acoustic models are adapted using the estimated style vector, and then standard HMM-based speech recognition is performed in the second stage. We assess the performance of the proposed technique in the recognition of simulated emotional speech uttered by both professional narrators and non-professional speakers.

  10. Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regression.

    PubMed

    Cherry, Kevin M; Peplinski, Brandon; Kim, Lauren; Wang, Shijun; Lu, Le; Zhang, Weidong; Liu, Jianfei; Wei, Zhuoshi; Summers, Ronald M

    2015-01-01

    Given the potential importance of marginal artery localization in automated registration in computed tomography colonography (CTC), we have devised a semi-automated method of marginal vessel detection employing sequential Monte Carlo tracking (also known as particle filtering tracking) by multiple cue fusion based on intensity, vesselness, organ detection, and minimum spanning tree information for poorly enhanced vessel segments. We then employed a random forest algorithm for intelligent cue fusion and decision making which achieved high sensitivity and robustness. After applying a vessel pruning procedure to the tracking results, we achieved statistically significantly improved precision compared to a baseline Hessian detection method (2.7% versus 75.2%, p<0.001). This method also showed statistically significantly improved recall rate compared to a 2-cue baseline method using fewer vessel cues (30.7% versus 67.7%, p<0.001). These results demonstrate that marginal artery localization on CTC is feasible by combining a discriminative classifier (i.e., random forest) with a sequential Monte Carlo tracking mechanism. In so doing, we present the effective application of an anatomical probability map to vessel pruning as well as a supplementary spatial coordinate system for colonic segmentation and registration when this task has been confounded by colon lumen collapse.

  11. Analysis of multiple linear regression algorithms used for respiratory mechanics monitoring during artificial ventilation.

    PubMed

    Polak, Adam G

    2011-02-01

    Many patients undergo long-term artificial ventilation and their respiratory system mechanics should be monitored to detect changes in the patient's state and to optimize ventilator settings. In this work the most popular algorithms for tracking variations of respiratory resistance (R(rs)) and elastance (E(rs)) over a ventilatory cycle were analysed in terms of systematic and random errors. Additionally, a new approach was proposed and compared to the previous ones. It takes into account an exact description of flow integration by volume-dependent lung compliance. The results of analyses showed advantages of this new approach and enabled to form several suggestions. Algorithms including R(rs) and E(rs) dependencies on airflow and lung volume can be effectively applied only at low levels of noise present in measurement data, otherwise the use of the simplest model with constant parameters is preferable. Additionally, one should avoid including the resistance dependence on airflow alone, since this considerably destroys the retrieved trace of R(rs). Finally, the estimated cyclic trajectories of R(rs) and E(rs) are more sensitive to noise present in pressure than in the flow signal, and the elastance traces are estimated more accurately than the resistance ones.

  12. Ridge Regression: A Panacea?

    ERIC Educational Resources Information Center

    Walton, Joseph M.; And Others

    1978-01-01

    Ridge regression is an approach to the problem of large standard errors of regression estimates of intercorrelated regressors. The effect of ridge regression on the estimated squared multiple correlation coefficient is discussed and illustrated. (JKS)

  13. Influence of Additive and Multiplicative Structure and Direction of Comparison on the Reversal Error

    ERIC Educational Resources Information Center

    González-Calero, José Antonio; Arnau, David; Laserna-Belenguer, Belén

    2015-01-01

    An empirical study has been carried out to evaluate the potential of word order matching and static comparison as explanatory models of reversal error. Data was collected from 214 undergraduate students who translated a set of additive and multiplicative comparisons expressed in Spanish into algebraic language. In these multiplicative comparisons…

  14. Langevin simulation of scalar fields: Additive and multiplicative noises and lattice renormalization

    NASA Astrophysics Data System (ADS)

    Cassol-Seewald, N. C.; Farias, R. L. S.; Fraga, E. S.; Krein, G.; Ramos, Rudnei O.

    2012-08-01

    We consider the Langevin lattice dynamics for a spontaneously broken λϕ4 scalar field theory where both additive and multiplicative noise terms are incorporated. The lattice renormalization for the corresponding stochastic Ginzburg-Landau-Langevin and the subtleties related to the multiplicative noise are investigated.

  15. QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression

    PubMed Central

    Zisi, Chrysostomi; Sampsonidis, Ioannis; Fasoula, Stella; Papachristos, Konstantinos; Witting, Michael; Gika, Helen G.; Nikitas, Panagiotis; Pappa-Louisi, Adriani

    2017-01-01

    Modified quantitative structure retention relationships (QSRRs) are proposed and applied to describe two retention data sets: A set of 94 metabolites studied by a hydrophilic interaction chromatography system under organic content gradient conditions and a set of tryptophan and its major metabolites analyzed by a reversed-phase chromatographic system under isocratic as well as pH and/or simultaneous pH and organic content gradient conditions. According to the proposed modification, an additional descriptor is added to a conventional QSRR expression, which is the analyte retention time, tR(R), measured under the same elution conditions, but in a second chromatographic column considered as a reference one. The 94 metabolites were studied on an Amide column using a Bare Silica column as a reference. For the second dataset, a Kinetex EVO C18 and a Gemini-NX column were used, where each of them was served as a reference column of the other. We found in all cases a significant improvement of the performance of the QSRR models when the descriptor tR(R) was considered. PMID:28208794

  16. Integration of geographic information systems and logistic multiple regression for aquatic macrophyte modeling

    SciTech Connect

    Narumalani, S.; Jensen, J.R.; Althausen, J.D.; Burkhalter, S.; Mackey, H.E. Jr.

    1994-06-01

    Since aquatic macrophytes have an important influence on the physical and chemical processes of an ecosystem while simultaneously affecting human activity, it is imperative that they be inventoried and managed wisely. However, mapping wetlands can be a major challenge because they are found in diverse geographic areas ranging from small tributary streams, to shrub or scrub and marsh communities, to open water lacustrian environments. In addition, the type and spatial distribution of wetlands can change dramatically from season to season, especially when nonpersistent species are present. This research, focuses on developing a model for predicting the future growth and distribution of aquatic macrophytes. This model will use a geographic information system (GIS) to analyze some of the biophysical variables that affect aquatic macrophyte growth and distribution. The data will provide scientists information on the future spatial growth and distribution of aquatic macrophytes. This study focuses on the Savannah River Site Par Pond (1,000 ha) and L Lake (400 ha) these are two cooling ponds that have received thermal effluent from nuclear reactor operations. Par Pond was constructed in 1958, and natural invasion of wetland has occurred over its 35-year history, with much of the shoreline having developed extensive beds of persistent and non-persistent aquatic macrophytes.

  17. A Technique for Estimating Intensity of Emotional Expressions and Speaking Styles in Speech Based on Multiple-Regression HSMM

    NASA Astrophysics Data System (ADS)

    Nose, Takashi; Kobayashi, Takao

    In this paper, we propose a technique for estimating the degree or intensity of emotional expressions and speaking styles appearing in speech. The key idea is based on a style control technique for speech synthesis using a multiple regression hidden semi-Markov model (MRHSMM), and the proposed technique can be viewed as the inverse of the style control. In the proposed technique, the acoustic features of spectrum, power, fundamental frequency, and duration are simultaneously modeled using the MRHSMM. We derive an algorithm for estimating explanatory variables of the MRHSMM, each of which represents the degree or intensity of emotional expressions and speaking styles appearing in acoustic features of speech, based on a maximum likelihood criterion. We show experimental results to demonstrate the ability of the proposed technique using two types of speech data, simulated emotional speech and spontaneous speech with different speaking styles. It is found that the estimated values have correlation with human perception.

  18. Ca analysis: An Excel based program for the analysis of intracellular calcium transients including multiple, simultaneous regression analysis☆

    PubMed Central

    Greensmith, David J.

    2014-01-01

    Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow. PMID:24125908

  19. 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.

  20. 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.

  1. A Multiple Regression Analysis Between UV Radiation Measurements at Badajoz and Ozone, Reflectivity and Aerosols Estimated by TOMS

    NASA Astrophysics Data System (ADS)

    Antón, M.; Cancillo, M. L.; Serrano, A.; García, J. A.

    2005-01-01

    This paper analyzes the relationship between ultraviolet erythemal radiation (UVER) measured in Badajoz (Spain) and ozone, cloudiness and aerosols. Initially, the values of transmissivity of UVER are related with three parameters (ozone amount, reflectivity and aerosol index) estimated by the satellite instrument TOMS. The relative importance and dependence of each variable is analyzed by means of a multiple regression analysis with an expression derived from the Lambert-Bouger-Beer law. The results indicate that the aerosol index is not a statistically significant factor for the initial expression. Then, a partial model with only ozone and reflectivity as regressors is proposed and coefficients are obtained using UVER measurements of year 2001. Finally the model is validated comparing its prediction for 2002 with UVER measurements at ground. The agreement between both data sets is reasonably good, suggesting that UVER estimations can be successfully derived from observations of other atmospheric variables, thus providing the basis to obtain spatial distributed maps of UV variations.

  2. Soil organic carbon distribution in Mediterranean areas under a climate change scenario via multiple linear regression analysis.

    PubMed

    Olaya-Abril, Alfonso; Parras-Alcántara, Luis; Lozano-García, Beatriz; Obregón-Romero, Rafael

    2017-03-15

    Over time, the interest on soil studies has increased due to its role in carbon sequestration in terrestrial ecosystems, which could contribute to decreasing atmospheric CO2 rates. In many studies, independent variables were related to soil organic carbon (SOC) alone, however, the contribution degree of each variable with the experimentally determined SOC content were not considered. In this study, samples from 612 soil profiles were obtained in a natural protected (Red Natura 2000) of Sierra Morena (Mediterranean area, South Spain), considering only the topsoil 0-25cm, for better comparison between results. 24 independent variables were used to define it relationship with SOC content. Subsequently, using a multiple linear regression analysis, the effects of these variables on the SOC correlation was considered. Finally, the best parameters determined with the regression analysis were used in a climatic change scenario. The model indicated that SOC in a future scenario of climate change depends on average temperature of coldest quarter (41.9%), average temperature of warmest quarter (34.5%), annual precipitation (22.2%) and annual average temperature (1.3%). When the current and future situations were compared, the SOC content in the study area was reduced a 35.4%, and a trend towards migration to higher latitude and altitude was observed.

  3. Modeling the energy content of combustible ship-scrapping waste at Alang-Sosiya, India, using multiple regression analysis.

    PubMed

    Reddy, M Srinivasa; Basha, Shaik; Joshi, H V; Sravan Kumar, V G; Jha, B; Ghosh, P K

    2005-01-01

    Alang-Sosiya is the largest ship-scrapping yard in the world, established in 1982. Every year an average of 171 ships having a mean weight of 2.10 x 10(6)(+/-7.82 x 10(5)) of light dead weight tonnage (LDT) being scrapped. Apart from scrapped metals, this yard generates a massive amount of combustible solid waste in the form of waste wood, plastic, insulation material, paper, glass wool, thermocol pieces (polyurethane foam material), sponge, oiled rope, cotton waste, rubber, etc. In this study multiple regression analysis was used to develop predictive models for energy content of combustible ship-scrapping solid wastes. The scope of work comprised qualitative and quantitative estimation of solid waste samples and performing a sequential selection procedure for isolating variables. Three regression models were developed to correlate the energy content (net calorific values (LHV)) with variables derived from material composition, proximate and ultimate analyses. The performance of these models for this particular waste complies well with the equations developed by other researchers (Dulong, Steuer, Scheurer-Kestner and Bento's) for estimating energy content of municipal solid waste.

  4. Intensity normalization of additive and multiplicative spatially multiplexed patterns with n encoded phases

    NASA Astrophysics Data System (ADS)

    Juarez-Salazar, Rigoberto; Robledo-Sanchez, Carlos; Guerrero-Sanchez, Fermin; Barcelata-Pinzon, Antonio; Gonzalez-Garcia, Jorge; Santiago-Alvarado, Agustin

    2016-02-01

    An efficient, robust and user-free intensity normalization method for patterns with n frequency-multiplexed phases of both additive and multiplicative types is proposed. For this, the parameter estimation approach by using the least-squares method is applied. The theoretical principles are given and the good performance of the proposal is verified by computer simulation and experimental results. Because the proposed method has good features such as robustness, simplicity, fast and user-free execution, it could be implemented in a wide variety of applications. This contribution motivates future generalization in both phase demodulation algorithms and experimental setups to exploit the benefits of additive/multiplicative patterns with multiple phases.

  5. Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.

    PubMed

    Ventura, Cristina; Latino, Diogo A R S; Martins, Filomena

    2013-01-01

    The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models.

  6. Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics

    USGS Publications Warehouse

    Lee, L.; Helsel, D.

    2005-01-01

    Trace contaminants in water, including metals and organics, often are measured at sufficiently low concentrations to be reported only as values below the instrument detection limit. Interpretation of these "less thans" is complicated when multiple detection limits occur. Statistical methods for multiply censored, or multiple-detection limit, datasets have been developed for medical and industrial statistics, and can be employed to estimate summary statistics or model the distributions of trace-level environmental data. We describe S-language-based software tools that perform robust linear regression on order statistics (ROS). The ROS method has been evaluated as one of the most reliable procedures for developing summary statistics of multiply censored data. It is applicable to any dataset that has 0 to 80% of its values censored. These tools are a part of a software library, or add-on package, for the R environment for statistical computing. This library can be used to generate ROS models and associated summary statistics, plot modeled distributions, and predict exceedance probabilities of water-quality standards. ?? 2005 Elsevier Ltd. All rights reserved.

  7. Predicting Patient Advocacy Engagement: A Multiple Regression Analysis Using Data From Health Professionals in Acute-Care Hospitals.

    PubMed

    Jansson, Bruce S; Nyamathi, Adeline; Heidemann, Gretchen; Duan, Lei; Kaplan, Charles

    2015-01-01

    Although literature documents the need for hospital social workers, nurses, and medical residents to engage in patient advocacy, little information exists about what predicts the extent they do so. This study aims to identify predictors of health professionals' patient advocacy engagement with respect to a broad range of patients' problems. A cross-sectional research design was employed with a sample of 94 social workers, 97 nurses, and 104 medical residents recruited from eight hospitals in Los Angeles. Bivariate correlations explored whether seven scales (Patient Advocacy Eagerness, Ethical Commitment, Skills, Tangible Support, Organizational Receptivity, Belief Other Professionals Engage, and Belief the Hospital Empowers Patients) were associated with patient advocacy engagement, measured by the validated Patient Advocacy Engagement Scale. Regression analysis examined whether these scales, when controlling for sociodemographic and setting variables, predicted patient advocacy engagement. While all seven predictor scales were significantly associated with patient advocacy engagement in correlational analyses, only Eagerness, Skills, and Belief the Hospital Empowers Patients predicted patient advocacy engagement in regression analyses. Additionally, younger professionals engaged in higher levels of patient advocacy than older professionals, and social workers engaged in greater patient advocacy than nurses. Limitations and the utility of these findings for acute-care hospitals are discussed.

  8. Combining different functions to describe milk, fat, and protein yield in goats using Bayesian multiple-trait random regression models.

    PubMed

    Oliveira, H R; Silva, F F; Siqueira, O H G B D; Souza, N O; Junqueira, V S; Resende, M D V; Borquis, R R A; Rodrigues, M T

    2016-05-01

    We proposed multiple-trait random regression models (MTRRM) combining different functions to describe milk yield (MY) and fat (FP) and protein (PP) percentage in dairy goat genetic evaluation by using Bayesian inference. A total of 3,856 MY, FP, and PP test-day records, measured between 2000 and 2014, from 535 first lactations of Saanen and Alpine goats, including their cross, were used in this study. The initial analyses were performed using the following single-trait random regression models (STRRM): third- and fifth-order Legendre polynomials (Leg3 and Leg5), linear B-splines with 3 and 5 knots, the Ali and Schaeffer function (Ali), and Wilmink function. Heterogeneity of residual variances was modeled considering 3 classes. After the selection of the best STRRM to describe each trait on the basis of the deviance information criterion (DIC) and posterior model probabilities (PMP), the functions were combined to compose the MTRRM. All combined MTRRM presented lower DIC values and higher PMP, showing the superiority of these models when compared to other MTRRM based only on the same function assumed for all traits. Among the combined MTRRM, those considering Ali to describe MY and PP and Leg5 to describe FP (Ali_Leg5_Ali model) presented the best fit. From the Ali_Leg5_Ali model, heritability estimates over time for MY, FP. and PP ranged from 0.25 to 0.54, 0.27 to 0.48, and 0.35 to 0.51, respectively. Genetic correlation between MY and FP, MY and PP, and FP and PP ranged from -0.58 to 0.03, -0.46 to 0.12, and 0.37 to 0.64, respectively. We concluded that combining different functions under a MTRRM approach can be a plausible alternative for joint genetic evaluation of milk yield and milk constituents in goats.

  9. Stochastic Vortex Dynamics in Two-Dimensional Easy Plane Ferromagnets: Multiplicative Versus Additive Noise

    SciTech Connect

    Kamppeter, T.; Mertens, F.G.; Moro, E.; Sanchez, A.; Bishop, A.R.

    1998-09-01

    We study how thermal fluctuations affect the dynamics of vortices in the two-dimensional anisotropic Heisenberg model depending on their additive or multiplicative character. Using a collective coordinate theory, we analytically show that multiplicative noise, arising from fluctuations in the local field term of the Landau-Lifshitz equations, and Langevin-like additive noise have the same effect on vortex dynamics (within a very plausible assumption consistent with the collective coordinate approach). This is a highly non-trivial result as multiplicative and additive noises usually modify the dynamics in very different ways. We also carry out numerical simulations of both versions of the model finding that they indeed give rise to very similar vortex dynamics.

  10. Estimation of human circadian phase via a multi-channel ambulatory monitoring system and a multiple regression model.

    PubMed

    Kolodyazhniy, Vitaliy; Späti, Jakub; Frey, Sylvia; Götz, Thomas; Wirz-Justice, Anna; Kräuchi, Kurt; Cajochen, Christian; Wilhelm, Frank H

    2011-02-01

    Reliable detection of circadian phase in humans using noninvasive ambulatory measurements in real-life conditions is challenging and still an unsolved problem. The masking effects of everyday behavior and environmental input such as physical activity and light on the measured variables need to be considered critically. Here, we aimed at developing techniques for estimating circadian phase with the lowest subject burden possible, that is, without the need of constant routine (CR) laboratory conditions or without measuring the standard circadian markers, (rectal) core body temperature (CBT), and melatonin levels. In this validation study, subjects (N = 16) wore multi-channel ambulatory monitoring devices and went about their daily routine for 1 week. The devices measured a large number of physiological, behavioral, and environmental variables, including CBT, skin temperatures, cardiovascular and respiratory function, movement/posture, ambient temperature, and the spectral composition and intensity of light received at eye level. Sleep diaries were logged electronically. After the ambulatory phase, subjects underwent a 32-h CR procedure in the laboratory for measuring unmasked circadian phase based on the "midpoint" of the salivary melatonin profile. To overcome the complex masking effects of confounding variables during ambulatory measurements, multiple regression techniques were applied in combination with the cross-validation approach to subject-independent prediction of circadian phase. The most accurate estimate of circadian phase was achieved using skin temperatures, irradiance for ambient light in the blue spectral band, and motion acceleration as predictors with lags of up to 24 h. Multiple regression showed statistically significant improvement of variance of prediction error over the traditional approaches to determining circadian phase based on single predictors (motion acceleration or sleep log), although CBT was intentionally not included as the predictor

  11. Logistic Regression

    NASA Astrophysics Data System (ADS)

    Grégoire, G.

    2014-12-01

    The logistic regression originally is intended to explain the relationship between the probability of an event and a set of covariables. The model's coefficients can be interpreted via the odds and odds ratio, which are presented in introduction of the chapter. The observations are possibly got individually, then we speak of binary logistic regression. When they are grouped, the logistic regression is said binomial. In our presentation we mainly focus on the binary case. For statistical inference the main tool is the maximum likelihood methodology: we present the Wald, Rao and likelihoods ratio results and their use to compare nested models. The problems we intend to deal with are essentially the same as in multiple linear regression: testing global effect, individual effect, selection of variables to build a model, measure of the fitness of the model, prediction of new values… . The methods are demonstrated on data sets using R. Finally we briefly consider the binomial case and the situation where we are interested in several events, that is the polytomous (multinomial) logistic regression and the particular case of ordinal logistic regression.

  12. A multiple imputation approach to the analysis of clustered interval-censored failure time data with the additive hazards model

    PubMed Central

    Chen, Ling; Sun, Jianguo; Xiong, Chengjie

    2016-01-01

    Clustered interval-censored failure time data can occur when the failure time of interest is collected from several clusters and known only within certain time intervals. Regression analysis of clustered interval-censored failure time data is discussed assuming that the data arise from the semiparametric additive hazards model. A multiple imputation approach is proposed for inference. A major advantage of the approach is its simplicity because it avoids estimating the correlation within clusters by implementing a resampling-based method. The presented approach can be easily implemented by using the existing software packages for right-censored failure time data. Extensive simulation studies are conducted, indicating that the proposed imputation approach performs well for practical situations. The proposed approach also performs well compared to the existing methods and can be more conveniently applied to various types of data representation. The proposed methodology is further demonstrated by applying it to a lymphatic filariasis study. PMID:27773956

  13. Additives

    NASA Technical Reports Server (NTRS)

    Smalheer, C. V.

    1973-01-01

    The chemistry of lubricant additives is discussed to show what the additives are chemically and what functions they perform in the lubrication of various kinds of equipment. Current theories regarding the mode of action of lubricant additives are presented. The additive groups discussed include the following: (1) detergents and dispersants, (2) corrosion inhibitors, (3) antioxidants, (4) viscosity index improvers, (5) pour point depressants, and (6) antifouling agents.

  14. Simulation scheme of dusk scene using piece-wise multiple regression based on time-series color-block images

    NASA Astrophysics Data System (ADS)

    Liu, Chen-Chung; Yang, Chih-Chao

    2010-09-01

    Dusk and dawn are usually the most beautiful moments of the day, and are almost always too short for busy people nowadays to witness their coming. In this work, an efficient strategy for simulating a dusk scene of an outdoor scene image taken at other times before the sunset is presented. The strategy is a hybrid approach combining the piece-wise multiple regression (PMR) of data, discrete cosine transformation (DCT), and a look-up table algorithm. The process begins using a series of color-block images taken in the afternoon of a day. The best fitting functions of PMR for these color block images exist on separate planes (red, green, and blue) in the DCT domain individually. The reference databases of the DCT coefficients varying with respect to time are then established according to the best fitting functions of PMR. Finally, the dusk scene of an outdoor scene taken in the afternoon is synthesized by querying the reference database. The experiment results show that the presented algorithm can precisely simulate the desired dusk scene from a scene image taken in the afternoon.

  15. Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting

    NASA Astrophysics Data System (ADS)

    Armaghani, Danial Jahed; Mahdiyar, Amir; Hasanipanah, Mahdi; Faradonbeh, Roohollah Shirani; Khandelwal, Manoj; Amnieh, Hassan Bakhshandeh

    2016-09-01

    Flyrock is considered as one of the main causes of human injury, fatalities, and structural damage among all undesirable environmental impacts of blasting. Therefore, it seems that the proper prediction/simulation of flyrock is essential, especially in order to determine blast safety area. If proper control measures are taken, then the flyrock distance can be controlled, and, in return, the risk of damage can be reduced or eliminated. The first objective of this study was to develop a predictive model for flyrock estimation based on multiple regression (MR) analyses, and after that, using the developed MR model, flyrock phenomenon was simulated by the Monte Carlo (MC) approach. In order to achieve objectives of this study, 62 blasting operations were investigated in Ulu Tiram quarry, Malaysia, and some controllable and uncontrollable factors were carefully recorded/calculated. The obtained results of MC modeling indicated that this approach is capable of simulating flyrock ranges with a good level of accuracy. The mean of simulated flyrock by MC was obtained as 236.3 m, while this value was achieved as 238.6 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that powder factor is the most influential parameter on fly rock among all model inputs. It is noticeable that the proposed MR and MC models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended.

  16. Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

    PubMed Central

    Shabri, Ani; Samsudin, Ruhaidah

    2014-01-01

    Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666

  17. Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error

    PubMed Central

    Jäntschi, Lorentz

    2016-01-01

    Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest. Usually, under the assumption that the errors follow a normal distribution, the coefficients of the model are estimated by minimizing the sum of squared deviations. A new approach based on maximum likelihood estimation is proposed for finding the coefficients on linear models with two predictors without any constrictive assumptions on the distribution of the errors. The algorithm was developed, implemented, and tested as proof-of-concept using fourteen sets of compounds by investigating the link between activity/property (as outcome) and structural feature information incorporated by molecular descriptors (as predictors). The results on real data demonstrated that in all investigated cases the power of the error is significantly different by the convenient value of two when the Gauss-Laplace distribution was used to relax the constrictive assumption of the normal distribution of the error. Therefore, the Gauss-Laplace distribution of the error could not be rejected while the hypothesis that the power of the error from Gauss-Laplace distribution is normal distributed also failed to be rejected. PMID:28090215

  18. QSAR study of HCV NS5B polymerase inhibitors using the genetic algorithm-multiple linear regression (GA-MLR)

    PubMed Central

    Rafiei, Hamid; Khanzadeh, Marziyeh; Mozaffari, Shahla; Bostanifar, Mohammad Hassan; Avval, Zhila Mohajeri; Aalizadeh, Reza; Pourbasheer, Eslam

    2016-01-01

    Quantitative structure-activity relationship (QSAR) study has been employed for predicting the inhibitory activities of the Hepatitis C virus (HCV) NS5B polymerase inhibitors. A data set consisted of 72 compounds was selected, and then different types of molecular descriptors were calculated. The whole data set was split into a training set (80 % of the dataset) and a test set (20 % of the dataset) using principle component analysis. The stepwise (SW) and the genetic algorithm (GA) techniques were used as variable selection tools. Multiple linear regression method was then used to linearly correlate the selected descriptors with inhibitory activities. Several validation technique including leave-one-out and leave-group-out cross-validation, Y-randomization method were used to evaluate the internal capability of the derived models. The external prediction ability of the derived models was further analyzed using modified r2, concordance correlation coefficient values and Golbraikh and Tropsha acceptable model criteria's. Based on the derived results (GA-MLR), some new insights toward molecular structural requirements for obtaining better inhibitory activity were obtained. PMID:27065774

  19. Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error.

    PubMed

    Jäntschi, Lorentz; Bálint, Donatella; Bolboacă, Sorana D

    2016-01-01

    Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest. Usually, under the assumption that the errors follow a normal distribution, the coefficients of the model are estimated by minimizing the sum of squared deviations. A new approach based on maximum likelihood estimation is proposed for finding the coefficients on linear models with two predictors without any constrictive assumptions on the distribution of the errors. The algorithm was developed, implemented, and tested as proof-of-concept using fourteen sets of compounds by investigating the link between activity/property (as outcome) and structural feature information incorporated by molecular descriptors (as predictors). The results on real data demonstrated that in all investigated cases the power of the error is significantly different by the convenient value of two when the Gauss-Laplace distribution was used to relax the constrictive assumption of the normal distribution of the error. Therefore, the Gauss-Laplace distribution of the error could not be rejected while the hypothesis that the power of the error from Gauss-Laplace distribution is normal distributed also failed to be rejected.

  20. Comparing Effects of Biologic Agents in Treating Patients with Rheumatoid Arthritis: A Multiple Treatment Comparison Regression Analysis

    PubMed Central

    Tvete, Ingunn Fride; Natvig, Bent; Gåsemyr, Jørund; Meland, Nils; Røine, Marianne; Klemp, Marianne

    2015-01-01

    Rheumatoid arthritis patients have been treated with disease modifying anti-rheumatic drugs (DMARDs) and the newer biologic drugs. We sought to compare and rank the biologics with respect to efficacy. We performed a literature search identifying 54 publications encompassing 9 biologics. We conducted a multiple treatment comparison regression analysis letting the number experiencing a 50% improvement on the ACR score be dependent upon dose level and disease duration for assessing the comparable relative effect between biologics and placebo or DMARD. The analysis embraced all treatment and comparator arms over all publications. Hence, all measured effects of any biologic agent contributed to the comparison of all biologic agents relative to each other either given alone or combined with DMARD. We found the drug effect to be dependent on dose level, but not on disease duration, and the impact of a high versus low dose level was the same for all drugs (higher doses indicated a higher frequency of ACR50 scores). The ranking of the drugs when given without DMARD was certolizumab (ranked highest), etanercept, tocilizumab/ abatacept and adalimumab. The ranking of the drugs when given with DMARD was certolizumab (ranked highest), tocilizumab, anakinra, rituximab, golimumab/ infliximab/ abatacept, adalimumab/ etanercept. Still, all drugs were effective. All biologic agents were effective compared to placebo, with certolizumab the most effective and adalimumab (without DMARD treatment) and adalimumab/ etanercept (combined with DMARD treatment) the least effective. The drugs were in general more effective, except for etanercept, when given together with DMARDs. PMID:26356639

  1. Ranking contributing areas of salt and selenium in the Lower Gunnison River Basin, Colorado, using multiple linear regression models

    USGS Publications Warehouse

    Linard, Joshua I.

    2013-01-01

    Mitigating the effects of salt and selenium on water quality in the Grand Valley and lower Gunnison River Basin in western Colorado is a major concern for land managers. Previous modeling indicated means to improve the models by including more detailed geospatial data and a more rigorous method for developing the models. After evaluating all possible combinations of geospatial variables, four multiple linear regression models resulted that could estimate irrigation-season salt yield, nonirrigation-season salt yield, irrigation-season selenium yield, and nonirrigation-season selenium yield. The adjusted r-squared and the residual standard error (in units of log-transformed yield) of the models were, respectively, 0.87 and 2.03 for the irrigation-season salt model, 0.90 and 1.25 for the nonirrigation-season salt model, 0.85 and 2.94 for the irrigation-season selenium model, and 0.93 and 1.75 for the nonirrigation-season selenium model. The four models were used to estimate yields and loads from contributing areas corresponding to 12-digit hydrologic unit codes in the lower Gunnison River Basin study area. Each of the 175 contributing areas was ranked according to its estimated mean seasonal yield of salt and selenium.

  2. The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

    SciTech Connect

    Jahandideh, Sepideh Jahandideh, Samad; Asadabadi, Ebrahim Barzegari; Askarian, Mehrdad; Movahedi, Mohammad Mehdi; Hosseini, Somayyeh; Jahandideh, Mina

    2009-11-15

    Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.

  3. The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation.

    PubMed

    Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari; Askarian, Mehrdad; Movahedi, Mohammad Mehdi; Hosseini, Somayyeh; Jahandideh, Mina

    2009-11-01

    Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R(2) were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R(2) confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.

  4. Estimating Dbh of Trees Employing Multiple Linear Regression of the best Lidar-Derived Parameter Combination Automated in Python in a Natural Broadleaf Forest in the Philippines

    NASA Astrophysics Data System (ADS)

    Ibanez, C. A. G.; Carcellar, B. G., III; Paringit, E. C.; Argamosa, R. J. L.; Faelga, R. A. G.; Posilero, M. A. V.; Zaragosa, G. P.; Dimayacyac, N. A.

    2016-06-01

    Diameter-at-Breast-Height Estimation is a prerequisite in various allometric equations estimating important forestry indices like stem volume, basal area, biomass and carbon stock. LiDAR Technology has a means of directly obtaining different forest parameters, except DBH, from the behavior and characteristics of point cloud unique in different forest classes. Extensive tree inventory was done on a two-hectare established sample plot in Mt. Makiling, Laguna for a natural growth forest. Coordinates, height, and canopy cover were measured and types of species were identified to compare to LiDAR derivatives. Multiple linear regression was used to get LiDAR-derived DBH by integrating field-derived DBH and 27 LiDAR-derived parameters at 20m, 10m, and 5m grid resolutions. To know the best combination of parameters in DBH Estimation, all possible combinations of parameters were generated and automated using python scripts and additional regression related libraries such as Numpy, Scipy, and Scikit learn were used. The combination that yields the highest r-squared or coefficient of determination and lowest AIC (Akaike's Information Criterion) and BIC (Bayesian Information Criterion) was determined to be the best equation. The equation is at its best using 11 parameters at 10mgrid size and at of 0.604 r-squared, 154.04 AIC and 175.08 BIC. Combination of parameters may differ among forest classes for further studies. Additional statistical tests can be supplemented to help determine the correlation among parameters such as Kaiser- Meyer-Olkin (KMO) Coefficient and the Barlett's Test for Spherecity (BTS).

  5. Rainfall estimation by rain gauge-radar combination: A concurrent multiplicative-additive approach

    NASA Astrophysics Data System (ADS)

    GarcíA-Pintado, Javier; Barberá, Gonzalo G.; Erena, Manuel; Castillo, Victor M.

    2009-01-01

    A procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) is proposed for operational rainfall estimation using rain gauges and radar data. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on Barnes' objective analysis scheme (OAS), whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km2. Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, a spatially variable adjustment with multiplicative factors, and ordinary cokriging.

  6. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression

    USGS Publications Warehouse

    Kokaly, R.F.; Clark, R.N.

    1999-01-01

    We develop a new method for estimating the biochemistry of plant material using spectroscopy. Normalized band depths calculated from the continuum-removed reflectance spectra of dried and ground leaves were used to estimate their concentrations of nitrogen, lignin, and cellulose. Stepwise multiple linear regression was used to select wavelengths in the broad absorption features centered at 1.73 ??m, 2.10 ??m, and 2.30 ??m that were highly correlated with the chemistry of samples from eastern U.S. forests. Band depths of absorption features at these wavelengths were found to also be highly correlated with the chemistry of four other sites. A subset of data from the eastern U.S. forest sites was used to derive linear equations that were applied to the remaining data to successfully estimate their nitrogen, lignin, and cellulose concentrations. Correlations were highest for nitrogen (R2 from 0.75 to 0.94). The consistent results indicate the possibility of establishing a single equation capable of estimating the chemical concentrations in a wide variety of species from the reflectance spectra of dried leaves. The extension of this method to remote sensing was investigated. The effects of leaf water content, sensor signal-to-noise and bandpass, atmospheric effects, and background soil exposure were examined. Leaf water was found to be the greatest challenge to extending this empirical method to the analysis of fresh whole leaves and complete vegetation canopies. The influence of leaf water on reflectance spectra must be removed to within 10%. Other effects were reduced by continuum removal and normalization of band depths. If the effects of leaf water can be compensated for, it might be possible to extend this method to remote sensing data acquired by imaging spectrometers to give estimates of nitrogen, lignin, and cellulose concentrations over large areas for use in ecosystem studies.We develop a new method for estimating the biochemistry of plant material using

  7. Multiple regression analysis to assess the role of plankton on the distribution and speciation of mercury in water of a contaminated lagoon.

    PubMed

    Stoichev, T; Tessier, E; Amouroux, D; Almeida, C M; Basto, M C P; Vasconcelos, V M

    2016-11-15

    Spatial and seasonal variation of mercury species aqueous concentrations and distributions was carried out during six sampling campaigns at four locations within Laranjo Bay, the most mercury-contaminated area of the Aveiro Lagoon (Portugal). Inorganic mercury (IHg(II)) and methylmercury (MeHg) were determined in filter-retained (IHgPART, MeHgPART) and filtered (<0.45μm) fractions (IHg(II)DISS, MeHgDISS). The concentrations of IHgPART depended on site and on dilution with downstream particles. Similar processes were evidenced for MeHgPART, however, its concentrations increased for particles rich in phaeophytin (Pha). The concentrations of MeHgDISS, and especially those of IHg(II)DISS, increased with Pha concentrations in the water. Multiple regression models are able to depict MeHgPART, IHg(II)DISS and MeHgDISS concentrations with salinity and Pha concentrations exhibiting additive statistical effects and allowing separation of possible addition and removal processes. A link between phytoplankton/algae and consumers' grazing pressure in the contaminated area can be involved to increase concentrations of IHg(II)DISS and MeHgPART. These processes could lead to suspended particles enriched with MeHg and to the enhancement of IHg(II) and MeHg availability in surface waters and higher transfer to the food web.

  8. Predicting Distribution and Inter-Annual Variability of Tropical Cyclone Intensity from a Stochastic, Multiple-Linear Regression Model

    NASA Astrophysics Data System (ADS)

    Lee, C. Y.; Tippett, M. K.; Sobel, A. H.; Camargo, S. J.

    2014-12-01

    We are working towards the development of a new statistical-dynamical downscaling system to study the influence of climate on tropical cyclones (TCs). The first step is development of an appropriate model for TC intensity as a function of environmental variables. We approach this issue with a stochastic model consisting of a multiple linear regression model (MLR) for 12-hour intensity forecasts as a deterministic component, and a random error generator as a stochastic component. Similar to the operational Statistical Hurricane Intensity Prediction Scheme (SHIPS), MLR relates the surrounding environment to storm intensity, but with only essential predictors calculated from monthly-mean NCEP reanalysis fields (potential intensity, shear, etc.) and from persistence. The deterministic MLR is developed with data from 1981-1999 and tested with data from 2000-2012 for the Atlantic, Eastern North Pacific, Western North Pacific, Indian Ocean, and Southern Hemisphere basins. While the global MLR's skill is comparable to that of the operational statistical models (e.g., SHIPS), the distribution of the predicted maximum intensity from deterministic results has a systematic low bias compared to observations; the deterministic MLR creates almost no storms with intensities greater than 100 kt. The deterministic MLR can be significantly improved by adding the stochastic component, based on the distribution of random forecasting errors from the deterministic model compared to the training data. This stochastic component may be thought of as representing the component of TC intensification that is not linearly related to the environmental variables. We find that in order for the stochastic model to accurately capture the observed distribution of maximum storm intensities, the stochastic component must be auto-correlated across 12-hour time steps. This presentation also includes a detailed discussion of the distributions of other TC-intensity related quantities, as well as the inter

  9. Prediction of the processing factor for pesticides in apple juice by principal component analysis and multiple linear regression.

    PubMed

    Martin, L; Mezcua, M; Ferrer, C; Gil Garcia, M D; Malato, O; Fernandez-Alba, A R

    2013-01-01

    The main objective of this work was to establish a mathematical function that correlates pesticide residue levels in apple juice with the levels of the pesticides applied on the raw fruit, taking into account some of their physicochemical properties such as water solubility, the octanol/water partition coefficient, the organic carbon partition coefficient, vapour pressure and density. A mixture of 12 pesticides was applied to an apple tree; apples were collected after 10 days of application. After harvest, apples were treated with a mixture of three post-harvest pesticides and the fruits were then processed in order to obtain apple juice following a routine industrial process. The pesticide residue levels in the apple samples were analysed using two multi-residue methods based on LC-MS/MS and GC-MS/MS. The concentration of pesticides was determined in samples derived from the different steps of processing. The processing factors (the coefficient between residue level in the processed commodity and the residue level in the commodity to be processed) obtained for the full juicing process were found to vary among the different pesticides studied. In order to investigate the relationships between the levels of pesticide residue found in apple juice samples and their physicochemical properties, principal component analysis (PCA) was performed using two sets of samples (one of them using experimental data obtained in this work and the other including the data taken from the literature). In both cases the correlation was found between processing factors of pesticides in the apple juice and the negative logarithms (base 10) of the water solubility, octanol/water partition coefficient and organic carbon partition coefficient. The linear correlation between these physicochemical properties and the processing factor were established using a multiple linear regression technique.

  10. Predicting punching acceleration from selected strength and power variables in elite karate athletes: a multiple regression analysis.

    PubMed

    Loturco, Irineu; Artioli, Guilherme Giannini; Kobal, Ronaldo; Gil, Saulo; Franchini, Emerson

    2014-07-01

    This study investigated the relationship between punching acceleration and selected strength and power variables in 19 professional karate athletes from the Brazilian National Team (9 men and 10 women; age, 23 ± 3 years; height, 1.71 ± 0.09 m; and body mass [BM], 67.34 ± 13.44 kg). Punching acceleration was assessed under 4 different conditions in a randomized order: (a) fixed distance aiming to attain maximum speed (FS), (b) fixed distance aiming to attain maximum impact (FI), (c) self-selected distance aiming to attain maximum speed, and (d) self-selected distance aiming to attain maximum impact. The selected strength and power variables were as follows: maximal dynamic strength in bench press and squat-machine, squat and countermovement jump height, mean propulsive power in bench throw and jump squat, and mean propulsive velocity in jump squat with 40% of BM. Upper- and lower-body power and maximal dynamic strength variables were positively correlated to punch acceleration in all conditions. Multiple regression analysis also revealed predictive variables: relative mean propulsive power in squat jump (W·kg-1), and maximal dynamic strength 1 repetition maximum in both bench press and squat-machine exercises. An impact-oriented instruction and a self-selected distance to start the movement seem to be crucial to reach the highest acceleration during punching execution. This investigation, while demonstrating strong correlations between punching acceleration and strength-power variables, also provides important information for coaches, especially for designing better training strategies to improve punching speed.

  11. One-pot construction of multiple contiguous chiral centers using Michael addition of chiral amine.

    PubMed

    Ozeki, Minoru; Ochi, Shunsuke; Hayama, Noboru; Hosoi, Shinzo; Kajimoto, Tetsuya; Node, Manabu

    2010-06-18

    Multiple contiguous chiral centers were constructed in one pot using three types of multistep reactions initiated with the Michael addition of N-benzyl-2(R)-methoxy-(+)-10-bornylamide to alpha,beta-unsaturated esters, i.e., asymmetric Michael-aldol reaction, double Michael addition, and double Michael-aldol reaction. The chiral 2-methoxy-10-bornyl group as well as the benzyl group on the amino group of the products in the Michael-aldol reaction could be easily cleaved by treatment with NIS (4 equiv), and beta-amino esters with multiple contiguous chiral centers were obtained in good yield. As an application, the beta-amino-beta'-hydroxy ester obtained in the asymmetric Michael-aldol reaction was converted to the beta-lactam derivative in good yield.

  12. Impression technique for a complete-arch prosthesis with multiple implants using additive manufacturing technologies.

    PubMed

    Revilla-León, Marta; Sánchez-Rubio, José Luis; Oteo-Calatayud, Jesús; Özcan, Mutlu

    2016-11-23

    This article describes an impression technique for a complete-arch prosthesis supported by multiple implants where additive manufacturing technologies were used to fabricate a splinting framework and a custom tray. The technique presented uses a shim method to control the homogenous splinting acrylic resin and impression material during the procedure, thereby reducing laboratory and chairside time and the number of impression copings and laboratory analogs needed.

  13. Additivity of Feature-Based and Symmetry-Based Grouping Effects in Multiple Object Tracking.

    PubMed

    Wang, Chundi; Zhang, Xuemin; Li, Yongna; Lyu, Chuang

    2016-01-01

    Multiple object tracking (MOT) is an attentional process wherein people track several moving targets among several distractors. Symmetry, an important indicator of regularity, is a general spatial pattern observed in natural and artificial scenes. According to the "laws of perceptual organization" proposed by Gestalt psychologists, regularity is a principle of perceptual grouping, such as similarity and closure. A great deal of research reported that feature-based similarity grouping (e.g., grouping based on color, size, or shape) among targets in MOT tasks can improve tracking performance. However, no additive feature-based grouping effects have been reported where the tracking objects had two or more features. "Additive effect" refers to a greater grouping effect produced by grouping based on multiple cues instead of one cue. Can spatial symmetry produce a similar grouping effect similar to that of feature similarity in MOT tasks? Are the grouping effects based on symmetry and feature similarity additive? This study includes four experiments to address these questions. The results of Experiments 1 and 2 demonstrated the automatic symmetry-based grouping effects. More importantly, an additive grouping effect of symmetry and feature similarity was observed in Experiments 3 and 4. Our findings indicate that symmetry can produce an enhanced grouping effect in MOT and facilitate the grouping effect based on color or shape similarity. The "where" and "what" pathways might have played an important role in the additive grouping effect.

  14. Additivity of Feature-Based and Symmetry-Based Grouping Effects in Multiple Object Tracking

    PubMed Central

    Wang, Chundi; Zhang, Xuemin; Li, Yongna; Lyu, Chuang

    2016-01-01

    Multiple object tracking (MOT) is an attentional process wherein people track several moving targets among several distractors. Symmetry, an important indicator of regularity, is a general spatial pattern observed in natural and artificial scenes. According to the “laws of perceptual organization” proposed by Gestalt psychologists, regularity is a principle of perceptual grouping, such as similarity and closure. A great deal of research reported that feature-based similarity grouping (e.g., grouping based on color, size, or shape) among targets in MOT tasks can improve tracking performance. However, no additive feature-based grouping effects have been reported where the tracking objects had two or more features. “Additive effect” refers to a greater grouping effect produced by grouping based on multiple cues instead of one cue. Can spatial symmetry produce a similar grouping effect similar to that of feature similarity in MOT tasks? Are the grouping effects based on symmetry and feature similarity additive? This study includes four experiments to address these questions. The results of Experiments 1 and 2 demonstrated the automatic symmetry-based grouping effects. More importantly, an additive grouping effect of symmetry and feature similarity was observed in Experiments 3 and 4. Our findings indicate that symmetry can produce an enhanced grouping effect in MOT and facilitate the grouping effect based on color or shape similarity. The “where” and “what” pathways might have played an important role in the additive grouping effect. PMID:27199875

  15. The use of regression analysis in determining reference intervals for low hematocrit and thrombocyte count in multiple electrode aggregometry and platelet function analyzer 100 testing of platelet function.

    PubMed

    Kuiper, Gerhardus J A J M; Houben, Rik; Wetzels, Rick J H; Verhezen, Paul W M; van Oerle, Rene; Ten Cate, Hugo; Henskens, Yvonne M C; Lancé, Marcus D

    2017-01-09

    Low platelet counts and hematocrit levels hinder whole blood point-of-care testing of platelet function. Thus far, no reference ranges for MEA (multiple electrode aggregometry) and PFA-100 (platelet function analyzer 100) devices exist for low ranges. Through dilution methods of volunteer whole blood, platelet function at low ranges of platelet count and hematocrit levels was assessed on MEA for four agonists and for PFA-100 in two cartridges. Using (multiple) regression analysis, 95% reference intervals were computed for these low ranges. Low platelet counts affected MEA in a positive correlation (all agonists showed r(2) ≥ 0.75) and PFA-100 in an inverse correlation (closure times were prolonged with lower platelet counts). Lowered hematocrit did not affect MEA testing, except for arachidonic acid activation (ASPI), which showed a weak positive correlation (r(2) = 0.14). Closure time on PFA-100 testing was inversely correlated with hematocrit for both cartridges. Regression analysis revealed different 95% reference intervals in comparison with originally established intervals for both MEA and PFA-100 in low platelet or hematocrit conditions. Multiple regression analysis of ASPI and both tests on the PFA-100 for combined low platelet and hematocrit conditions revealed that only PFA-100 testing should be adjusted for both thrombocytopenia and anemia. 95% reference intervals were calculated using multiple regression analysis. However, coefficients of determination of PFA-100 were poor, and some variance remained unexplained. Thus, in this pilot study using (multiple) regression analysis, we could establish reference intervals of platelet function in anemia and thrombocytopenia conditions on PFA-100 and in thrombocytopenia conditions on MEA.

  16. Mass dependence of instabilities of an oscillator with multiplicative and additive noise.

    PubMed

    Gitterman, Moshe; Kessler, David A

    2013-02-01

    We study the instabilities of a harmonic oscillator subject to additive and dichotomous multiplicative noise, focusing on the dependence of the instability threshold on the mass. For multiplicative noise in the damping, the energy instability threshold is crossed as the mass is decreased, as long as the smaller damping is in fact negative. For multiplicative noise in the stiffness, the situation is more complicated and in fact the energy transition is reentrant for intermediate noise strength and damping. For multiplicative noise in the mass, the results depend on the implementation of the noise. One can take the velocity or the momentum to be conserved as the mass is changed. In these cases increasing the mass destabilizes the system. Alternatively, if the change in mass is caused by the accretion and loss of particles to the Brownian particle, these processes are asymmetric with momentum conserved upon accretion and velocity upon loss. In this case, there is no instability, as opposed to the other two implementations. We also present the mass dependence of the instability threshold for the first moment. Finally, we study the distribution of the energy, finding a power-law cutoff at a value that increases with time.

  17. Use of multiple regression models to evaluate the formation of halonitromethane via chlorination/chloramination of water from Tai Lake and the Qiantang River, China.

    PubMed

    Hong, Huachang; Qian, Lingya; Xiong, Yujing; Xiao, Zhuoqun; Lin, Hongjun; Yu, Haiying

    2015-01-01

    The deterioration of water quality, especially organic pollution in Tai Lake and the Qiantang River, have recently received attention in China. The objectives of this study were to evaluate the formation of halonitromethanes (HNMs) using multiple regression models for chlorination and chloramination and to identify the key factors that influence the formation of HNMs in Tai Lake and the Qiantang River. The results showed that the total formation of HNMs (T-HNMs) during chlorination and chloramination could be described using the following models: (1) [Formula: see text] =(10)(5.267)(DON)(6.645)(Br(-))(0.737)(DOC)(-)(5.537)(Cl2)(0.333)(t)(0.165) (R(2)=0.974, p<0.01, n=33), and (2) T-HNMNH2Cl=(10)(-)(2.481)(Cl2)(0.451)(NO2(-))(0.382)(Br(-))(0.630)(t)(0.640)(Temp)(0.581) (R(2)=0.961, p<0.05, n=33), respectively. The key factors that influenced the T-HNM yields during chlorination were dissolved organic nitrogen (DON), bromide and dissolved organic carbon (DOC). The nitrite and bromide concentrations and the reaction time mainly affected the T-HNM yields during chloramination. Additional analysis indicated that the bromine incorporation factors (BIFs) for trihalogenated HNMs generally decreased as the chlorine/chloramine dose, temperature and reaction time decreased and increased as the bromide concentration increased.

  18. Partial Least Squares Regression Can Aid in Detecting Differential Abundance of Multiple Features in Sets of Metagenomic Samples.

    PubMed

    Libiger, Ondrej; Schork, Nicholas J

    2015-01-01

    It is now feasible to examine the composition and diversity of microbial communities (i.e., "microbiomes") that populate different human organs and orifices using DNA sequencing and related technologies. To explore the potential links between changes in microbial communities and various diseases in the human body, it is essential to test associations involving different species within and across microbiomes, environmental settings and disease states. Although a number of statistical techniques exist for carrying out relevant analyses, it is unclear which of these techniques exhibit the greatest statistical power to detect associations given the complexity of most microbiome datasets. We compared the statistical power of principal component regression, partial least squares regression, regularized regression, distance-based regression, Hill's diversity measures, and a modified test implemented in the popular and widely used microbiome analysis methodology "Metastats" across a wide range of simulated scenarios involving changes in feature abundance between two sets of metagenomic samples. For this purpose, simulation studies were used to change the abundance of microbial species in a real dataset from a published study examining human hands. Each technique was applied to the same data, and its ability to detect the simulated change in abundance was assessed. We hypothesized that a small subset of methods would outperform the rest in terms of the statistical power. Indeed, we found that the Metastats technique modified to accommodate multivariate analysis and partial least squares regression yielded high power under the models and data sets we studied. The statistical power of diversity measure-based tests, distance-based regression and regularized regression was significantly lower. Our results provide insight into powerful analysis strategies that utilize information on species counts from large microbiome data sets exhibiting skewed frequency distributions obtained

  19. Ridge Regression: A Regression Procedure for Analyzing correlated Independent Variables

    ERIC Educational Resources Information Center

    Rakow, Ernest A.

    1978-01-01

    Ridge regression is a technique used to ameliorate the problem of highly correlated independent variables in multiple regression analysis. This paper explains the fundamentals of ridge regression and illustrates its use. (JKS)

  20. 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.

  1. Guide to using Multiple Regression in Excel (MRCX v.1.1) for Removal of River Stage Effects from Well Water Levels

    SciTech Connect

    Mackley, Rob D.; Spane, Frank A.; Pulsipher, Trenton C.; Allwardt, Craig H.

    2010-09-01

    A software tool was created in Fiscal Year 2010 (FY11) that enables multiple-regression correction of well water levels for river-stage effects. This task was conducted as part of the Remediation Science and Technology project of CH2MHILL Plateau Remediation Company (CHPRC). This document contains an overview of the correction methodology and a user’s manual for Multiple Regression in Excel (MRCX) v.1.1. It also contains a step-by-step tutorial that shows users how to use MRCX to correct river effects in two different wells. This report is accompanied by an enclosed CD that contains the MRCX installer application and files used in the tutorial exercises.

  2. The concurrent multiplicative-additive approach for gauge-radar/satellite multisensor precipitation estimates

    NASA Astrophysics Data System (ADS)

    Garcia-Pintado, J.; Barberá, G. G.; Erena Arrabal, M.; Castillo, V. M.

    2010-12-01

    Objective analysis schemes (OAS), also called ``succesive correction methods'' or ``observation nudging'', have been proposed for multisensor precipitation estimation combining remote sensing data (meteorological radar or satellite) with data from ground-based raingauge networks. However, opposite to the more complex geostatistical approaches, the OAS techniques for this use are not optimized. On the other hand, geostatistical techniques ideally require, at the least, modelling the covariance from the rain gauge data at every time step evaluated, which commonly cannot be soundly done. Here, we propose a new procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) for operational rainfall estimation using rain gauges and meteorological radar, which does not require explicit modelling of spatial covariances. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on the OAS, whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The approach considers radar estimates as background a priori information (first guess), so that nudging to observations (gauges) may be relaxed smoothly to the first guess, and the relaxation shape is obtained from the sequential

  3. Quantitative structure-property relationship (QSPR) for the adsorption of organic compounds onto activated carbon cloth: Comparison between multiple linear regression and neural network

    SciTech Connect

    Brasquet, C.; Bourges, B.; Le Cloirec, P.

    1999-12-01

    The adsorption of 55 organic compounds is carried out onto a recently discovered adsorbent, activated carbon cloth. Isotherms are modeled using the Freundlich classical model, and the large database generated allows qualitative assumptions about the adsorption mechanism. However, to confirm these assumptions, a quantitative structure-property relationship methodology is used to assess the correlations between an adsorbability parameter (expressed using the Freundlich parameter K) and topological indices related to the compounds molecular structure (molecular connectivity indices, MCI). This correlation is set up by mean of two different statistical tools, multiple linear regression (MLR) and neural network (NN). A principal component analysis is carried out to generate new and uncorrelated variables. It enables the relations between the MCI to be analyzed, but the multiple linear regression assessed using the principal components (PCs) has a poor statistical quality and introduces high order PCs, too inaccurate for an explanation of the adsorption mechanism. The correlations are thus set up using the original variables (MCI), and both statistical tools, multiple linear regression and neutral network, are compared from a descriptive and predictive point of view. To compare the predictive ability of both methods, a test database of 10 organic compounds is used.

  4. Comparison of multiple linear regression, partial least squares and artificial neural networks for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids.

    PubMed

    Fragkaki, A G; Farmaki, E; Thomaidis, N; Tsantili-Kakoulidou, A; Angelis, Y S; Koupparis, M; Georgakopoulos, C

    2012-09-21

    The comparison among different modelling techniques, such as multiple linear regression, partial least squares and artificial neural networks, has been performed in order to construct and evaluate models for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids. The performance of the quantitative structure-retention relationship study, using the multiple linear regression and partial least squares techniques, has been previously conducted. In the present study, artificial neural networks models were constructed and used for the prediction of relative retention times of anabolic androgenic steroids, while their efficiency is compared with that of the models derived from the multiple linear regression and partial least squares techniques. For overall ranking of the models, a novel procedure [Trends Anal. Chem. 29 (2010) 101-109] based on sum of ranking differences was applied, which permits the best model to be selected. The suggested models are considered useful for the estimation of relative retention times of designer steroids for which no analytical data are available.

  5. Sufficient conditions for the additivity of stall forces generated by multiple filaments or motors

    NASA Astrophysics Data System (ADS)

    Bameta, Tripti; Das, Dipjyoti; Das, Dibyendu; Padinhateeri, Ranjith; Inamdar, Mandar M.

    2017-02-01

    Molecular motors and cytoskeletal filaments work collectively most of the time under opposing forces. This opposing force may be due to cargo carried by motors or resistance coming from the cell membrane pressing against the cytoskeletal filaments. Some recent studies have shown that the collective maximum force (stall force) generated by multiple cytoskeletal filaments or molecular motors may not always be just a simple sum of the stall forces of the individual filaments or motors. To understand this excess or deficit in the collective force, we study a broad class of models of both cytoskeletal filaments and molecular motors. We argue that the stall force generated by a group of filaments or motors is additive, that is, the stall force of N number of filaments (motors) is N times the stall force of one filament (motor), when the system is reversible at stall. Conversely, we show that this additive property typically does not hold true when the system is irreversible at stall. We thus present a novel and unified understanding of the existing models exhibiting such non-addivity, and generalise our arguments by developing new models that demonstrate this phenomena. We also propose a quantity similar to thermodynamic efficiency to easily predict this deviation from stall-force additivity for filament and motor collectives.

  6. Gene-based multiple regression association testing for combined examination of common and low frequency variants in quantitative trait analysis.

    PubMed

    Yoo, Yun Joo; Sun, Lei; Bull, Shelley B

    2013-01-01

    Multi-marker methods for genetic association analysis can be performed for common and low frequency SNPs to improve power. Regression models are an intuitive way to formulate multi-marker tests. In previous studies we evaluated regression-based multi-marker tests for common SNPs, and through identification of bins consisting of correlated SNPs, developed a multi-bin linear combination (MLC) test that is a compromise between a 1 df linear combination test and a multi-df global test. Bins of SNPs in high linkage disequilibrium (LD) are identified, and a linear combination of individual SNP statistics is constructed within each bin. Then association with the phenotype is represented by an overall statistic with df as many or few as the number of bins. In this report we evaluate multi-marker tests for SNPs that occur at low frequencies. There are many linear and quadratic multi-marker tests that are suitable for common or low frequency variant analysis. We compared the performance of the MLC tests with various linear and quadratic statistics in joint or marginal regressions. For these comparisons, we performed a simulation study of genotypes and quantitative traits for 85 genes with many low frequency SNPs based on HapMap Phase III. We compared the tests using (1) set of all SNPs in a gene, (2) set of common SNPs in a gene (MAF ≥ 5%), (3) set of low frequency SNPs (1% ≤ MAF < 5%). For different trait models based on low frequency causal SNPs, we found that combined analysis using all SNPs including common and low frequency SNPs is a good and robust choice whereas using common SNPs alone or low frequency SNP alone can lose power. MLC tests performed well in combined analysis except where two low frequency causal SNPs with opposing effects are positively correlated. Overall, across different sets of analysis, the joint regression Wald test showed consistently good performance whereas other statistics including the ones based on marginal regression had lower power for

  7. Investigating the quantitative structure-activity relationships for antibody recognition of two immunoassays for polycyclic aromatic hydrocarbons by multiple regression methods.

    PubMed

    Zhang, Yan-Feng; Zhang, Li; Gao, Zhi-Xian; Dai, Shu-Gui

    2012-01-01

    Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous contaminants found in the environment. Immunoassays represent useful analytical methods to complement traditional analytical procedures for PAHs. Cross-reactivity (CR) is a very useful character to evaluate the extent of cross-reaction of a cross-reactant in immunoreactions and immunoassays. The quantitative relationships between the molecular properties and the CR of PAHs were established by stepwise multiple linear regression, principal component regression and partial least square regression, using the data of two commercial enzyme-linked immunosorbent assay (ELISA) kits. The objective is to find the most important molecular properties that affect the CR, and predict the CR by multiple regression methods. The results show that the physicochemical, electronic and topological properties of the PAH molecules have an integrated effect on the CR properties for the two ELISAs, among which molar solubility (S(m)) and valence molecular connectivity index ((3)χ(v)) are the most important factors. The obtained regression equations for Ris(C) kit are all statistically significant (p < 0.005) and show satisfactory ability for predicting CR values, while equations for RaPID kit are all not significant (p > 0.05) and not suitable for predicting. It is probably because that the Ris(C) immunoassay employs a monoclonal antibody, while the RaPID kit is based on polyclonal antibody. Considering the important effect of solubility on the CR values, cross-reaction potential (CRP) is calculated and used as a complement of CR for evaluation of cross-reactions in immunoassays. Only the compounds with both high CR and high CRP can cause intense cross-reactions in immunoassays.

  8. Is the structural diversity of tripeptides sufficient for developing functional food additives with satisfactory multiple bioactivities?

    NASA Astrophysics Data System (ADS)

    Wang, Jian-Hui; Liu, Yong-Le; Ning, Jing-Heng; Yu, Jian; Li, Xiang-Hong; Wang, Fa-Xiang

    2013-05-01

    Multifunctional peptides have attracted increasing attention in the food science community because of their therapeutic potential, low toxicity and rapid intestinal absorption. However, previous study demonstrated that the limited structural variations make it difficult to optimize dipeptide molecules in a good balance between desirable and undesirable properties (F. Tian, P. Zhou, F. Lv, R. Song, Z. Li, J. Pept. Sci. 13 (2007) 549-566). In the present work, we attempt to answer whether the structural diversity is sufficient for a tripeptide to have satisfactory multiple bioactivities. Statistical test, structural examination and energetic analysis confirm that peptides of three amino acids long can bind tightly to human angiotensin converting enzyme (ACE) and thus exert significant antihypertensive efficacy. Further quantitative structure-activity relationship (QSAR) modeling and prediction of all 8000 possible tripeptides reveal that their ACE-inhibitory potency exhibits a good (positive) relationship to antioxidative activity, but has only a quite modest correlation with bitterness. This means that it is possible to find certain tripeptide entities possessing the optimal combination of strong ACE-inhibitory potency, high antioxidative activity and weak bitter taste, which are the promising candidates for developing multifunctional food additives with satisfactory multiple bioactivities. The marked difference between dipeptide and tripeptide can be attributed to the fact that the structural diversity of peptides increases dramatically with a slight change in sequence length.

  9. Generalized Additive Mixed-Models for Pharmacology Using Integrated Discrete Multiple Organ Co-Culture.

    PubMed

    Ingersoll, Thomas; Cole, Stephanie; Madren-Whalley, Janna; Booker, Lamont; Dorsey, Russell; Li, Albert; Salem, Harry

    2016-01-01

    Integrated Discrete Multiple Organ Co-culture (IDMOC) is emerging as an in-vitro alternative to in-vivo animal models for pharmacology studies. IDMOC allows dose-response relationships to be investigated at the tissue and organoid levels, yet, these relationships often exhibit responses that are far more complex than the binary responses often measured in whole animals. To accommodate departure from binary endpoints, IDMOC requires an expansion of analytic techniques beyond simple linear probit and logistic models familiar in toxicology. IDMOC dose-responses may be measured at continuous scales, exhibit significant non-linearity such as local maxima or minima, and may include non-independent measures. Generalized additive mixed-modeling (GAMM) provides an alternative description of dose-response that relaxes assumptions of independence and linearity. We compared GAMMs to traditional linear models for describing dose-response in IDMOC pharmacology studies.

  10. Generalized Additive Mixed-Models for Pharmacology Using Integrated Discrete Multiple Organ Co-Culture

    PubMed Central

    Ingersoll, Thomas; Cole, Stephanie; Madren-Whalley, Janna; Booker, Lamont; Dorsey, Russell; Li, Albert; Salem, Harry

    2016-01-01

    Integrated Discrete Multiple Organ Co-culture (IDMOC) is emerging as an in-vitro alternative to in-vivo animal models for pharmacology studies. IDMOC allows dose-response relationships to be investigated at the tissue and organoid levels, yet, these relationships often exhibit responses that are far more complex than the binary responses often measured in whole animals. To accommodate departure from binary endpoints, IDMOC requires an expansion of analytic techniques beyond simple linear probit and logistic models familiar in toxicology. IDMOC dose-responses may be measured at continuous scales, exhibit significant non-linearity such as local maxima or minima, and may include non-independent measures. Generalized additive mixed-modeling (GAMM) provides an alternative description of dose-response that relaxes assumptions of independence and linearity. We compared GAMMs to traditional linear models for describing dose-response in IDMOC pharmacology studies. PMID:27110941

  11. Stochastic responses of a viscoelastic-impact system under additive and multiplicative random excitations

    NASA Astrophysics Data System (ADS)

    Zhao, Xiangrong; Xu, Wei; Yang, Yongge; Wang, Xiying

    2016-06-01

    This paper deals with the stochastic responses of a viscoelastic-impact system under additive and multiplicative random excitations. The viscoelastic force is replaced by a combination of stiffness and damping terms. The non-smooth transformation of the state variables is utilized to transform the original system to a new system without the impact term. The stochastic averaging method is applied to yield the stationary probability density functions. The validity of the analytical method is verified by comparing the analytical results with the numerical results. It is invaluable to note that the restitution coefficient, the viscoelastic parameters and the damping coefficients can induce the occurrence of stochastic P-bifurcation. Furthermore, the joint stationary probability density functions with three peaks are explored.

  12. A new approach to handle additive and multiplicative uncertainties in the measurement for ? LPV filtering

    NASA Astrophysics Data System (ADS)

    Lacerda, Márcio J.; Tognetti, Eduardo S.; Oliveira, Ricardo C. L. F.; Peres, Pedro L. D.

    2016-04-01

    This paper presents a general framework to cope with full-order ? linear parameter-varying (LPV) filter design subject to inexactly measured parameters. The main novelty is the ability of handling additive and multiplicative uncertainties in the measurements, for both continuous and discrete-time LPV systems, in a unified approach. By conveniently modelling scheduling parameters and uncertainties affecting the measurements, the ? filter design problem can be expressed in terms of robust matrix inequalities that become linear when two scalar parameters are fixed. Therefore, the proposed conditions can be efficiently solved through linear matrix inequality relaxations based on polynomial solutions. Numerical examples are presented to illustrate the improved efficiency of the proposed approach when compared to other methods and, more important, its capability to deal with scenarios where the available strategies in the literature cannot be used.

  13. Comparison of two-concentration with multi-concentration linear regressions: Retrospective data analysis of multiple regulated LC-MS bioanalytical projects.

    PubMed

    Musuku, Adrien; Tan, Aimin; Awaiye, Kayode; Trabelsi, Fethi

    2013-09-01

    Linear calibration is usually performed using eight to ten calibration concentration levels in regulated LC-MS bioanalysis because a minimum of six are specified in regulatory guidelines. However, we have previously reported that two-concentration linear calibration is as reliable as or even better than using multiple concentrations. The purpose of this research is to compare two-concentration with multiple-concentration linear calibration through retrospective data analysis of multiple bioanalytical projects that were conducted in an independent regulated bioanalytical laboratory. A total of 12 bioanalytical projects were randomly selected: two validations and two studies for each of the three most commonly used types of sample extraction methods (protein precipitation, liquid-liquid extraction, solid-phase extraction). When the existing data were retrospectively linearly regressed using only the lowest and the highest concentration levels, no extra batch failure/QC rejection was observed and the differences in accuracy and precision between the original multi-concentration regression and the new two-concentration linear regression are negligible. Specifically, the differences in overall mean apparent bias (square root of mean individual bias squares) are within the ranges of -0.3% to 0.7% and 0.1-0.7% for the validations and studies, respectively. The differences in mean QC concentrations are within the ranges of -0.6% to 1.8% and -0.8% to 2.5% for the validations and studies, respectively. The differences in %CV are within the ranges of -0.7% to 0.9% and -0.3% to 0.6% for the validations and studies, respectively. The average differences in study sample concentrations are within the range of -0.8% to 2.3%. With two-concentration linear regression, an average of 13% of time and cost could have been saved for each batch together with 53% of saving in the lead-in for each project (the preparation of working standard solutions, spiking, and aliquoting). Furthermore

  14. Quantitative structure-property relationship modeling of water-to-wet butyl acetate partition coefficient of 76 organic solutes using multiple linear regression and artificial neural network.

    PubMed

    Dashtbozorgi, Zahra; Golmohammadi, Hassan

    2010-12-01

    The main aim of this study was the development of a quantitative structure-property relationship method using an artificial neural network (ANN) for predicting the water-to-wet butyl acetate partition coefficients of organic solutes. As a first step, a genetic algorithm-multiple linear regression model was developed; the descriptors appearing in this model were considered as inputs for the ANN. These descriptors are principal moment of inertia C (I(C)), area-weighted surface charge of hydrogen-bonding donor atoms (HACA-2), Kier and Hall index (order 2) ((2)χ), Balaban index (J), minimum bond order of a C atom (P(C)) and relative negative-charged SA (RNCS). Then a 6-4-1 neural network was generated for the prediction of water-to-wet butyl acetate partition coefficients of 76 organic solutes. By comparing the results obtained from multiple linear regression and ANN models, it can be seen that statistical parameters (Fisher ratio, correlation coefficient and standard error) of the ANN model are better than that regression model, which indicates that nonlinear model can simulate the relationship between the structural descriptors and the partition coefficients of the investigated molecules more accurately.

  15. Use of principal-component, correlation, and stepwise multiple-regression analyses to investigate selected physical and hydraulic properties of carbonate-rock aquifers

    USGS Publications Warehouse

    Brown, C. Erwin

    1993-01-01

    Correlation analysis in conjunction with principal-component and multiple-regression analyses were applied to laboratory chemical and petrographic data to assess the usefulness of these techniques in evaluating selected physical and hydraulic properties of carbonate-rock aquifers in central Pennsylvania. Correlation and principal-component analyses were used to establish relations and associations among variables, to determine dimensions of property variation of samples, and to filter the variables containing similar information. Principal-component and correlation analyses showed that porosity is related to other measured variables and that permeability is most related to porosity and grain size. Four principal components are found to be significant in explaining the variance of data. Stepwise multiple-regression analysis was used to see how well the measured variables could predict porosity and (or) permeability for this suite of rocks. The variation in permeability and porosity is not totally predicted by the other variables, but the regression is significant at the 5% significance level. ?? 1993.

  16. Application of least squares support vector regression and linear multiple regression for modeling removal of methyl orange onto tin oxide nanoparticles loaded on activated carbon and activated carbon prepared from Pistacia atlantica wood.

    PubMed

    Ghaedi, M; Rahimi, Mahmoud Reza; Ghaedi, A M; Tyagi, Inderjeet; Agarwal, Shilpi; Gupta, Vinod Kumar

    2016-01-01

    Two novel and eco friendly adsorbents namely tin oxide nanoparticles loaded on activated carbon (SnO2-NP-AC) and activated carbon prepared from wood tree Pistacia atlantica (AC-PAW) were used for the rapid removal and fast adsorption of methyl orange (MO) from the aqueous phase. The dependency of MO removal with various adsorption influential parameters was well modeled and optimized using multiple linear regressions (MLR) and least squares support vector regression (LSSVR). The optimal parameters for the LSSVR model were found based on γ value of 0.76 and σ(2) of 0.15. For testing the data set, the mean square error (MSE) values of 0.0010 and the coefficient of determination (R(2)) values of 0.976 were obtained for LSSVR model, and the MSE value of 0.0037 and the R(2) value of 0.897 were obtained for the MLR model. The adsorption equilibrium and kinetic data was found to be well fitted and in good agreement with Langmuir isotherm model and second-order equation and intra-particle diffusion models respectively. The small amount of the proposed SnO2-NP-AC and AC-PAW (0.015 g and 0.08 g) is applicable for successful rapid removal of methyl orange (>95%). The maximum adsorption capacity for SnO2-NP-AC and AC-PAW was 250 mg g(-1) and 125 mg g(-1) respectively.

  17. Improved spatial regression analysis of diffusion tensor imaging for lesion detection during longitudinal progression of multiple sclerosis in individual subjects.

    PubMed

    Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui

    2016-03-21

    Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.

  18. Improved spatial regression analysis of diffusion tensor imaging for lesion detection during longitudinal progression of multiple sclerosis in individual subjects

    NASA Astrophysics Data System (ADS)

    Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui

    2016-03-01

    Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.

  19. Stochastic resonance subject to multiplicative and additive noise: The influence of potential asymmetries

    NASA Astrophysics Data System (ADS)

    Qiao, Zijian; Lei, Yaguo; Lin, Jing; Niu, Shantao

    2016-11-01

    The influence of potential asymmetries on stochastic resonance (SR) subject to both multiplicative and additive noise is studied by using two-state theory, where three types of asymmetries are introduced in double-well potential by varying the depth, the width, and both the depth and the width of the left well alone. The characteristics of SR in the asymmetric cases are different from symmetric ones, where asymmetry has a strong influence on output signal-to-noise ratio (SNR) and optimal noise intensity. Even optimal noise intensity is also associated with the steepness of the potential-barrier wall, which is generally ignored. Moreover, the largest SNR in asymmetric SR is found to be relatively larger than the symmetric one, which also closely depends on noise intensity ratio. In addition, a moderate cross-correlation intensity between two noises is good for improving the output SNR. More interestingly, a double SR phenomenon is observed in certain cases for two correlated noises, whereas it disappears for two independent noises. The above clues are helpful in achieving weak signal detection under heavy background noise.

  20. Multiple Linkage Disequilibrium Mapping Methods to Validate Additive Quantitative Trait Loci in Korean Native Cattle (Hanwoo)

    PubMed Central

    Li, Yi; Kim, Jong-Joo

    2015-01-01

    The efficiency of genome-wide association analysis (GWAS) depends on power of detection for quantitative trait loci (QTL) and precision for QTL mapping. In this study, three different strategies for GWAS were applied to detect QTL for carcass quality traits in the Korean cattle, Hanwoo; a linkage disequilibrium single locus regression method (LDRM), a combined linkage and linkage disequilibrium analysis (LDLA) and a BayesCπ approach. The phenotypes of 486 steers were collected for weaning weight (WWT), yearling weight (YWT), carcass weight (CWT), backfat thickness (BFT), longissimus dorsi muscle area, and marbling score (Marb). Also the genotype data for the steers and their sires were scored with the Illumina bovine 50K single nucleotide polymorphism (SNP) chips. For the two former GWAS methods, threshold values were set at false discovery rate <0.01 on a chromosome-wide level, while a cut-off threshold value was set in the latter model, such that the top five windows, each of which comprised 10 adjacent SNPs, were chosen with significant variation for the phenotype. Four major additive QTL from these three methods had high concordance found in 64.1 to 64.9Mb for Bos taurus autosome (BTA) 7 for WWT, 24.3 to 25.4Mb for BTA14 for CWT, 0.5 to 1.5Mb for BTA6 for BFT and 26.3 to 33.4Mb for BTA29 for BFT. Several candidate genes (i.e. glutamate receptor, ionotropic, ampa 1 [GRIA1], family with sequence similarity 110, member B [FAM110B], and thymocyte selection-associated high mobility group box [TOX]) may be identified close to these QTL. Our result suggests that the use of different linkage disequilibrium mapping approaches can provide more reliable chromosome regions to further pinpoint DNA makers or causative genes in these regions. PMID:26104396

  1. Multiple Linkage Disequilibrium Mapping Methods to Validate Additive Quantitative Trait Loci in Korean Native Cattle (Hanwoo).

    PubMed

    Li, Yi; Kim, Jong-Joo

    2015-07-01

    The efficiency of genome-wide association analysis (GWAS) depends on power of detection for quantitative trait loci (QTL) and precision for QTL mapping. In this study, three different strategies for GWAS were applied to detect QTL for carcass quality traits in the Korean cattle, Hanwoo; a linkage disequilibrium single locus regression method (LDRM), a combined linkage and linkage disequilibrium analysis (LDLA) and a BayesCπ approach. The phenotypes of 486 steers were collected for weaning weight (WWT), yearling weight (YWT), carcass weight (CWT), backfat thickness (BFT), longissimus dorsi muscle area, and marbling score (Marb). Also the genotype data for the steers and their sires were scored with the Illumina bovine 50K single nucleotide polymorphism (SNP) chips. For the two former GWAS methods, threshold values were set at false discovery rate <0.01 on a chromosome-wide level, while a cut-off threshold value was set in the latter model, such that the top five windows, each of which comprised 10 adjacent SNPs, were chosen with significant variation for the phenotype. Four major additive QTL from these three methods had high concordance found in 64.1 to 64.9Mb for Bos taurus autosome (BTA) 7 for WWT, 24.3 to 25.4Mb for BTA14 for CWT, 0.5 to 1.5Mb for BTA6 for BFT and 26.3 to 33.4Mb for BTA29 for BFT. Several candidate genes (i.e. glutamate receptor, ionotropic, ampa 1 [GRIA1], family with sequence similarity 110, member B [FAM110B], and thymocyte selection-associated high mobility group box [TOX]) may be identified close to these QTL. Our result suggests that the use of different linkage disequilibrium mapping approaches can provide more reliable chromosome regions to further pinpoint DNA makers or causative genes in these regions.

  2. Arithmetic on Your Phone: A Large Scale Investigation of Simple Additions and Multiplications

    PubMed Central

    Zimmerman, Federico; Shalom, Diego; Gonzalez, Pablo A.; Garrido, Juan Manuel; Alvarez Heduan, Facundo; Dehaene, Stanislas; Sigman, Mariano; Rieznik, Andres

    2016-01-01

    We present the results of a gamified mobile device arithmetic application which allowed us to collect vast amount of data in simple arithmetic operations. Our results confirm and replicate, on a large sample, six of the main principles derived in a long tradition of investigation: size effect, tie effect, size-tie interaction effect, five-effect, RTs and error rates correlation effect, and most common error effect. Our dataset allowed us to perform a robust analysis of order effects for each individual problem, for which there is controversy both in experimental findings and in the predictions of theoretical models. For addition problems, the order effect was dominated by a max-then-min structure (i.e 7+4 is easier than 4+7). This result is predicted by models in which additions are performed as a translation starting from the first addend, with a distance given by the second addend. In multiplication, we observed a dominance of two effects: (1) a max-then-min pattern that can be accounted by the fact that it is easier to perform fewer additions of the largest number (i.e. 8x3 is easier to compute as 8+8+8 than as 3+3+…+3) and (2) a phonological effect by which problems for which there is a rhyme (i.e. "seis por cuatro es veinticuatro") are performed faster. Above and beyond these results, our study bares an important practical conclusion, as proof of concept, that participants can be motivated to perform substantial arithmetic training simply by presenting it in a gamified format. PMID:28033357

  3. Arithmetic on Your Phone: A Large Scale Investigation of Simple Additions and Multiplications.

    PubMed

    Zimmerman, Federico; Shalom, Diego; Gonzalez, Pablo A; Garrido, Juan Manuel; Alvarez Heduan, Facundo; Dehaene, Stanislas; Sigman, Mariano; Rieznik, Andres

    2016-01-01

    We present the results of a gamified mobile device arithmetic application which allowed us to collect vast amount of data in simple arithmetic operations. Our results confirm and replicate, on a large sample, six of the main principles derived in a long tradition of investigation: size effect, tie effect, size-tie interaction effect, five-effect, RTs and error rates correlation effect, and most common error effect. Our dataset allowed us to perform a robust analysis of order effects for each individual problem, for which there is controversy both in experimental findings and in the predictions of theoretical models. For addition problems, the order effect was dominated by a max-then-min structure (i.e 7+4 is easier than 4+7). This result is predicted by models in which additions are performed as a translation starting from the first addend, with a distance given by the second addend. In multiplication, we observed a dominance of two effects: (1) a max-then-min pattern that can be accounted by the fact that it is easier to perform fewer additions of the largest number (i.e. 8x3 is easier to compute as 8+8+8 than as 3+3+…+3) and (2) a phonological effect by which problems for which there is a rhyme (i.e. "seis por cuatro es veinticuatro") are performed faster. Above and beyond these results, our study bares an important practical conclusion, as proof of concept, that participants can be motivated to perform substantial arithmetic training simply by presenting it in a gamified format.

  4. Melanin and blood concentration in a human skin model studied by multiple regression analysis: assessment by Monte Carlo simulation.

    PubMed

    Shimada, M; Yamada, Y; Itoh, M; Yatagai, T

    2001-09-01

    Measurement of melanin and blood concentration in human skin is needed in the medical and the cosmetic fields because human skin colour is mainly determined by the colours of melanin and blood. It is difficult to measure these concentrations in human skin because skin has a multi-layered structure and scatters light strongly throughout the visible spectrum. The Monte Carlo simulation currently used for the analysis of skin colour requires long calculation times and knowledge of the specific optical properties of each skin layer. A regression analysis based on the modified Beer-Lambert law is presented as a method of measuring melanin and blood concentration in human skin in a shorter period of time and with fewer calculations. The accuracy of this method is assessed using Monte Carlo simulations.

  5. Melanin and blood concentration in a human skin model studied by multiple regression analysis: assessment by Monte Carlo simulation

    NASA Astrophysics Data System (ADS)

    Shimada, M.; Yamada, Y.; Itoh, M.; Yatagai, T.

    2001-09-01

    Measurement of melanin and blood concentration in human skin is needed in the medical and the cosmetic fields because human skin colour is mainly determined by the colours of melanin and blood. It is difficult to measure these concentrations in human skin because skin has a multi-layered structure and scatters light strongly throughout the visible spectrum. The Monte Carlo simulation currently used for the analysis of skin colour requires long calculation times and knowledge of the specific optical properties of each skin layer. A regression analysis based on the modified Beer-Lambert law is presented as a method of measuring melanin and blood concentration in human skin in a shorter period of time and with fewer calculations. The accuracy of this method is assessed using Monte Carlo simulations.

  6. Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression

    PubMed Central

    2015-01-01

    The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost. PMID:26574437

  7. Study relationship between inorganic and organic coal analysis with gross calorific value by multiple regression and ANFIS

    USGS Publications Warehouse

    Chelgani, S.C.; Hart, B.; Grady, W.C.; Hower, J.C.

    2011-01-01

    The relationship between maceral content plus mineral matter and gross calorific value (GCV) for a wide range of West Virginia coal samples (from 6518 to 15330 BTU/lb; 15.16 to 35.66MJ/kg) has been investigated by multivariable regression and adaptive neuro-fuzzy inference system (ANFIS). The stepwise least square mathematical method comparison between liptinite, vitrinite, plus mineral matter as input data sets with measured GCV reported a nonlinear correlation coefficient (R2) of 0.83. Using the same data set the correlation between the predicted GCV from the ANFIS model and the actual GCV reported a R2 value of 0.96. It was determined that the GCV-based prediction methods, as used in this article, can provide a reasonable estimation of GCV. Copyright ?? Taylor & Francis Group, LLC.

  8. Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression.

    PubMed

    Meng, Yilin; Roux, Benoît

    2015-08-11

    The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost.

  9. Extension of the Peters–Belson method to estimate health disparities among multiple groups using logistic regression with survey data

    PubMed Central

    Li, Y.; Graubard, B. I.; Huang, P.; Gastwirth, J. L.

    2015-01-01

    Determining the extent of a disparity, if any, between groups of people, for example, race or gender, is of interest in many fields, including public health for medical treatment and prevention of disease. An observed difference in the mean outcome between an advantaged group (AG) and disadvantaged group (DG) can be due to differences in the distribution of relevant covariates. The Peters–Belson (PB) method fits a regression model with covariates to the AG to predict, for each DG member, their outcome measure as if they had been from the AG. The difference between the mean predicted and the mean observed outcomes of DG members is the (unexplained) disparity of interest. We focus on applying the PB method to estimate the disparity based on binary/multinomial/proportional odds logistic regression models using data collected from complex surveys with more than one DG. Estimators of the unexplained disparity, an analytic variance–covariance estimator that is based on the Taylor linearization variance–covariance estimation method, as well as a Wald test for testing a joint null hypothesis of zero for unexplained disparities between two or more minority groups and a majority group, are provided. Simulation studies with data selected from simple random sampling and cluster sampling, as well as the analyses of disparity in body mass index in the National Health and Nutrition Examination Survey 1999–2004, are conducted. Empirical results indicate that the Taylor linearization variance–covariance estimation is accurate and that the proposed Wald test maintains the nominal level. PMID:25382235

  10. Automated microbial metabolism laboratory. [design of advanced labeled release experiment based on single addition of soil and multiple sequential additions of media into test chambers

    NASA Technical Reports Server (NTRS)

    1974-01-01

    The design and rationale of an advanced labeled release experiment based on single addition of soil and multiple sequential additions of media into each of four test chambers are outlined. The feasibility for multiple addition tests was established and various details of the methodology were studied. The four chamber battery of tests include: (1) determination of the effect of various atmospheric gases and selection of that gas which produces an optimum response; (2) determination of the effect of incubation temperature and selection of the optimum temperature for performing Martian biochemical tests; (3) sterile soil is dosed with a battery of C-14 labeled substrates and subjected to experimental temperature range; and (4) determination of the possible inhibitory effects of water on Martian organisms is performed initially by dosing with 0.01 ml and 0.5 ml of medium, respectively. A series of specifically labeled substrates are then added to obtain patterns in metabolic 14CO2 (C-14)O2 evolution.

  11. Al2O3 Nanoparticle Addition to Commercial Magnesium Alloys: Multiple Beneficial Effects

    PubMed Central

    Paramsothy, Muralidharan; Chan, Jimmy; Kwok, Richard; Gupta, Manoj

    2012-01-01

    The multiple beneficial effects of Al2O3 nanoparticle addition to cast magnesium based systems (followed by extrusion) were investigated, constituting either: (a) enhanced strength; or (b) simultaneously enhanced strength and ductility of the corresponding magnesium alloys. AZ31 and ZK60A nanocomposites containing Al2O3 nanoparticle reinforcement were each fabricated using solidification processing followed by hot extrusion. Compared to monolithic AZ31 (tension levels), the corresponding nanocomposite exhibited higher yield strength (0.2% tensile yield strength (TYS)), ultimate strength (UTS), failure strain and work of fracture (WOF) (+19%, +21%, +113% and +162%, respectively). Compared to monolithic AZ31 (compression levels), the corresponding nanocomposite exhibited higher yield strength (0.2% compressive yield strength (CYS)) and ultimate strength (UCS), lower failure strain and higher WOF (+5%, +5%, −4% and +11%, respectively). Compared to monolithic ZK60A (tension levels), the corresponding nanocomposite exhibited lower 0.2% TYS and higher UTS, failure strain and WOF (−4%, +13%, +170% and +200%, respectively). Compared to monolithic ZK60A (compression levels), the corresponding nanocomposite exhibited lower 0.2% CYS and higher UCS, failure strain and WOF (−10%, +7%, +15% and +26%, respectively). The capability of Al2O3 nanoparticles to enhance the properties of cast magnesium alloys in a way never seen before with micron length scale reinforcements is clearly demonstrated.

  12. Measuring decision weights in recognition experiments with multiple response alternatives: comparing the correlation and multinomial-logistic-regression methods.

    PubMed

    Dai, Huanping; Micheyl, Christophe

    2012-11-01

    Psychophysical "reverse-correlation" methods allow researchers to gain insight into the perceptual representations and decision weighting strategies of individual subjects in perceptual tasks. Although these methods have gained momentum, until recently their development was limited to experiments involving only two response categories. Recently, two approaches for estimating decision weights in m-alternative experiments have been put forward. One approach extends the two-category correlation method to m > 2 alternatives; the second uses multinomial logistic regression (MLR). In this article, the relative merits of the two methods are discussed, and the issues of convergence and statistical efficiency of the methods are evaluated quantitatively using Monte Carlo simulations. The results indicate that, for a range of values of the number of trials, the estimated weighting patterns are closer to their asymptotic values for the correlation method than for the MLR method. Moreover, for the MLR method, weight estimates for different stimulus components can exhibit strong correlations, making the analysis and interpretation of measured weighting patterns less straightforward than for the correlation method. These and other advantages of the correlation method, which include computational simplicity and a close relationship to other well-established psychophysical reverse-correlation methods, make it an attractive tool to uncover decision strategies in m-alternative experiments.

  13. Radiologic assessment of third molar tooth and spheno-occipital synchondrosis for age estimation: a multiple regression analysis study.

    PubMed

    Demirturk Kocasarac, Husniye; Sinanoglu, Alper; Noujeim, Marcel; Helvacioglu Yigit, Dilek; Baydemir, Canan

    2016-05-01

    For forensic age estimation, radiographic assessment of third molar mineralization is important between 14 and 21 years which coincides with the legal age in most countries. The spheno-occipital synchondrosis (SOS) is an important growth site during development, and its use for age estimation is beneficial when combined with other markers. In this study, we aimed to develop a regression model to estimate and narrow the age range based on the radiologic assessment of third molar and SOS in a Turkish subpopulation. Panoramic radiographs and cone beam CT scans of 349 subjects (182 males, 167 females) with age between 8 and 25 were evaluated. Four-stage system was used to evaluate the fusion degree of SOS, and Demirjian's eight stages of development for calcification for third molars. The Pearson correlation indicated a strong positive relationship between age and third molar calcification for both sexes (r = 0.850 for females, r = 0.839 for males, P < 0.001) and also between age and SOS fusion for females (r = 0.814), but a moderate relationship was found for males (r = 0.599), P < 0.001). Based on the results obtained, an age determination formula using these scores was established.

  14. Forecasting hourly PM(10) concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management.

    PubMed

    Paschalidou, Anastasia K; Karakitsios, Spyridon; Kleanthous, Savvas; Kassomenos, Pavlos A

    2011-02-01

    In the present work, two types of artificial neural network (NN) models using the multilayer perceptron (MLP) and the radial basis function (RBF) techniques, as well as a model based on principal component regression analysis (PCRA), are employed to forecast hourly PM(10) concentrations in four urban areas (Larnaca, Limassol, Nicosia and Paphos) in Cyprus. The model development is based on a variety of meteorological and pollutant parameters corresponding to the 2-year period between July 2006 and June 2008, and the model evaluation is achieved through the use of a series of well-established evaluation instruments and methodologies. The evaluation reveals that the MLP NN models display the best forecasting performance with R (2) values ranging between 0.65 and 0.76, whereas the RBF NNs and the PCRA models reveal a rather weak performance with R (2) values between 0.37-0.43 and 0.33-0.38, respectively. The derived MLP models are also used to forecast Saharan dust episodes with remarkable success (probability of detection ranging between 0.68 and 0.71). On the whole, the analysis shows that the models introduced here could provide local authorities with reliable and precise predictions and alarms about air quality if used on an operational basis.

  15. Alternative parameterizations of the multiple-trait random regression model for milk yield and somatic cell score via recursive links between phenotypes.

    PubMed

    Jamrozik, J; Schaeffer, L R

    2011-08-01

    Multiple-trait random regression models with recursive phenotypic link from somatic cell score (SCS) to milk yield on the same test day and with different restrictions on co-variances between these traits were fitted to the first-lactation Canadian Holstein data. Bayesian methods with Gibbs sampling were used to derive inferences about parameters for all models. Bayes factor indicated that the recursive model with uncorrelated environmental effects between traits was the most plausible specification in describing the data. Goodness of fit in terms of a within-trait weighted mean square error and correlation between observed and predicted data was the same for all parameterizations. All recursive models estimated similar negative causal effects from SCS to milk yield (up to -0.4 in 46-115 days in milk in lactation). Estimates of heritabilities, genetic and environmental correlations for the first two regression coefficients (overall level of a trait and lactation persistency) within both traits were similar among models. Genetic correlations between milk and SCS were dependent on the restrictions on genetic co-variances for these traits. Recursive model with uncorrelated system genetic effects between milk and SCS gave estimates of genetic correlations of the opposite sign compared with a regular multiple-trait model. Phenotypic recursion between milk and SCS seemed, however, to be the only source of environmental correlations between these two traits. Rankings of sires for total milk yield in lactation, average daily SCS and persistency for both traits were similar among models. Multiple-trait model with recursive links between milk and SCS and uncorrelated random environmental effects could be an attractive alternative for a regular multiple-trait model in terms of model parsimony and accuracy.

  16. Binary Logistic Regression Versus Boosted Regression Trees in Assessing Landslide Susceptibility for Multiple-Occurring Regional Landslide Events: Application to the 2009 Storm Event in Messina (Sicily, southern Italy).

    NASA Astrophysics Data System (ADS)

    Lombardo, L.; Cama, M.; Maerker, M.; Parisi, L.; Rotigliano, E.

    2014-12-01

    This study aims at comparing the performances of Binary Logistic Regression (BLR) and Boosted Regression Trees (BRT) methods in assessing landslide susceptibility for multiple-occurrence regional landslide events within the Mediterranean region. A test area was selected in the north-eastern sector of Sicily (southern Italy), corresponding to the catchments of the Briga and the Giampilieri streams both stretching for few kilometres from the Peloritan ridge (eastern Sicily, Italy) to the Ionian sea. This area was struck on the 1st October 2009 by an extreme climatic event resulting in thousands of rapid shallow landslides, mainly of debris flows and debris avalanches types involving the weathered layer of a low to high grade metamorphic bedrock. Exploiting the same set of predictors and the 2009 landslide archive, BLR- and BRT-based susceptibility models were obtained for the two catchments separately, adopting a random partition (RP) technique for validation; besides, the models trained in one of the two catchments (Briga) were tested in predicting the landslide distribution in the other (Giampilieri), adopting a spatial partition (SP) based validation procedure. All the validation procedures were based on multi-folds tests so to evaluate and compare the reliability of the fitting, the prediction skill, the coherence in the predictor selection and the precision of the susceptibility estimates. All the obtained models for the two methods produced very high predictive performances, with a general congruence between BLR and BRT in the predictor importance. In particular, the research highlighted that BRT-models reached a higher prediction performance with respect to BLR-models, for RP based modelling, whilst for the SP-based models the difference in predictive skills between the two methods dropped drastically, converging to an analogous excellent performance. However, when looking at the precision of the probability estimates, BLR demonstrated to produce more robust

  17. Continuous-variable quantum teleportation with non-Gaussian entangled states generated via multiple-photon subtraction and addition

    NASA Astrophysics Data System (ADS)

    Wang, Shuai; Hou, Li-Li; Chen, Xian-Feng; Xu, Xue-Fen

    2015-06-01

    We theoretically analyze the Einstein-Podolsky-Rosen (EPR) correlation, the quadrature squeezing, and the continuous-variable quantum teleportation when considering non-Gaussian entangled states generated by applying multiple-photon subtraction and multiple-photon addition to a two-mode squeezed vacuum state (TMSVs). Our results indicate that in the case of the multiple-photon-subtracted TMSVs with symmetric operations, the corresponding EPR correlation, the two-mode squeezing degree, the sum squeezing, and the fidelity of teleporting a coherent state or a squeezed vacuum state can be enhanced for any squeezing parameter r and these enhancements increase with the number of subtracted photons in the low-squeezing regime, while asymmetric multiple-photon subtractions will generally reduce these quantities. For the multiple-photon-added TMSVs, although it holds stronger entanglement, its EPR correlation, two-mode squeezing, sum squeezing, and the fidelity of a coherent state are always smaller than that of the TMSVs. Only when considering the case of teleporting a squeezed vacuum state does the symmetric photon addition make somewhat of an improvement in the fidelity for large-squeezing parameters. In addition, we analytically prove that a one-mode multiple-photon-subtracted TMSVs is equivalent to that of the one-mode multiple-photon-added one. And one-mode multiple-photon operations will diminish the above four quantities for any squeezing parameter r .

  18. Downscaling of surface moisture flux and precipitation in the Ebro Valley (Spain) using analogues and analogues followed by random forests and multiple linear regression

    NASA Astrophysics Data System (ADS)

    Ibarra-Berastegi, G.; Saénz, J.; Ezcurra, A.; Elías, A.; Diaz Argandoña, J.; Errasti, I.

    2011-06-01

    In this paper, reanalysis fields from the ECMWF have been statistically downscaled to predict from large-scale atmospheric fields, surface moisture flux and daily precipitation at two observatories (Zaragoza and Tortosa, Ebro Valley, Spain) during the 1961-2001 period. Three types of downscaling models have been built: (i) analogues, (ii) analogues followed by random forests and (iii) analogues followed by multiple linear regression. The inputs consist of data (predictor fields) taken from the ERA-40 reanalysis. The predicted fields are precipitation and surface moisture flux as measured at the two observatories. With the aim to reduce the dimensionality of the problem, the ERA-40 fields have been decomposed using empirical orthogonal functions. Available daily data has been divided into two parts: a training period used to find a group of about 300 analogues to build the downscaling model (1961-1996) and a test period (1997-2001), where models' performance has been assessed using independent data. In the case of surface moisture flux, the models based on analogues followed by random forests do not clearly outperform those built on analogues plus multiple linear regression, while simple averages calculated from the nearest analogues found in the training period, yielded only slightly worse results. In the case of precipitation, the three types of model performed equally. These results suggest that most of the models' downscaling capabilities can be attributed to the analogues-calculation stage.

  19. Downscaling of surface moisture flux and precipitation in the Ebro Valley (Spain) using analogues and analogues followed by random forests and multiple linear regression

    NASA Astrophysics Data System (ADS)

    Ibarra-Berastegi, G.; Saénz, J.; Ezcurra, A.; Elías, A.; Diaz de Argandoña, J.; Errasti, I.

    2011-02-01

    In this paper, reanalysis fields from the ECMWF have been statistically downscaled to predict from large-scale atmospheric fields surface moisture flux and daily precipitation at two observatories (Zaragoza and Tortosa, Ebro Valley, Spain) during the 1961-2001 period. Three types of downscaling models have been built (i) analogues, (ii) analogues followed by random forests and (iii) analogues followed by multiple linear regression. The inputs consist of data (predictor fields) taken from the ERA-40 reanalysis. The predicted fields are precipitation and surface moisture flux as measured at the two observatories. With the aim to reduce the dimensionality of the problem, the ERA-40 fields have been decomposed using empirical orthogonal functions. Available daily data has been divided into two parts: a training period used to find a group of about 300 analogues to build the downscaling model (1961-1996) and a test period (1997-2001), where models' performance has been assessed using independent data. In the case of surface moisture flux, the models based on analogues followed by random forests do not clearly outperform those built on analogues plus multiple linear regression, while simple averages calculated from the nearest analogues found in the training period, yielded only slightly worse results. In the case of precipitation, the three types of model performed equally. These results suggest that most of the models' downscaling capabilities can be attributted to the analogues-calculation stage.

  20. Does NASA's Constellation Architecture Offer Opportunities to Achieve Multiple Additional Goals in Space?

    NASA Technical Reports Server (NTRS)

    Thronson, Harley A.; Lester, Daniel F.

    2008-01-01

    Every major NASA human spaceflight program in the last four decades has been modified to achieve goals in space not incorporated within the original design goals: the Apollo Applications Program, Skylab, Space Shuttle, and International Space Station. Several groups in the US have been identifying major future science goals, the science facilities necessary to investigate them, as well as possible roles for augmented versions of elements of NASA's Constellation program. Specifically, teams in the astronomy community have been developing concepts for very capable missions to follow the James Webb Space Telescope that could take advantage of - or require - free-space operations by astronauts and/or robots. Taking as one example, the Single-Aperture Far-InfraRed (SAFIR) telescope with a approx.10+ m aperture proposed for operation in the 2020 timeframe. According to current NASA plans, the Ares V launch vehicle (or a variant) will be available about the same time, as will the capability to transport astronauts to the vicinity of the Moon via the Orion Crew Exploration Vehicle and associated systems. [As the lunar surface offers no advantages - and major disadvantages - for most major optical systems, the expensive system for landing and operating on the lunar surface is not required.] Although as currently conceived, SAFIR and other astronomical missions will operate at the Sun-Earth L2 location, it appears trivial to travel for servicing to the more accessible Earth-Moon L1,2 locations. Moreover, as the recent Orbital Express and Automated Transfer Vehicle missions have demonstrated, future robotic capabilities should offer capabilities that would (remotely) extend human presence far beyond the vicinity of the Earth. In addition to multiplying the value of NASA's architecture for future human spaceflight to achieve the goals multiple major stakeholders, if humans one day travel beyond the Earth-Moon system - say, to Mars - technologies and capabilities for operating

  1. Does NASA's Constellation Architecture Offer Opportunities to Achieve Multiple Additional Goals in Space?

    NASA Technical Reports Server (NTRS)

    Thronson, Harley; Lester, Daniel F.

    2008-01-01

    Every major NASA human spaceflight program in the last four decades has been modified to achieve goals in space not incorporated within the original design goals: the Apollo Applications Program, Skylab, Space Shuttle, and International Space Station. Several groups in the US have been identifying major future science goals, the science facilities necessary to investigate them, as well as possible roles for augmented versions of elements of NASA's Constellation program. Specifically, teams in the astronomy community have been developing concepts for very capable missions to follow the James Webb Space Telescope that could take advantage of - or require - free-space operations by astronauts and/or robots. Taking as one example, the Single-Aperture Far-InfraRed (SAFIR) telescope with a approx. 10+ m aperture proposed for operation in the 2020 timeframe. According to current NASA plans, the Ares V launch vehicle (or a variant) will be available about the same time, as will the capability to transport astronauts to the vicinity of the Moon via the Orion Crew Exploration Vehicle and associated systems. [As the lunar surface offers no advantages - and major disadvantages - for most major optical systems, the expensive system for landing and operating on the lunar surface is not required.] Although as currently conceived, SAFIR and other astronomical missions will operate at the Sun-Earth L2 location, it appears trivial to travel for servicing to the more accessible Earth-Moon L1,2 locations. Moreover. as the recent Orbital Express and Automated Transfer Vehicle missions have demonstrated, future robotic capabilities should offer capabilities that would (remotely) extend human presence far beyond the vicinity of the Earth. In addition to multiplying the value of NASA's architecture for future human spaceflight to achieve the goals multiple major stakeholders. if humans one day travel beyond the Earth-Moon system - say, to Mars - technologies and capabilities for operating

  2. Recognition of extensive freshwater and brackish marshes and of multiple transgressions and regressions: The Holocene wetlands of the Delaware Bay and Atlantic Ocean coasts

    SciTech Connect

    Yi, H.I. . Dept. of Geology)

    1992-01-01

    Extensive and closely spaced cores (204) were analyzed to find detailed facies (microfacies) and paleoenvironments in the subsurface sediments along the Delaware Bay and Atlantic Ocean. To determine detailed facies and paleoenvironments, several composite methods were employed: traditional lithological analysis, botanical identification, macro- and micro-paleontological analysis, grain size analysis, organic and inorganic content, water content, mineral composition, particulate plant, and C-14 dating. Twenty-two sedimentary microfacies were identified in the surface and subsurface sediments of the study area. Most of the lower section of the Holocene sediments contained freshwater and brackish marsh microfacies which alternated or intercalated with fluvial microfacies or brackish tidal flat/tidal stream microfacies. After tides encroached upon the freshwater marshes and swamps, several events of transgression and regression were recorded in the stratigraphic section. Finally, saline paleoenvironments predominated at the top section of subsurface sediments. Within saline facies, three subgroups of salt marsh microfacies were identified: high salt marsh sub-microfacies, middle salt marsh sub-microfacies were identified: high salt marsh sub-microfacies, middle salt marsh sub-microfacies, and low salt marsh sub-microfacies. The major controlling factors of these paleoenvironmental changes were local relative sea-level fluctuations, sediment supply, pre-Holocene configuration, fluvial activity, groundwater influence, climatic change, sediment compaction, tectonics, isostasy and biological competition. Ten events of transgression and regression in some areas were found in about 2,000 years, but other areas apparently contained no evidence of multiple events of transgression and regression. Some other areas showed one or two distinctive events of transgression and regression. Therefore, further investigation is necessary to understand the details of these records.

  3. Transitioning from Additive to Multiplicative Thinking: A Design and Teaching Experiment with Third through Fifth Graders

    ERIC Educational Resources Information Center

    Brickwedde, James

    2011-01-01

    The maturation of multiplicative thinking is key to student progress in middle school as rational number, ratio, and proportion concepts are encountered. But many students arrive from the intermediate grades and falter in developing this essential disposition. Elementary students have historically learned multiplication and division as operation…

  4. [Multiple imputation and complete case analysis in logistic regression models: a practical assessment of the impact of incomplete covariate data].

    PubMed

    Camargos, Vitor Passos; César, Cibele Comini; Caiaffa, Waleska Teixeira; Xavier, Cesar Coelho; Proietti, Fernando Augusto

    2011-12-01

    Researchers in the health field often deal with the problem of incomplete databases. Complete Case Analysis (CCA), which restricts the analysis to subjects with complete data, reduces the sample size and may result in biased estimates. Based on statistical grounds, Multiple Imputation (MI) uses all collected data and is recommended as an alternative to CCA. Data from the study Saúde em Beagá, attended by 4,048 adults from two of nine health districts in the city of Belo Horizonte, Minas Gerais State, Brazil, in 2008-2009, were used to evaluate CCA and different MI approaches in the context of logistic models with incomplete covariate data. Peculiarities in some variables in this study allowed analyzing a situation in which the missing covariate data are recovered and thus the results before and after recovery are compared. Based on the analysis, even the more simplistic MI approach performed better than CCA, since it was closer to the post-recovery results.

  5. Highly enantioselective direct Michael addition of nitroalkanes to nitroalkenes catalyzed by amine-thiourea bearing multiple hydrogen-bonding donors.

    PubMed

    Dong, Xiu-Qin; Teng, Huai-Long; Wang, Chun-Jiang

    2009-03-19

    A highly diastereoselective and enantioselective Michael addition of nitroalkanes to nitroalkenes has been achieved by chiral bifunctional amine-thiourea catalyst bearing multiple hydrogen-bonding donors. This catalytic system performs well over a broad scope of substrates, furnishing various 1,3-dinitro compounds in high diastereoselectivity (up to 98:2) and excellent enantioselectivity (up to 99% ee) under mild conditions. Multiple hydrogen bonding donors play a significant role in accelerating reactions, improving diastereoselectivities and enantioselectivities.

  6. 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.

  7. The severity of Minamata disease declined in 25 years: temporal profile of the neurological findings analyzed by multiple logistic regression model.

    PubMed

    Uchino, Makoto; Hirano, Teruyuki; Satoh, Hiroshi; Arimura, Kimiyoshi; Nakagawa, Masanori; Wakamiya, Jyunji

    2005-01-01

    Minamata disease (MD) was caused by ingestion of seafood from the methylmercury-contaminated areas. Although 50 years have passed since the discovery of MD, there have been only a few studies on the temporal profile of neurological findings in certified MD patients. Thus, we evaluated changes in neurological symptoms and signs of MD using discriminants by multiple logistic regression analysis. The severity of predictive index declined in 25 years in most of the patients. Only a few patients showed aggravation of neurological findings, which was due to complications such as spino-cerebellar degeneration. Patients with chronic MD aged over 45 years had several concomitant diseases so that their clinical pictures were complicated. It was difficult to differentiate chronic MD using statistically established discriminants based on sensory disturbance alone. In conclusion, the severity of MD declined in 25 years along with the modification by age-related concomitant disorders.

  8. Fundamental Analysis of the Linear Multiple Regression Technique for Quantification of Water Quality Parameters from Remote Sensing Data. Ph.D. Thesis - Old Dominion Univ.

    NASA Technical Reports Server (NTRS)

    Whitlock, C. H., III

    1977-01-01

    Constituents with linear radiance gradients with concentration may be quantified from signals which contain nonlinear atmospheric and surface reflection effects for both homogeneous and non-homogeneous water bodies provided accurate data can be obtained and nonlinearities are constant with wavelength. Statistical parameters must be used which give an indication of bias as well as total squared error to insure that an equation with an optimum combination of bands is selected. It is concluded that the effect of error in upwelled radiance measurements is to reduce the accuracy of the least square fitting process and to increase the number of points required to obtain a satisfactory fit. The problem of obtaining a multiple regression equation that is extremely sensitive to error is discussed.

  9. Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

    PubMed

    Azadi, Sama; Karimi-Jashni, Ayoub

    2016-02-01

    Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study of 20 cities located in Fars Province, Iran. Four performance measures, MAE, MAPE, RMSE and R were used to evaluate the performance of these models. The MLR, as a conventional model, showed poor prediction performance. On the other hand, the results indicated that the ANN model, as a non-linear model, has a higher predictive accuracy when it comes to prediction of the mean SMSWG rate. As a result, in order to develop a more cost-effective strategy for waste management in the future, the ANN model could be used to predict the mean SMSWG rate.

  10. Development and application of a multiple linear regression model to consider the impact of weekly waste container capacity on the yield from kerbside recycling programmes in Scotland.

    PubMed

    Baird, Jim; Curry, Robin; Reid, Tim

    2013-03-01

    This article describes the development and application of a multiple linear regression model to identify how the key elements of waste and recycling infrastructure, namely container capacity and frequency of collection, affect the yield from municipal kerbside recycling programmes. The overall aim of the research was to gain an understanding of the factors affecting the yield from municipal kerbside recycling programmes in Scotland with an underlying objective to evaluate the efficacy of the model as a decision-support tool for informing the design of kerbside recycling programmes. The study isolates the principal kerbside collection service offered by all 32 councils across Scotland, eliminating those recycling programmes associated with flatted properties or multi-occupancies. The results of the regression analysis model have identified three principal factors which explain 80% of the variability in the average yield of the principal dry recyclate services: weekly residual waste capacity, number of materials collected and the weekly recycling capacity. The use of the model has been evaluated and recommendations made on ongoing methodological development and the use of the results in informing the design of kerbside recycling programmes. We hope that the research can provide insights for the further development of methods to optimise the design and operation of kerbside recycling programmes.

  11. A note on the relationships between multiple imputation, maximum likelihood and fully Bayesian methods for missing responses in linear regression models.

    PubMed

    Chen, Qingxia; Ibrahim, Joseph G

    2014-07-01

    Multiple Imputation, Maximum Likelihood and Fully Bayesian methods are the three most commonly used model-based approaches in missing data problems. Although it is easy to show that when the responses are missing at random (MAR), the complete case analysis is unbiased and efficient, the aforementioned methods are still commonly used in practice for this setting. To examine the performance of and relationships between these three methods in this setting, we derive and investigate small sample and asymptotic expressions of the estimates and standard errors, and fully examine how these estimates are related for the three approaches in the linear regression model when the responses are MAR. We show that when the responses are MAR in the linear model, the estimates of the regression coefficients using these three methods are asymptotically equivalent to the complete case estimates under general conditions. One simulation and a real data set from a liver cancer clinical trial are given to compare the properties of these methods when the responses are MAR.

  12. Multiple Linear Regression Analysis Indicates Association of P-Glycoprotein Substrate or Inhibitor Character with Bitterness Intensity, Measured with a Sensor.

    PubMed

    Yano, Kentaro; Mita, Suzune; Morimoto, Kaori; Haraguchi, Tamami; Arakawa, Hiroshi; Yoshida, Miyako; Yamashita, Fumiyoshi; Uchida, Takahiro; Ogihara, Takuo

    2015-09-01

    P-glycoprotein (P-gp) regulates absorption of many drugs in the gastrointestinal tract and their accumulation in tumor tissues, but the basis of substrate recognition by P-gp remains unclear. Bitter-tasting phenylthiocarbamide, which stimulates taste receptor 2 member 38 (T2R38), increases P-gp activity and is a substrate of P-gp. This led us to hypothesize that bitterness intensity might be a predictor of P-gp-inhibitor/substrate status. Here, we measured the bitterness intensity of a panel of P-gp substrates and nonsubstrates with various taste sensors, and used multiple linear regression analysis to examine the relationship between P-gp-inhibitor/substrate status and various physical properties, including intensity of bitter taste measured with the taste sensor. We calculated the first principal component analysis score (PC1) as the representative value of bitterness, as all taste sensor's outputs shared significant correlation. The P-gp substrates showed remarkably greater mean bitterness intensity than non-P-gp substrates. We found that Km value of P-gp substrates were correlated with molecular weight, log P, and PC1 value, and the coefficient of determination (R(2) ) of the linear regression equation was 0.63. This relationship might be useful as an aid to predict P-gp substrate status at an early stage of drug discovery.

  13. Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow's Milk.

    PubMed

    Marami Milani, Mohammad Reza; Hense, Andreas; Rahmani, Elham; Ploeger, Angelika

    2016-07-23

    This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow's milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R² (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R² (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

  14. Multiple regression and inverse moments improve the characterization of the spatial scaling behavior of daily streamflows in the Southeast United States

    USGS Publications Warehouse

    Farmer, William H.; Over, Thomas M.; Vogel, Richard M.

    2015-01-01

    Understanding the spatial structure of daily streamflow is essential for managing freshwater resources, especially in poorly-gaged regions. Spatial scaling assumptions are common in flood frequency prediction (e.g., index-flood method) and the prediction of continuous streamflow at ungaged sites (e.g. drainage-area ratio), with simple scaling by drainage area being the most common assumption. In this study, scaling analyses of daily streamflow from 173 streamgages in the southeastern US resulted in three important findings. First, the use of only positive integer moment orders, as has been done in most previous studies, captures only the probabilistic and spatial scaling behavior of flows above an exceedance probability near the median; negative moment orders (inverse moments) are needed for lower streamflows. Second, assessing scaling by using drainage area alone is shown to result in a high degree of omitted-variable bias, masking the true spatial scaling behavior. Multiple regression is shown to mitigate this bias, controlling for regional heterogeneity of basin attributes, especially those correlated with drainage area. Previous univariate scaling analyses have neglected the scaling of low-flow events and may have produced biased estimates of the spatial scaling exponent. Third, the multiple regression results show that mean flows scale with an exponent of one, low flows scale with spatial scaling exponents greater than one, and high flows scale with exponents less than one. The relationship between scaling exponents and exceedance probabilities may be a fundamental signature of regional streamflow. This signature may improve our understanding of the physical processes generating streamflow at different exceedance probabilities. 

  15. A flexible mixed-effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients.

    PubMed

    Kondo, Yumi; Zhao, Yinshan; Petkau, John

    2015-06-15

    We develop a new modeling approach to enhance a recently proposed method to detect increases of contrast-enhancing lesions (CELs) on repeated magnetic resonance imaging, which have been used as an indicator for potential adverse events in multiple sclerosis clinical trials. The method signals patients with unusual increases in CEL activity by estimating the probability of observing CEL counts as large as those observed on a patient's recent scans conditional on the patient's CEL counts on previous scans. This conditional probability index (CPI), computed based on a mixed-effect negative binomial regression model, can vary substantially depending on the choice of distribution for the patient-specific random effects. Therefore, we relax this parametric assumption to model the random effects with an infinite mixture of beta distributions, using the Dirichlet process, which effectively allows any form of distribution. To our knowledge, no previous literature considers a mixed-effect regression for longitudinal count variables where the random effect is modeled with a Dirichlet process mixture. As our inference is in the Bayesian framework, we adopt a meta-analytic approach to develop an informative prior based on previous clinical trials. This is particularly helpful at the early stages of trials when less data are available. Our enhanced method is illustrated with CEL data from 10 previous multiple sclerosis clinical trials. Our simulation study shows that our procedure estimates the CPI more accurately than parametric alternatives when the patient-specific random effect distribution is misspecified and that an informative prior improves the accuracy of the CPI estimates.

  16. USING DOSE ADDITION TO ESTIMATE CUMULATIVE RISKS FROM EXPOSURES TO MULTIPLE CHEMICALS

    EPA Science Inventory

    The Food Quality Protection Act (FQPA) of 1996 requires the EPA to consider the cumulative risk from exposure to multiple chemicals that have a common mechanism of toxicity. Three methods, hazard index (HI), point-of-departure index (PODI), and toxicity equivalence factor (TEF), ...

  17. Precise quantitative addition of multiple reagents into droplets in sequence using glass fiber-induced droplet coalescence.

    PubMed

    Li, Chunyu; Xu, Jian; Ma, Bo

    2015-02-07

    Precise quantitative addition of multiple reagents into droplets in sequence is still a bottleneck in droplet-based analysis. To address this issue, we presented a simple and robust glass fiber-induced droplet coalescence method. The hydrophilic glass fiber embedded in the microchannels can induce the deformation of droplets and trigger the coalescence. Serial addition of reagents with controlled volumes was performed by this method without the requirement for an external power source.

  18. Interaction Models for Functional Regression

    PubMed Central

    USSET, JOSEPH; STAICU, ANA-MARIA; MAITY, ARNAB

    2015-01-01

    A functional regression model with a scalar response and multiple functional predictors is proposed that accommodates two-way interactions in addition to their main effects. The proposed estimation procedure models the main effects using penalized regression splines, and the interaction effect by a tensor product basis. Extensions to generalized linear models and data observed on sparse grids or with measurement error are presented. A hypothesis testing procedure for the functional interaction effect is described. The proposed method can be easily implemented through existing software. Numerical studies show that fitting an additive model in the presence of interaction leads to both poor estimation performance and lost prediction power, while fitting an interaction model where there is in fact no interaction leads to negligible losses. The methodology is illustrated on the AneuRisk65 study data. PMID:26744549

  19. 15 CFR 921.33 - Boundary changes, amendments to the management plan, and addition of multiple-site components.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... management plan, and addition of multiple-site components. (a) Changes in the boundary of a Reserve and major changes to the final management plan, including state laws or regulations promulgated specifically for the... management plan change. Changes in the boundary of a Reserve involving the acquisition of properties...

  20. 15 CFR 921.33 - Boundary changes, amendments to the management plan, and addition of multiple-site components.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... management plan, and addition of multiple-site components. (a) Changes in the boundary of a Reserve and major changes to the final management plan, including state laws or regulations promulgated specifically for the... management plan change. Changes in the boundary of a Reserve involving the acquisition of properties...

  1. A Longitudinal Field Study Comparing a Multiplicative and an Additive Model of Motivation and Ability. Technical Report No. 11.

    ERIC Educational Resources Information Center

    Barrett, Gerald V.; And Others

    The relative contribution of motivation to ability measures in predicting performance criteria of sales personnel from successive fiscal periods was investigated. In this context, the merits of a multiplicative and additive combination of motivation and ability measures were examined. The relationship between satisfaction and motivation and…

  2. Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components.

    PubMed

    Riccardi, M; Mele, G; Pulvento, C; Lavini, A; d'Andria, R; Jacobsen, S-E

    2014-06-01

    Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time consuming. Over the years alternative methods, rapid and non-destructive, have been explored. The aim of this work was to evaluate the applicability of a fast and non-invasive field method for estimation of chlorophyll content in quinoa and amaranth leaves based on RGB components analysis of digital images acquired with a standard SLR camera. Digital images of leaves from different genotypes of quinoa and amaranth were acquired directly in the field. Mean values of each RGB component were evaluated via image analysis software and correlated to leaf chlorophyll provided by standard laboratory procedure. Single and multiple regression models using RGB color components as independent variables have been tested and validated. The performance of the proposed method was compared to that of the widely used non-destructive SPAD method. Sensitivity of the best regression models for different genotypes of quinoa and amaranth was also checked. Color data acquisition of the leaves in the field with a digital camera was quick, more effective, and lower cost than SPAD. The proposed RGB models provided better correlation (highest R (2)) and prediction (lowest RMSEP) of the true value of foliar chlorophyll content and had a lower amount of noise in the whole range of chlorophyll studied compared with SPAD and other leaf image processing based models when applied to quinoa and amaranth.

  3. ERP correlates of word production predictors in picture naming: a trial by trial multiple regression analysis from stimulus onset to response

    PubMed Central

    Valente, Andrea; Bürki, Audrey; Laganaro, Marina

    2014-01-01

    A major effort in cognitive neuroscience of language is to define the temporal and spatial characteristics of the core cognitive processes involved in word production. One approach consists in studying the effects of linguistic and pre-linguistic variables in picture naming tasks. So far, studies have analyzed event-related potentials (ERPs) during word production by examining one or two variables with factorial designs. Here we extended this approach by investigating simultaneously the effects of multiple theoretical relevant predictors in a picture naming task. High density EEG was recorded on 31 participants during overt naming of 100 pictures. ERPs were extracted on a trial by trial basis from picture onset to 100 ms before the onset of articulation. Mixed-effects regression models were conducted to examine which variables affected production latencies and the duration of periods of stable electrophysiological patterns (topographic maps). Results revealed an effect of a pre-linguistic variable, visual complexity, on an early period of stable electric field at scalp, from 140 to 180 ms after picture presentation, a result consistent with the proposal that this time period is associated with visual object recognition processes. Three other variables, word Age of Acquisition, Name Agreement, and Image Agreement influenced response latencies and modulated ERPs from ~380 ms to the end of the analyzed period. These results demonstrate that a topographic analysis fitted into the single trial ERPs and covering the entire processing period allows one to associate the cost generated by psycholinguistic variables to the duration of specific stable electrophysiological processes and to pinpoint the precise time-course of multiple word production predictors at once. PMID:25538546

  4. Multiple Pathways Suppress Telomere Addition to DNA Breaks in the Drosophila Germline

    PubMed Central

    Beaucher, Michelle; Zheng, Xiao-Feng; Amariei, Flavia; Rong, Yikang S.

    2012-01-01

    Telomeres protect chromosome ends from being repaired as double-strand breaks (DSBs). Just as DSB repair is suppressed at telomeres, de novo telomere addition is suppressed at the site of DSBs. To identify factors responsible for this suppression, we developed an assay to monitor de novo telomere formation in Drosophila, an organism in which telomeres can be established on chromosome ends with essentially any sequence. Germline expression of the I-SceI endonuclease resulted in precise telomere formation at its cut site with high efficiency. Using this assay, we quantified the frequency of telomere formation in different genetic backgrounds with known or possible defects in DNA damage repair. We showed that disruption of DSB repair factors (Rad51 or DNA ligase IV) or DSB sensing factors (ATRIP or MDC1) resulted in more efficient telomere formation. Interestingly, partial disruption of factors that normally regulate telomere protection (ATM or NBS) also led to higher frequencies of telomere formation, suggesting that these proteins have opposing roles in telomere maintenance vs. establishment. In the ku70 mutant background, telomere establishment was preceded by excessive degradation of DSB ends, which were stabilized upon telomere formation. Most strikingly, the removal of ATRIP caused a dramatic increase in telomeric retrotransposon attachment to broken ends. Our study identifies several pathways thatsuppress telomere addition at DSBs, paving the way for future mechanistic studies. PMID:22446318

  5. Development of comprehensive descriptors for multiple linear regression and artificial neural network modeling of retention behaviors of a variety of compounds on different stationary phases.

    PubMed

    Jalali-Heravi, M; Parastar, F

    2000-12-01

    A new series of six comprehensive descriptors that represent different features of the gas-liquid partition coefficient, K(L), for commonly used stationary phases is developed. These descriptors can be considered as counterparts of the parameters in the Abraham solvatochromic model of solution. A separate multiple linear regression (MLR) model was developed by using the six descriptors for each stationary phase of poly(ethylene glycol adipate) (EGAD), N,N,N',N'-tetrakis(2-hydroxypropyl) ethylenediamine (THPED), poly(ethylene glycol) (Ucon 50 HB 660) (U50HB), di(2-ethylhexyl)phosphoric acid (DEHPA) and tetra-n-butylammonium N,N-(bis-2-hydroxylethyl)-2-aminoethanesulfonate (QBES). The results obtained using these models are in good agreement with the experiment and with the results of the empirical model based on the solvatochromic theory. A 6-6-5 neural network was developed using the descriptors appearing in the MLR models as inputs. Comparison of the mean square errors (MSEs) shows the superiority of the artificial neural network (ANN) over that of the MLR. This indicates that the retention behavior of the molecules on different columns show some nonlinear characteristics. The experimental solvatochromic parameters proposed by Abraham can be replaced by the calculated descriptors in this work.

  6. 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.

  7. Differential effects of MDMA, cocaine, and cannabis use severity on distinctive components of the executive functions in polysubstance users: a multiple regression analysis.

    PubMed

    Verdejo-García, Antonio J; López-Torrecillas, Francisca; Aguilar de Arcos, Francisco; Pérez-García, Miguel

    2005-01-01

    Executive functioning impairments have been demonstrated following consumption of drugs of abuse. These executive impairments could play an important role on the development of the addictive process and rehabilitation of substance abusers. Recent neuropsychological models of executive functioning assume a multicomponent organization of these processes, suggesting different functions could contribute differentially to performance on executive tasks. The aim of this study was to analyze the relationship between severity of consumption of different drugs and neuropsychological performance on tasks sensitive to impairment in the executive subprocesses of working memory, response inhibition, cognitive flexibility, and abstract reasoning. Instruments sensitive to impairment in these four components were administered to 38 polysubstance abusers along with a severity of drug consumption interview. Multiple regression analyses were used. Results showed a differential impact of severity of MDMA abuse on working memory and abstract reasoning indices, of cocaine severity on an inhibitory control index and of cannabis on a cognitive flexibility index. Metabolic reorganization of monoamine frontal-subcortical pathways after drug exposure are proposed as possible explanations for these impairments.

  8. Determination of the acid dissociation constant of bromocresol green and cresol red in water/AOT/isooctane reverse micelles by multiple linear regression and extended principal component analysis.

    PubMed

    Caselli, Maurizio; Mangone, Annarosa; Paolillo, Paola; Traini, Angela

    2002-01-01

    The pKa of 3',3",5',5"tetrabromo-m-cresolsulfonephtalein (Bromocresol Green) and o-cresolsulphonephtalein (Cresol Red) was spectrophotometrically measured in a water/AOT/isooctane microemulsion in the presence of a series of buffers carrying different charges at different water/surfactant ratios. Extended Principal Component Analysis was used for a precise determination of the apparent pKa and of the spectra of the acid and base forms of the dye. The apparent pKa of dyes in water-in-oil microemulsions depends on the charge of the acid and base forms of the buffers present in the water pool. Combination with multiple linear regression increases the precision. Results are discussed taking into account the profile of the electrostatic potential in the water pool and the possible partition of the indicator between the aqueous core and the surfactant. The pKa corrected for these effects are independent of w0 and are close to the value of the pKa in bulk water. On the basis of a tentative hypothesis it is possible to calculate the true pKa of the buffer in the pool.

  9. Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.

    PubMed

    Golmohammadi, Hassan

    2009-11-30

    A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models.

  10. Logistic regression analysis of multiple noninvasive tests for the prediction of the presence and extent of coronary artery disease in men

    SciTech Connect

    Hung, J.; Chaitman, B.R.; Lam, J.; Lesperance, J.; Dupras, G.; Fines, P.; Cherkaoui, O.; Robert, P.; Bourassa, M.G.

    1985-08-01

    The incremental diagnostic yield of clinical data, exercise ECG, stress thallium scintigraphy, and cardiac fluoroscopy to predict coronary and multivessel disease was assessed in 171 symptomatic men by means of multiple logistic regression analyses. When clinical variables alone were analyzed, chest pain type and age were predictive of coronary disease, whereas chest pain type, age, a family history of premature coronary disease before age 55 years, and abnormal ST-T wave changes on the rest ECG were predictive of multivessel disease. The percentage of patients correctly classified by cardiac fluoroscopy (presence or absence of coronary artery calcification), exercise ECG, and thallium scintigraphy was 9%, 25%, and 50%, respectively, greater than for clinical variables, when the presence or absence of coronary disease was the outcome, and 13%, 25%, and 29%, respectively, when multivessel disease was studied; 5% of patients were misclassified. When the 37 clinical and noninvasive test variables were analyzed jointly, the most significant variable predictive of coronary disease was an abnormal thallium scan and for multivessel disease, the amount of exercise performed. The data from this study provide a quantitative model and confirm previous reports that optimal diagnostic efficacy is obtained when noninvasive tests are ordered sequentially. In symptomatic men, cardiac fluoroscopy is a relatively ineffective test when compared to exercise ECG and thallium scintigraphy.

  11. Multiple linear regression model for bromate formation based on the survey data of source waters from geographically different regions across China.

    PubMed

    Yu, Jianwei; Liu, Juan; An, Wei; Wang, Yongjing; Zhang, Junzhi; Wei, Wei; Su, Ming; Yang, Min

    2015-01-01

    A total of 86 source water samples from 38 cities across major watersheds of China were collected for a bromide (Br(-)) survey, and the bromate (BrO3 (-)) formation potentials (BFPs) of 41 samples with Br(-) concentration >20 μg L(-1) were evaluated using a batch ozonation reactor. Statistical analyses indicated that higher alkalinity, hardness, and pH of water samples could lead to higher BFPs, with alkalinity as the most important factor. Based on the survey data, a multiple linear regression (MLR) model including three parameters (alkalinity, ozone dose, and total organic carbon (TOC)) was established with a relatively good prediction performance (model selection criterion = 2.01, R (2) = 0.724), using logarithmic transformation of the variables. Furthermore, a contour plot was used to interpret the influence of alkalinity and TOC on BrO3 (-) formation with prediction accuracy as high as 71 %, suggesting that these two parameters, apart from ozone dosage, were the most important ones affecting the BFPs of source waters with Br(-) concentration >20 μg L(-1). The model could be a useful tool for the prediction of the BFPs of source water.

  12. 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

  13. 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

  14. Escape through an unstable limit cycle driven by multiplicative colored non-Gaussian and additive white Gaussian noises.

    PubMed

    Bag, Bidhan Chandra; Hu, Chin-Kun

    2007-04-01

    In a previous paper [Bag and Hu, Phys. Rev. E 73, 061107 (2006)], we studied the mean lifetime (MLT) for the escape of a Brownian particle through an unstable limit cycle driven by multiplicative colored Gaussian and additive Gaussian white noises and found resonant activation (RA) behavior. In the present paper we switch from Gaussian to non-Gaussian multiplicative colored noise. We find that in the RA phenomenon, the minimum appears at a smaller noise correlation time (tau) for non-Gaussian noises compared to Gaussian noises in the plot of MLT vs tau for a fixed noise variance; the same plot for a given noise strength increases linearly and the increasing rate is smaller for non-Gaussian noises than for the Gaussian noises; the plot of logarithm of inverse of MLT vs inverse of the strength of additive noise is Arrhenius-like for Gaussian colored noise and it becomes similar to the quantum-Kramers rate if the multiplicative noise is non-Gaussian.

  15. Short communication: Genetic correlation and heritability of milk coagulation traits within and across lactations in Holstein cows using multiple-lactation random regression animal models.

    PubMed

    Pretto, D; Vallas, M; Pärna, E; Tänavots, A; Kiiman, H; Kaart, T

    2014-12-01

    Genetic parameters of milk rennet coagulation time (RCT) and curd firmness (a30) among the first 3 lactations in Holstein cows were estimated. The data set included 39,960 test-day records from 5,216 Estonian Holstein cows (the progeny of 306 sires), which were recorded from April 2005 to May 2010 in 98 herds across the country. A multiple-lactation random regression animal model was used. Individual milk samples from each cow were collected during routine milk recording. These samples were analyzed for milk composition and coagulation traits with intervals of 2 to 3 mo in each lactation (7 to 305 DIM) and from first to third lactation. Mean heritabilities were 0.36, 0.32, and 0.28 for log-transformed RCT [ln(RCT)] and 0.47, 0.40, and 0.62 for a30 for parities 1, 2, and 3, respectively. Mean repeatabilities for ln(RCT) were 0.53, 0.55, and 0.56, but 0.59, 0.61, and 0.68 for a30 for parities 1, 2 and 3, respectively. Mean genetic correlations between ln(RCT) and a30 were -0.19, -0.14, and 0.02 for parities 1, 2, and 3, respectively. Mean genetic correlations were 0.91, 0.79, and 0.99 for ln(RCT), and 0.95, 0.94, and 0.94 for a30 between parities 1 and 2, 1 and 3, and 2 and 3, respectively. Due to these high genetic correlations, we concluded that for a proper genetic evaluation of milk coagulation properties it is sufficient to record RCT and a30 only in the first lactation.

  16. Taking into account latency, amplitude, and morphology: improved estimation of single-trial ERPs by wavelet filtering and multiple linear regression.

    PubMed

    Hu, L; Liang, M; Mouraux, A; Wise, R G; Hu, Y; Iannetti, G D

    2011-12-01

    Across-trial averaging is a widely used approach to enhance the signal-to-noise ratio (SNR) of event-related potentials (ERPs). However, across-trial variability of ERP latency and amplitude may contain physiologically relevant information that is lost by across-trial averaging. Hence, we aimed to develop a novel method that uses 1) wavelet filtering (WF) to enhance the SNR of ERPs and 2) a multiple linear regression with a dispersion term (MLR(d)) that takes into account shape distortions to estimate the single-trial latency and amplitude of ERP peaks. Using simulated ERP data sets containing different levels of noise, we provide evidence that, compared with other approaches, the proposed WF+MLR(d) method yields the most accurate estimate of single-trial ERP features. When applied to a real laser-evoked potential data set, the WF+MLR(d) approach provides reliable estimation of single-trial latency, amplitude, and morphology of ERPs and thereby allows performing meaningful correlations at single-trial level. We obtained three main findings. First, WF significantly enhances the SNR of single-trial ERPs. Second, MLR(d) effectively captures and measures the variability in the morphology of single-trial ERPs, thus providing an accurate and unbiased estimate of their peak latency and amplitude. Third, intensity of pain perception significantly correlates with the single-trial estimates of N2 and P2 amplitude. These results indicate that WF+MLR(d) can be used to explore the dynamics between different ERP features, behavioral variables, and other neuroimaging measures of brain activity, thus providing new insights into the functional significance of the different brain processes underlying the brain responses to sensory stimuli.

  17. Multiple linear regression approach for the analysis of the relationships between joints mobility and regional pressure-based parameters in the normal-arched foot.

    PubMed

    Caravaggi, Paolo; Leardini, Alberto; Giacomozzi, Claudia

    2016-10-03

    Plantar load can be considered as a measure of the foot ability to transmit forces at the foot/ground, or foot/footwear interface during ambulatory activities via the lower limb kinematic chain. While morphological and functional measures have been shown to be correlated with plantar load, no exhaustive data are currently available on the possible relationships between range of motion of foot joints and plantar load regional parameters. Joints' kinematics from a validated multi-segmental foot model were recorded together with plantar pressure parameters in 21 normal-arched healthy subjects during three barefoot walking trials. Plantar pressure maps were divided into six anatomically-based regions of interest associated to corresponding foot segments. A stepwise multiple regression analysis was performed to determine the relationships between pressure-based parameters, joints range of motion and normalized walking speed (speed/subject height). Sagittal- and frontal-plane joint motion were those most correlated to plantar load. Foot joints' range of motion and normalized walking speed explained between 6% and 43% of the model variance (adjusted R(2)) for pressure-based parameters. In general, those joints' presenting lower mobility during stance were associated to lower vertical force at forefoot and to larger mean and peak pressure at hindfoot and forefoot. Normalized walking speed was always positively correlated to mean and peak pressure at hindfoot and forefoot. While a large variance in plantar pressure data is still not accounted for by the present models, this study provides statistical corroboration of the close relationship between joint mobility and plantar pressure during stance in the normal healthy foot.

  18. Investigation of the relationship between very warm days in Romania and large-scale atmospheric circulation using multiple linear regression approach

    NASA Astrophysics Data System (ADS)

    Barbu, N.; Cuculeanu, V.; Stefan, S.

    2016-10-01

    The aim of this study is to investigate the relationship between the frequency of very warm days (TX90p) in Romania and large-scale atmospheric circulation for winter (December-February) and summer (June-August) between 1962 and 2010. In order to achieve this, two catalogues from COST733Action were used to derive daily circulation types. Seasonal occurrence frequencies of the circulation types were calculated and have been utilized as predictors within the multiple linear regression model (MLRM) for the estimation of winter and summer TX90p values for 85 synoptic stations covering the entire Romania. A forward selection procedure has been utilized to find adequate predictor combinations and those predictor combinations were tested for collinearity. The performance of the MLRMs has been quantified based on the explained variance. Furthermore, the leave-one-out cross-validation procedure was applied and the root-mean-squared error skill score was calculated at station level in order to obtain reliable evidence of MLRM robustness. From this analysis, it can be stated that the MLRM performance is higher in winter compared to summer. This is due to the annual cycle of incoming insolation and to the local factors such as orography and surface albedo variations. The MLRM performances exhibit distinct variations between regions with high performance in wintertime for the eastern and southern part of the country and in summertime for the western part of the country. One can conclude that the MLRM generally captures quite well the TX90p variability and reveals the potential for statistical downscaling of TX90p values based on circulation types.

  19. Morse–Smale Regression

    SciTech Connect

    Gerber, Samuel; Rubel, Oliver; Bremer, Peer -Timo; Pascucci, Valerio; Whitaker, Ross T.

    2012-01-19

    This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduces a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse–Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this article introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to overfitting. The Morse–Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse–Smale regression. Supplementary Materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse–Smale complex approximation, and additional tables for the climate-simulation study.

  20. Morse-Smale Regression

    PubMed Central

    Gerber, Samuel; Rübel, Oliver; Bremer, Peer-Timo; Pascucci, Valerio; Whitaker, Ross T.

    2012-01-01

    This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduce a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse-Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this paper introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to over-fitting. The Morse-Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse-Smale regression. Supplementary materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse-Smale complex approximation and additional tables for the climate-simulation study. PMID:23687424

  1. Using multiple calibration sets to improve the quantitative accuracy of partial least squares (PLS) regression on open-path fourier transform infrared (OP/FT-IR) spectra of ammonia over wide concentration ranges

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A technique of using multiple calibration sets in partial least squares regression (PLS) was proposed to improve the quantitative determination of ammonia from open-path Fourier transform infrared spectra. The spectra were measured near animal farms, and the path-integrated concentration of ammonia...

  2. New Insights into Trace Element Partitioning in Amphibole from Multiple Regression Analysis, with Application to the Magma Plumbing System of Mt. Lamington (Papua New Guinea)

    NASA Astrophysics Data System (ADS)

    Zhang, J.; Humphreys, M.; Cooper, G.; Davidson, J.; Macpherson, C.

    2015-12-01

    We present a new multiple regression (MR) analysis of published amphibole-melt trace element partitioning data, with the aim of retrieving robust relationships between amphibole crystal-chemical compositions and trace element partition coefficients (D). We examined experimental data for calcic amphiboles of kaersutite, pargasite, tschermakite (Tsch), magnesiohornblende (MgHbl) and magnesiohastingsite (MgHst) compositions crystallized from basanitic-rhyolitic melts (n = 150). The MR analysis demonstrates the varying significance of amphibole major element components assigned to different crystallographic sites (T, M1-3, M4, A) as independent variables in controlling D, and it allows us to retrieve statistically significant relationships for REE, Y, Rb, Sr, Pb, Ti, Zr, Nb (n > 25, R2 > 0.6, p-value < 0.05). For example, DLREE are controlled by SiT, M1-3 site components and CaM4, whereas DMREE-HREE are controlled solely by M1-3 site components. Our overall results for the REE are supported by application of the lattice strain model (Blundy & Wood, 1994). A significant advantage of our study over previous work linking D to melt polymerization (e.g. Tiepolo et al., 2007) is the ability to reconstruct melt compositions from in situ amphibole compositional analyses and published D data. We applied our MR analysis to Mt. Lamington (PNG), where Mg-Hst in quenched mafic enclaves are juxtaposed with MgHbl-Tsch phenocrysts from andesitic host lavas. The results indicate that MgHbl-Tsch are crystallized from a cool, rhyolitic melt (800-900±50 ºC, 70-77±5 wt % SiO2; Ridolfi & Renzulli 2012) with lower Rb and Sr and higher Pb, relative to a hot, andesitic-dacitic melt (950-1,000±50 ºC; 60-70±5 wt % SiO2) where MgHst are crystallized. REE and Nb contents are similar in both types of melts despite higher REE and Nb in MgHbl-Tsch. Therefore, the REE compositional disparity between MgHst and MgHbl-Tsch is driven by the difference in the DREE, rather than the melt REE

  3. Boosted Beta Regression

    PubMed Central

    Schmid, Matthias; Wickler, Florian; Maloney, Kelly O.; Mitchell, Richard; Fenske, Nora; Mayr, Andreas

    2013-01-01

    Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1). Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures. PMID:23626706

  4. Reliability of regression-based normative data for the oral symbol digit modalities test: an evaluation of demographic influences, construct validity, and impairment classification rates in multiple sclerosis samples.

    PubMed

    Berrigan, Lindsay I; Fisk, John D; Walker, Lisa A S; Wojtowicz, Magdalena; Rees, Laura M; Freedman, Mark S; Marrie, Ruth Ann

    2014-01-01

    The oral Symbol Digit Modalities Test (SDMT) has been recommended to assess cognition for multiple sclerosis (MS) patients. However, the lack of adequate normative data has limited its clinical utility. Recently published regression-based norms may resolve this limitation but, because these norms were derived from a relatively small sample, their stability is unclear. We aimed to evaluate the stability of regression-based SDMT norms by comparing existing norms to a cross-validation dataset. First, regression-based normative data were created from a similarly-sized, independent, control sample (n = 94). Next the original and cross-validation norms were compared for equivalency, management of demographic influences, construct validity, and impairment classification rates in a mildly affected MS sample (n = 70). Lastly, similar comparisons were made for a large, representative MS clinic sample (n = 354). We found construct validity and management of demographic influences were equivalent for the two sets of regression-based norms but lower T-scores were obtained using the original dataset, resulting in discrepancies in impairment classification. In conclusion, regression-based norms for the oral SDMT attenuate demographic influences and possess adequate construct validity. However, norms generated using small samples may yield unreliable classification of cognitive impairment. Larger, representative databases will be necessary to improve the clinical utility of regression-based norms.

  5. Understanding poisson regression.

    PubMed

    Hayat, Matthew J; Higgins, Melinda

    2014-04-01

    Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes.

  6. Modeling probability and additive summation for detection across multiple mechanisms under the assumptions of signal detection theory.

    PubMed

    Kingdom, Frederick A A; Baldwin, Alex S; Schmidtmann, Gunnar

    2015-01-01

    Many studies have investigated how multiple stimuli combine to reach threshold. There are broadly speaking two ways this can occur: additive summation (AS) where inputs from the different stimuli add together in a single mechanism, or probability summation (PS) where different stimuli are detected independently by separate mechanisms. PS is traditionally modeled under high threshold theory (HTT); however, tests have shown that HTT is incorrect and that signal detection theory (SDT) is the better framework for modeling summation. Modeling the equivalent of PS under SDT is, however, relatively complicated, leading many investigators to use Monte Carlo simulations for the predictions. We derive formulas that employ numerical integration to predict the proportion correct for detecting multiple stimuli assuming PS under SDT, for the situations in which stimuli are either equal or unequal in strength. Both formulas are general purpose, calculating performance for forced-choice tasks with M alternatives, n stimuli, in Q monitored mechanisms, each subject to a non-linear transducer with exponent τ. We show how the probability (and additive) summation formulas can be used to simulate psychometric functions, which when fitted with Weibull functions make signature predictions for how thresholds and psychometric function slopes vary as a function of τ, n, and Q. We also show how one can fit the formulas directly to real psychometric functions using data from a binocular summation experiment, and show how one can obtain estimates of τ and test whether binocular summation conforms more to PS or AS. The methods described here can be readily applied using software functions newly added to the Palamedes toolbox.

  7. LUT REVEALS AN ALGOL-TYPE ECLIPSING BINARY WITH THREE ADDITIONAL STELLAR COMPANIONS IN A MULTIPLE SYSTEM

    SciTech Connect

    Zhu, L.-Y.; Zhou, X.; Qian, S.-B.; Li, L.-J.; Liao, W.-P.; Tian, X.-M.; Wang, Z.-H.; Hu, J.-Y.

    2016-04-15

    A complete light curve of the neglected eclipsing binary Algol V548 Cygni in the UV band was obtained with the Lunar-based Ultraviolet Telescope in 2014 May. Photometric solutions are obtained using the Wilson–Devinney method. It is found that solutions with and without third light are quite different. The mass ratio without third light is determined to be q = 0.307, while that derived with third light is q = 0.606. It is shown that V548 Cygni is a semi-detached binary where the secondary component is filling the critical Roche lobe. An analysis of all available eclipse times suggests that there are three cyclic variations in the O–C diagram that are interpreted by the light travel-time effect via the presence of three additional stellar companions. This is in agreement with the presence of a large quantity of third light in the system. The masses of these companions are estimated as m sin i′ ∼ 1.09, 0.20, and 0.52 M{sub ⊙}. They are orbiting the central binary with orbital periods of about 5.5, 23.3, and 69.9 years, i.e., in 1:4:12 resonance orbit. Their orbital separations are about 4.5, 13.2, and 26.4 au, respectively. Our photometric solutions suggest that they contribute about 32.4% to the total light of the multiple system. No obvious long-term changes in the orbital period were found, indicating that the contributions of the mass transfer and the mass loss due to magnetic braking to the period variations are comparable. The detection of three possible additional stellar components orbiting a typical Algol in a multiple system make V548 Cygni a very interesting binary to study in the future.

  8. LUT Reveals an Algol-type Eclipsing Binary With Three Additional Stellar Companions in a Multiple System

    NASA Astrophysics Data System (ADS)

    Zhu, L.-Y.; Zhou, X.; Hu, J.-Y.; Qian, S.-B.; Li, L.-J.; Liao, W.-P.; Tian, X.-M.; Wang, Z.-H.

    2016-04-01

    A complete light curve of the neglected eclipsing binary Algol V548 Cygni in the UV band was obtained with the Lunar-based Ultraviolet Telescope in 2014 May. Photometric solutions are obtained using the Wilson-Devinney method. It is found that solutions with and without third light are quite different. The mass ratio without third light is determined to be q = 0.307, while that derived with third light is q = 0.606. It is shown that V548 Cygni is a semi-detached binary where the secondary component is filling the critical Roche lobe. An analysis of all available eclipse times suggests that there are three cyclic variations in the O-C diagram that are interpreted by the light travel-time effect via the presence of three additional stellar companions. This is in agreement with the presence of a large quantity of third light in the system. The masses of these companions are estimated as m sin i‧ ˜ 1.09, 0.20, and 0.52 M⊙. They are orbiting the central binary with orbital periods of about 5.5, 23.3, and 69.9 years, i.e., in 1:4:12 resonance orbit. Their orbital separations are about 4.5, 13.2, and 26.4 au, respectively. Our photometric solutions suggest that they contribute about 32.4% to the total light of the multiple system. No obvious long-term changes in the orbital period were found, indicating that the contributions of the mass transfer and the mass loss due to magnetic braking to the period variations are comparable. The detection of three possible additional stellar components orbiting a typical Algol in a multiple system make V548 Cygni a very interesting binary to study in the future.

  9. Precision Efficacy Analysis for Regression.

    ERIC Educational Resources Information Center

    Brooks, Gordon P.

    When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…

  10. Preventing Return of Fear in an Animal Model of Anxiety: Additive Effects of Massive Extinction and Extinction in Multiple Contexts

    PubMed Central

    Laborda, Mario A.; Miller, Ralph R.

    2013-01-01

    Fear conditioning and experimental extinction have been presented as models of anxiety disorders and exposure therapy, respectively. Moreover, the return of fear serves as a model of relapse after exposure therapy. Here we present two experiments, with rats as subjects in a lick suppression preparation, in which we assessed the additive effects of two different treatments to attenuate the return of fear. First, we evaluated whether two phenomena known to generate return of fear (i.e., spontaneous recovery and renewal) summate to produce a stronger reappearance of extinguished fear. At test, rats evaluated outside the extinction context following a long delay after extinction (i.e., a delayed context shift) exhibited greater return of extinguished fear than rats evaluated outside the extinction context alone, but return of extinguished fear following a delayed context shift did not significantly differ from the return of fear elicited in rats tested following a long delay after extinction alone. Additionally, extinction in multiple contexts and a massive extinction treatment each attenuated the strong return of fear produced by a delayed context shift. Moreover, the conjoint action of these treatments was significantly more successful in preventing the reappearance of extinguished fear, suggesting that extensive cue exposure administered in several different therapeutic settings has the potential to reduce relapse after therapy for anxiety disorders, more than either manipulation alone. PMID:23611075

  11. Preventing return of fear in an animal model of anxiety: additive effects of massive extinction and extinction in multiple contexts.

    PubMed

    Laborda, Mario A; Miller, Ralph R

    2013-06-01

    Fear conditioning and experimental extinction have been presented as models of anxiety disorders and exposure therapy, respectively. Moreover, the return of fear serves as a model of relapse after exposure therapy. Here we present two experiments, with rats as subjects in a lick suppression preparation, in which we assessed the additive effects of two different treatments to attenuate the return of fear. First, we evaluated whether two phenomena known to generate return of fear (i.e., spontaneous recovery and renewal) summate to produce a stronger reappearance of extinguished fear. At test, rats evaluated outside the extinction context following a long delay after extinction (i.e., a delayed context shift) exhibited greater return of extinguished fear than rats evaluated outside the extinction context alone, but return of extinguished fear following a delayed context shift did not significantly differ from the return of fear elicited in rats tested following a long delay after extinction alone. Additionally, extinction in multiple contexts and a massive extinction treatment each attenuated the strong return of fear produced by a delayed context shift. Moreover, the conjoint action of these treatments was significantly more successful in preventing the reappearance of extinguished fear, suggesting that extensive cue exposure administered in several different therapeutic settings has the potential to reduce relapse after therapy for anxiety disorders, more than either manipulation alone.

  12. Molecular cytogenetic identification of a wheat-rye 1R addition line with multiple spikelets and resistance to powdery mildew.

    PubMed

    Yang, Wujuan; Wang, Changyou; Chen, Chunhuan; Wang, Yajuan; Zhang, Hong; Liu, Xinlun; Ji, Wanquan

    2016-04-01

    Alien addition lines are important for transferring useful genes from alien species into common wheat. Rye is an important and valuable gene resource for improving wheat disease resistance, yield, and environment adaptation. A new wheat-rye addition line, N9436B, was developed from the progeny of the cross of common wheat (Triticum aestivum L., 2n = 6x = 42, AABBDD) cultivar Shaanmai 611 and rye (Secale cereal L., 2n = 2x = 14, RR) accession Austrian rye. We characterized this new line by cytology, genomic in situ hybridization (GISH), fluorescence in situ hybridization (FISH), molecular markers, and disease resistance screening. N9436B was stable in morphology and cytology, with a chromosome composition of 2n = 42 + 2t = 22II. GISH investigations showed that this line contained two rye chromosomes. GISH, FISH, and molecular maker identification suggested that the introduced R chromosome and the missing wheat chromosome arms were 1R chromosome and 2DL chromosome arm, respectively. N9436B exhibited 30-37 spikelets per spike and a high level of resistance to powdery mildew (Blumeria graminis f. sp. tritici, Bgt) isolate E09 at the seedling stage. N9436B was cytologically stable, had the trait of multiple spikelets, and was resistant to powdery mildew; this line should thus be useful in wheat improvement.

  13. The effectiveness of selected feed and water additives for reducing Salmonella spp. of public health importance in broiler chickens: a systematic review, meta-analysis, and meta-regression approach.

    PubMed

    Totton, Sarah C; Farrar, Ashley M; Wilkins, Wendy; Bucher, Oliver; Waddell, Lisa A; Wilhelm, Barbara J; McEwen, Scott A; Rajić, Andrijana

    2012-10-01

    Eating inappropriately prepared poultry meat is a major cause of foodborne salmonellosis. Our objectives were to determine the efficacy of feed and water additives (other than competitive exclusion and antimicrobials) on reducing Salmonella prevalence or concentration in broiler chickens using systematic review-meta-analysis and to explore sources of heterogeneity found in the meta-analysis through meta-regression. Six electronic databases were searched (Current Contents (1999-2009), Agricola (1924-2009), MEDLINE (1860-2009), Scopus (1960-2009), Centre for Agricultural Bioscience (CAB) (1913-2009), and CAB Global Health (1971-2009)), five topic experts were contacted, and the bibliographies of review articles and a topic-relevant textbook were manually searched to identify all relevant research. Study inclusion criteria comprised: English-language primary research investigating the effects of feed and water additives on the Salmonella prevalence or concentration in broiler chickens. Data extraction and study methodological assessment were conducted by two reviewers independently using pretested forms. Seventy challenge studies (n=910 unique treatment-control comparisons), seven controlled studies (n=154), and one quasi-experiment (n=1) met the inclusion criteria. Compared to an assumed control group prevalence of 44 of 1000 broilers, random-effects meta-analysis indicated that the Salmonella cecal colonization in groups with prebiotics (fructooligosaccharide, lactose, whey, dried milk, lactulose, lactosucrose, sucrose, maltose, mannanoligosaccharide) added to feed or water was 15 out of 1000 broilers; with lactose added to feed or water it was 10 out of 1000 broilers; with experimental chlorate product (ECP) added to feed or water it was 21 out of 1000. For ECP the concentration of Salmonella in the ceca was decreased by 0.61 log(10)cfu/g in the treated group compared to the control group. Significant heterogeneity (Cochran's Q-statistic p≤0.10) was observed

  14. Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos (2016).

    PubMed

    McClelland, Gary H; Irwin, Julie R; Disatnik, David; Sivan, Liron

    2017-02-01

    Multicollinearity is irrelevant to the search for moderator variables, contrary to the implications of Iacobucci, Schneider, Popovich, and Bakamitsos (Behavior Research Methods, 2016, this issue). Multicollinearity is like the red herring in a mystery novel that distracts the statistical detective from the pursuit of a true moderator relationship. We show multicollinearity is completely irrelevant for tests of moderator variables. Furthermore, readers of Iacobucci et al. might be confused by a number of their errors. We note those errors, but more positively, we describe a variety of methods researchers might use to test and interpret their moderated multiple regression models, including two-stage testing, mean-centering, spotlighting, orthogonalizing, and floodlighting without regard to putative issues of multicollinearity. We cite a number of recent studies in the psychological literature in which the researchers used these methods appropriately to test, to interpret, and to report their moderated multiple regression models. We conclude with a set of recommendations for the analysis and reporting of moderated multiple regression that should help researchers better understand their models and facilitate generalizations across studies.

  15. Stochastic resonance in a piecewise nonlinear model driven by multiplicative non-Gaussian noise and additive white noise

    NASA Astrophysics Data System (ADS)

    Guo, Yongfeng; Shen, Yajun; Tan, Jianguo

    2016-09-01

    The phenomenon of stochastic resonance (SR) in a piecewise nonlinear model driven by a periodic signal and correlated noises for the cases of a multiplicative non-Gaussian noise and an additive Gaussian white noise is investigated. Applying the path integral approach, the unified colored noise approximation and the two-state model theory, the analytical expression of the signal-to-noise ratio (SNR) is derived. It is found that conventional stochastic resonance exists in this system. From numerical computations we obtain that: (i) As a function of the non-Gaussian noise intensity, the SNR is increased when the non-Gaussian noise deviation parameter q is increased. (ii) As a function of the Gaussian noise intensity, the SNR is decreased when q is increased. This demonstrates that the effect of the non-Gaussian noise on SNR is different from that of the Gaussian noise in this system. Moreover, we further discuss the effect of the correlation time of the non-Gaussian noise, cross-correlation strength, the amplitude and frequency of the periodic signal on SR.

  16. A handheld mid-infrared methane sensor using a dual-step differential method for additive/multiplicative noise suppression

    NASA Astrophysics Data System (ADS)

    Zheng, Yue; Dang, Peipei; Zheng, Chuantao; Ye, Weilin; Wang, Yiding

    2016-11-01

    A miniature mid-infrared (mid-IR) methane (CH4) sensor system was developed by employing a wide-band wire-source and a semi-ellipsoid multi-pass gas cell. A dual-step differential method instead of the traditional one-step differential method was adopted by this sensor to tune measuring range/zero point and to suppress the additive/multiplicative noise. This method included a first subtraction operation between the two output signals (including a detection signal and a reference signal) from the dual-channel detector and a second subtraction operation on the amplitudes of the first-subtraction signal and the reference signal, followed by a ratio operation between the amplitude of the second-subtraction signal and the reference signal. Detailed experiments were performed to assess the performance of the sensor system. The detection range is 0-50 k ppm, and as the concentration gets larger than 12 k ppm, the relative detection error falls into the range of -3% to +3%. The Allan deviation is about 4.65 ppm with an averaging time of 1 s, and such value can be further improved to 0.45 ppm with an averaging time of 124 s. Due to the cost-effective incandescence wire-source, the small-size ellipsoid multi-pass gas cell and the miniature structure of the sensor, the developed standalone device shows potential applications of CH4 detection under coal-mine environment.

  17. A field operational test on valve-regulated lead-acid absorbent-glass-mat batteries in micro-hybrid electric vehicles. Part II. Results based on multiple regression analysis and tear-down analysis

    NASA Astrophysics Data System (ADS)

    Schaeck, S.; Karspeck, T.; Ott, C.; Weirather-Koestner, D.; Stoermer, A. O.

    2011-03-01

    In the first part of this work [1] a field operational test (FOT) on micro-HEVs (hybrid electric vehicles) and conventional vehicles was introduced. Valve-regulated lead-acid (VRLA) batteries in absorbent glass mat (AGM) technology and flooded batteries were applied. The FOT data were analyzed by kernel density estimation. In this publication multiple regression analysis is applied to the same data. Square regression models without interdependencies are used. Hereby, capacity loss serves as dependent parameter and several battery-related and vehicle-related parameters as independent variables. Battery temperature is found to be the most critical parameter. It is proven that flooded batteries operated in the conventional power system (CPS) degrade faster than VRLA-AGM batteries in the micro-hybrid power system (MHPS). A smaller number of FOT batteries were applied in a vehicle-assigned test design where the test battery is repeatedly mounted in a unique test vehicle. Thus, vehicle category and specific driving profiles can be taken into account in multiple regression. Both parameters have only secondary influence on battery degradation, instead, extended vehicle rest time linked to low mileage performance is more serious. A tear-down analysis was accomplished for selected VRLA-AGM batteries operated in the MHPS. Clear indications are found that pSoC-operation with periodically fully charging the battery (refresh charging) does not result in sulphation of the negative electrode. Instead, the batteries show corrosion of the positive grids and weak adhesion of the positive active mass.

  18. Bayesian quantile regression-based nonlinear mixed-effects joint models for time-to-event and longitudinal data with multiple features.

    PubMed

    Huang, Yangxin; Chen, Jiaqing

    2016-12-30

    This article explores Bayesian joint models for a quantile of longitudinal response, mismeasured covariate and event time outcome with an attempt to (i) characterize the entire conditional distribution of the response variable based on quantile regression that may be more robust to outliers and misspecification of error distribution; (ii) tailor accuracy from measurement error, evaluate non-ignorable missing observations, and adjust departures from normality in covariate; and (iii) overcome shortages of confidence in specifying a time-to-event model. When statistical inference is carried out for a longitudinal data set with non-central location, non-linearity, non-normality, measurement error, and missing values as well as event time with being interval censored, it is important to account for the simultaneous treatment of these data features in order to obtain more reliable and robust inferential results. Toward this end, we develop Bayesian joint modeling approach to simultaneously estimating all parameters in the three models: quantile regression-based nonlinear mixed-effects model for response using asymmetric Laplace distribution, linear mixed-effects model with skew-t distribution for mismeasured covariate in the presence of informative missingness and accelerated failure time model with unspecified nonparametric distribution for event time. We apply the proposed modeling approach to analyzing an AIDS clinical data set and conduct simulation studies to assess the performance of the proposed joint models and method. Copyright © 2016 John Wiley & Sons, Ltd.

  19. An improved algorithm of temperature compensation for a near infrared multiple-acquisition system based on two-dimensional regression analysis.

    PubMed

    Yu, Xu-yao; An, Jia-bao; Yu, Hui; Shi, Yao; Deng, Yong; Zhou, Jia-lu; Xu, Ke-xin

    2015-08-01

    The near infrared (NIR) spectroscopy analytical technique is one of the most advanced and promising tools in many domains. NIR acquisition is easily influenced by temperature, thereby affecting qualitative and quantitative analyses. In this paper, a temperature compensation model was established between NIR signals and output voltage values based on two-dimensional regression analysis. The effectiveness of the proposed compensation scheme was experimentally demonstrated by the measurement of six super luminescent diode sources at 293-313 K. The coefficient of variation was decreased 2-fold with this compensation algorithm. The results indicated that it was suitable for various NIR spectral acquisition systems with lower complexity and a higher signal-noise-ratio after being applied to an acousto-optic-tunable-filter system.

  20. Regression: A Bibliography.

    ERIC Educational Resources Information Center

    Pedrini, D. T.; Pedrini, Bonnie C.

    Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…

  1. Practical Session: Simple Linear Regression

    NASA Astrophysics Data System (ADS)

    Clausel, M.; Grégoire, G.

    2014-12-01

    Two exercises are proposed to illustrate the simple linear regression. The first one is based on the famous Galton's data set on heredity. We use the lm R command and get coefficients estimates, standard error of the error, R2, residuals …In the second example, devoted to data related to the vapor tension of mercury, we fit a simple linear regression, predict values, and anticipate on multiple linear regression. This pratical session is an excerpt from practical exercises proposed by A. Dalalyan at EPNC (see Exercises 1 and 2 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_4.pdf).

  2. A polyethylenimine-modified carboxyl-poly(styrene/acrylamide) copolymer nanosphere for co-delivering of CpG and TGF-β receptor I inhibitor with remarkable additive tumor regression effect against liver cancer in mice

    PubMed Central

    Liang, Shuyan; Hu, Jun; Xie, Yuanyuan; Zhou, Qing; Zhu, Yanhong; Yang, Xiangliang

    2016-01-01

    Cancer immunotherapy based on nanodelivery systems has shown potential for treatment of various malignancies, owing to the benefits of tumor targeting of nanoparticles. However, induction of a potent T-cell immune response against tumors still remains a challenge. In this study, polyethylenimine-modified carboxyl-styrene/acrylamide (PS) copolymer nano-spheres were developed as a delivery system of unmethylated cytosine-phosphate-guanine (CpG) oligodeoxynucleotides and transforming growth factor-beta (TGF-β) receptor I inhibitors for cancer immunotherapy. TGF-β receptor I inhibitors (LY2157299, LY) were encapsulated to the PS via hydrophobic interaction, while CpG oligodeoxynucleotides were loaded onto the PS through electrostatic interaction. Compared to the control group, tumor inhibition in the PS-LY/CpG group was up to 99.7% without noticeable toxicity. The tumor regression may be attributed to T-cell activation and amplification in mouse models. The results highlight the additive effect of CpG and TGF-β receptor I inhibitors co-delivered in cancer immunotherapy. PMID:28008250

  3. "Bunched Black Swans" in Complex Geosystems: Cross-Disciplinary Approaches to the Additive and Multiplicative Modelling of Correlated Extreme Bursts

    NASA Astrophysics Data System (ADS)

    Watkins, N. W.; Rypdal, M.; Lovsletten, O.

    2012-12-01

    -stationarity explicitly built in. In record breaking statistics, a record is defined in the sense used in everyday language, to be the largest value yet recorded in a time series, for example, the 2004 Sumatran Boxing Day earthquake was at the time the largest to be digitally recorded. The third group of approaches (e.g. avalanches) are explicitly spatiotemporal and so also include spatial structure. This presentation will discuss two examples of our recent work on the burst problem. We will show numerical results extending the preliminary results presented in [Watkins et al, PRE, 2009] using a standard additive model, linear fractional stable motion (LFSM). LFSM explicitly includes both heavy tails and long range dependence, allowing us to study how these 2 effects compete in determining the burst duration and size exponent probability distributions. We will contrast these simulations with new analytical studies of bursts in a multiplicative process, the multifractal random walk (MRW). We will present an analytical derivation for the scaling of the burst durations and make a preliminary comparison with data from the AE index from solar-terrestrial physics. We believe our result is more generally applicable than the MRW model, and that it applies to a broad class of multifractal processes.

  4. Trends in Mathematics and Science Performance in 18 Countries: Multiple Regression Analysis of the Cohort Effects of TIMSS 1995-2007

    ERIC Educational Resources Information Center

    Hong, Hee Kyung

    2012-01-01

    The purpose of this study was to simultaneously examine relationships between teacher quality and instructional time and mathematics and science achievement of 8th grade cohorts in 18 advanced and developing economies. In addition, the study examined changes in mathematics and science performance across the two groups of economies over time using…

  5. Regression Analysis: Legal Applications in Institutional Research

    ERIC Educational Resources Information Center

    Frizell, Julie A.; Shippen, Benjamin S., Jr.; Luna, Andrew L.

    2008-01-01

    This article reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of…

  6. A Comparison of Seven Cox Regression-Based Models to Account for Heterogeneity Across Multiple HIV Treatment Cohorts in Latin America and the Caribbean

    PubMed Central

    Giganti, Mark J.; Luz, Paula M.; Caro-Vega, Yanink; Cesar, Carina; Padgett, Denis; Koenig, Serena; Echevarria, Juan; McGowan, Catherine C.; Shepherd, Bryan E.

    2015-01-01

    Abstract Many studies of HIV/AIDS aggregate data from multiple cohorts to improve power and generalizability. There are several analysis approaches to account for cross-cohort heterogeneity; we assessed how different approaches can impact results from an HIV/AIDS study investigating predictors of mortality. Using data from 13,658 HIV-infected patients starting antiretroviral therapy from seven Latin American and Caribbean cohorts, we illustrate the assumptions of seven readily implementable approaches to account for across cohort heterogeneity with Cox proportional hazards models, and we compare hazard ratio estimates across approaches. As a sensitivity analysis, we modify cohort membership to generate specific heterogeneity conditions. Hazard ratio estimates varied slightly between the seven analysis approaches, but differences were not clinically meaningful. Adjusted hazard ratio estimates for the association between AIDS at treatment initiation and death varied from 2.00 to 2.20 across approaches that accounted for heterogeneity; the adjusted hazard ratio was estimated as 1.73 in analyses that ignored across cohort heterogeneity. In sensitivity analyses with more extreme heterogeneity, we noted a slightly greater distinction between approaches. Despite substantial heterogeneity between cohorts, the impact of the specific approach to account for heterogeneity was minimal in our case study. Our results suggest that it is important to account for across cohort heterogeneity in analyses, but that the specific technique for addressing heterogeneity may be less important. Because of their flexibility in accounting for cohort heterogeneity, we prefer stratification or meta-analysis methods, but we encourage investigators to consider their specific study conditions and objectives. PMID:25647087

  7. Cactus: An Introduction to Regression

    ERIC Educational Resources Information Center

    Hyde, Hartley

    2008-01-01

    When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…

  8. Increased effect of IMiDs by addition of cytokine-induced killer cells in multiple myeloma.

    PubMed

    Bullok, Katharina F; Sippel, Christoph; Schmidt-Wolf, Ingo G H

    2016-12-01

    Immunomodulatory drugs (IMiDs), such as thalidomide, lenalidomide and pomalidomide, represent the basic principle of multiple myeloma treatment. However, the development of resistance is a limiting factor. Over the last years, the efficient application of cytokine-induced killer (CIK) cells has been reported as an alternative strategy to treat hematological neoplasms. In this study, we tested for a potential synergistic effect by combining the IMiDs thalidomide, lenalidomide and pomalidomide with CIK cells in different myeloma cell lines in vitro. Myeloma cells tested with CIK cells were significantly reduced. In the combination, myeloma cells were significantly reduced compared with cells only tested with IMiDs but not to the cells tested with CIK cells. Otherwise, the number of CIK cells was significantly reduced when treated with IMiDs. Because IMiDs are active in patients with myeloma, these results lead to the expectation that combination of IMiDs and CIK cells achieve better results in the treatment of multiple myeloma compared with the single use of IMiDs. Therefore, further examinations in an in vivo setting are necessary to have a closer look on the cellular interactions. Copyright © 2015 John Wiley & Sons, Ltd.

  9. Formation of peptides from amino acids by single or multiple additions of ATP to suspensions of nucleoproteinoid microparticles

    NASA Technical Reports Server (NTRS)

    Nakashima, T.; Fox, S. W.

    1981-01-01

    The synthesis of peptides from individual amino acids or pairs of amino acids and ATP in the presence of catalysis by nucleoproteinoid microparticles is investigated. Experiments were performed with suspensions formed from the condensation of lysine-rich and acidic proteinoids with polyadenylic acid, to which were added glycine, phenylalanine, proline, lysine or glycine-phenylalanine mixtures, and ATP either at once or serially. Peptide yields are found to be greatest for equal amounts of acidic and basic proteinoids. The addition of imidazole is found to alter the preference of glycine-phenylalanine mixtures to form mixed heteropeptides rather than homopeptides. A rapid ATP decay in the peptide synthesis reaction is observed, and a greater yield is obtained for repeated small additions than for a single addition of ATP. The experimental system has properties similar to modern cells, and represents an organizational unit ready for the evolution of associated biochemical pathways.

  10. In situ experiments for element species-specific environmental reactivity of tin and mercury compounds using isotopic tracers and multiple linear regression.

    PubMed

    Rodriguez-Gonzalez, Pablo; Bouchet, Sylvain; Monperrus, Mathilde; Tessier, Emmanuel; Amouroux, David

    2013-03-01

    The fate of mercury (Hg) and tin (Sn) compounds in ecosystems is strongly determined by their alkylation/dealkylation pathways. However, the experimental determination of those transformations is still not straightforward and methodologies need to be refined. The purpose of this work is the development of a comprehensive and adaptable tool for an accurate experimental assessment of specific formation/degradation yields and half-lives of elemental species in different aquatic environments. The methodology combines field incubations of coastal waters and surface sediments with the addition of species-specific isotopically enriched tracers and a mathematical approach based on the deconvolution of isotopic patterns. The method has been applied to the study of the environmental reactivity of Hg and Sn compounds in coastal water and surface sediment samples collected in two different coastal ecosystems of the South French Atlantic Coast (Arcachon Bay and Adour Estuary). Both the level of isotopically enriched species and the spiking solution composition were found to alter dibutyltin and monomethylmercury degradation yields, while no significant changes were measurable for tributyltin and Hg(II). For butyltin species, the presence of light was found to be the main source of degradation and removal of these contaminants from surface coastal environments. In contrast, photomediated processes do not significantly influence either the methylation of mercury or the demethylation of methylmercury. The proposed method constitutes an advancement from the previous element-specific isotopic tracers' approaches, which allows for instance to discriminate the extent of net and oxidative Hg demethylation and to identify which debutylation step is controlling the environmental persistence of butyltin compounds.

  11. Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

    PubMed Central

    Marami Milani, Mohammad Reza; Hense, Andreas; Rahmani, Elham; Ploeger, Angelika

    2016-01-01

    This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available. PMID:28231147

  12. Wrong Signs in Regression Coefficients

    NASA Technical Reports Server (NTRS)

    McGee, Holly

    1999-01-01

    When using parametric cost estimation, it is important to note the possibility of the regression coefficients having the wrong sign. A wrong sign is defined as a sign on the regression coefficient opposite to the researcher's intuition and experience. Some possible causes for the wrong sign discussed in this paper are a small range of x's, leverage points, missing variables, multicollinearity, and computational error. Additionally, techniques for determining the cause of the wrong sign are given.

  13. Physicochemical factors associated with binding and retention of compounds in ocular melanin of rats: correlations using data from whole-body autoradiography and molecular modeling for multiple linear regression analyses.

    PubMed

    Zane, P A; Brindle, S D; Gause, D O; O'Buck, A J; Raghavan, P R; Tripp, S L

    1990-09-01

    The relationship between the physicochemical characteristics of 27 new drug candidates and their distribution into the melanin-containing structure of the rat eye, the uveal tract, was examined. Tissue distribution data were obtained from whole-body autoradiograms of pigmented Long-Evans rats sacrificed at 5 min and 96 hr after dosing. The physicochemical parameters considered include molecular weight, pKa, degree of ionization, octanol/water partition coefficient (log Po/w), drug-melanin binding energy, and acid/base status of the functional groups within the molecule. Multiple linear regression analysis was used to describe the best model correlating physicochemical and/or biological characteristics of these compounds to their initial distribution at 5 min and to the retention of residual radioactivity in ocular melanin at 96 hr post-injection. The early distribution was a function primarily of acid/base status, pKa, binding energy, and log P(o/w), whereas uveal tract retention in rats was a function of volume of distribution (V1), log P(o/w), pKa, and binding energy. Further, there was a relationship between the initial distribution of a compound into the uveal tract and its retention 96 hr later. More specifically, the structures most likely to be distributed and ultimately retained at high concentrations were those containing strongly basic functionalities, such as piperidine or piperazine moieties and other amines. Further, the more lipophilic and, hence, widely distributed the basic compound, the greater the likelihood that it interacts with ocular melanin. In summary, the use of multiple linear regression analysis was useful in distinguishing which physicochemical characteristics of a compound or group of compounds contributed to melanin binding in pigmented rats in vivo.

  14. Anaerobic alkane biodegradation by cultures enriched from oil sands tailings ponds involves multiple species capable of fumarate addition.

    PubMed

    Tan, BoonFei; Semple, Kathleen; Foght, Julia

    2015-05-01

    A methanogenic short-chain alkane-degrading culture (SCADC) was enriched from oil sands tailings and transferred several times with a mixture of C6, C7, C8 and C10 n-alkanes as the predominant organic carbon source, plus 2-methylpentane, 3-methylpentane and methylcyclopentane as minor components. Cultures produced ∼40% of the maximum theoretical methane during 18 months incubation while depleting the n-alkanes, 2-methylpentane and methylcyclopentane. Substrate depletion correlated with detection of metabolites characteristic of fumarate activation of 2-methylpentane and methylcyclopentane, but not n-alkane metabolites. During active methanogenesis with the mixed alkanes, reverse-transcription PCR confirmed the expression of functional genes (assA and bssA) associated with hydrocarbon addition to fumarate. Pyrosequencing of 16S rRNA genes amplified during active alkane degradation revealed enrichment of Clostridia (particularly Peptococcaceae) and methanogenic Archaea (Methanosaetaceae and Methanomicrobiaceae). Methanogenic cultures transferred into medium containing sulphate produced sulphide, depleted n-alkanes and produced the corresponding succinylated alkane metabolites, but were slow to degrade 2-methylpentane and methylcyclopentane; these cultures were enriched in Deltaproteobacteria rather than Clostridia. 3-Methylpentane was not degraded by any cultures. Thus, nominally methanogenic oil sands tailings harbour dynamic and versatile hydrocarbon-degrading fermentative syntrophs and sulphate reducers capable of degrading n-, iso- and cyclo-alkanes by addition to fumarate.

  15. Transition-metal-catalyzed additions of C-H bonds to C-X (X = N, O) multiple bonds via C-H bond activation.

    PubMed

    Yan, Guobing; Wu, Xiangmei; Yang, Minghua

    2013-09-14

    Chemical transformations via catalytic C-H bond activation have been established as one of the most powerful tools in organic synthetic chemistry. Transition-metal-catalyzed addition reactions of C-H bonds to polar C-X (X = N, O) multiple bonds, such as aldehydes, ketones, imines, isocyanates, nitriles, isocyanides, carbon monoxide and carbon dioxide, have undergone a rapid development in recent years. In this review, recent advances in this active area have been highlighted and their mechanisms have been discussed.

  16. Combined action of time-delay and colored cross-associated multiplicative and additive noises on stability and stochastic resonance for a stochastic metapopulation system

    NASA Astrophysics Data System (ADS)

    Wang, Kang-Kang; Zong, De-Cai; Wang, Ya-Jun; Li, Sheng-Hong

    2016-05-01

    In this paper, the transition between the stable state of a big density and the extinction state and stochastic resonance (SR) for a time-delayed metapopulation system disturbed by colored cross-correlated noises are investigated. By applying the fast descent method, the small time-delay approximation and McNamara and Wiesenfeld's SR theory, we investigate the impacts of time-delay, the multiplicative, additive noises and colored cross-correlated noise on the SNR and the shift between the two states of the system. Numerical results show that the multiplicative, additive noises and time-delay can all speed up the transition from the stable state to the extinction state, while the correlation noise and its correlation time can slow down the extinction process of the population system. With respect to SNR, the multiplicative noise always weakens the SR effect, while noise correlation time plays a dual role in motivating the SR phenomenon. Meanwhile, time-delay mainly plays a negative role in stimulating the SR phenomenon. Conversely, it could motivate the SR effect to increase the strength of the cross-correlation noise in the SNR-β plot, while the increase of additive noise intensity will firstly excite SR, and then suppress the SR effect.

  17. Predicting Counselor Effectiveness: A Multiple Regression Approach.

    ERIC Educational Resources Information Center

    Mendoza, Buena Flor H.

    This study attempted to determine whether counselor effectiveness designated by a high level of performance in a first counseling practicum as ranked by faculty supervisors, can be predicted with a knowledge of the extent to which the individual possesses the personal qualities of open-mindedness, tolerance for ambiguity, general mental health,…

  18. TI-59 Programs for Multiple Regression.

    DTIC Science & Technology

    1980-05-01

    and record 8, 8’x’y, (x’X) y’y and x’y (e) load Program #2 (1 bank), along with the stored data (f) compute 0’(x’Y) for the reduced model.r( r Steps ... c ), (e) and (f) are optional, depending on whether it is desired to test hypotheses that various components of a are zero. Components of S and (x’x

  19. Faculty Salary: Issues in Multiple Regressions

    ERIC Educational Resources Information Center

    Liu, Richard; Liu, Rebecca

    2005-01-01

    Equity in faculty salaries has always been a controversial issue facing institutions of higher education. As a bastion of academic freedom where faculty and students purse knowledge, it is almost anti-intellectual to find a gender gap in salaries. More importantly, there are laws, which ensure gender equity in salaries. Thus, salary equity has…

  20. Multiple predictor smoothing methods for sensitivity analysis.

    SciTech Connect

    Helton, Jon Craig; Storlie, Curtis B.

    2006-08-01

    The use of multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models is investigated. Specifically, sensitivity analysis procedures based on smoothing methods employing the stepwise application of the following nonparametric regression techniques are described: (1) locally weighted regression (LOESS), (2) additive models, (3) projection pursuit regression, and (4) recursive partitioning regression. The indicated procedures are illustrated with both simple test problems and results from a performance assessment for a radioactive waste disposal facility (i.e., the Waste Isolation Pilot Plant). As shown by the example illustrations, the use of smoothing procedures based on nonparametric regression techniques can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present.

  1. Multiple Stressors in Agricultural Streams: A Mesocosm Study of Interactions among Raised Water Temperature, Sediment Addition and Nutrient Enrichment

    PubMed Central

    Piggott, Jeremy J.; Lange, Katharina; Townsend, Colin R.; Matthaei, Christoph D.

    2012-01-01

    Changes to land use affect streams through nutrient enrichment, increased inputs of sediment and, where riparian vegetation has been removed, raised water temperature. We manipulated all three stressors in experimental streamside channels for 30 days and determined the individual and pair-wise combined effects on benthic invertebrate and algal communities and on leaf decay, a measure of ecosystem functioning. We added nutrients (phosphorus+nitrogen; high, intermediate, natural) and/or sediment (grain size 0.2 mm; high, intermediate, natural) to 18 channels supplied with water from a nearby stream. Temperature was increased by 1.4°C in half the channels, simulating the loss of upstream and adjacent riparian shade. Sediment affected 93% of all biological response variables (either as an individual effect or via an interaction with another stressor) generally in a negative manner, while nutrient enrichment affected 59% (mostly positive) and raised temperature 59% (mostly positive). More of the algal components of the community responded to stressors acting individually than did invertebrate components, whereas pair-wise stressor interactions were more common in the invertebrate community. Stressors interacted often and in a complex manner, with interactions between sediment and temperature most common. Thus, the negative impact of high sediment on taxon richness of both algae and invertebrates was stronger at raised temperature, further reducing biodiversity. In addition, the decay rate of leaf material (strength loss) accelerated with nutrient enrichment at ambient but not at raised temperature. A key implication of our findings for resource managers is that the removal of riparian shading from streams already subjected to high sediment inputs, or land-use changes that increase erosion or nutrient runoff in a landscape without riparian buffers, may have unexpected effects on stream health. We highlight the likely importance of intact or restored buffer strips, both

  2. Survival Data and Regression Models

    NASA Astrophysics Data System (ADS)

    Grégoire, G.

    2014-12-01

    We start this chapter by introducing some basic elements for the analysis of censored survival data. Then we focus on right censored data and develop two types of regression models. The first one concerns the so-called accelerated failure time models (AFT), which are parametric models where a function of a parameter depends linearly on the covariables. The second one is a semiparametric model, where the covariables enter in a multiplicative form in the expression of the hazard rate function. The main statistical tool for analysing these regression models is the maximum likelihood methodology and, in spite we recall some essential results about the ML theory, we refer to the chapter "Logistic Regression" for a more detailed presentation.

  3. Regressive systemic sclerosis.

    PubMed Central

    Black, C; Dieppe, P; Huskisson, T; Hart, F D

    1986-01-01

    Systemic sclerosis is a disease which usually progresses or reaches a plateau with persistence of symptoms and signs. Regression is extremely unusual. Four cases of established scleroderma are described in which regression is well documented. The significance of this observation and possible mechanisms of disease regression are discussed. Images PMID:3718012

  4. NCCS Regression Test Harness

    SciTech Connect

    Tharrington, Arnold N.

    2015-09-09

    The NCCS Regression Test Harness is a software package that provides a framework to perform regression and acceptance testing on NCCS High Performance Computers. The package is written in Python and has only the dependency of a Subversion repository to store the regression tests.

  5. Unitary Response Regression Models

    ERIC Educational Resources Information Center

    Lipovetsky, S.

    2007-01-01

    The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

  6. Research of the Additional Losses Occurring in Optical Fiber at its Multiple Bends in the Range Waves 1310nm, 1550nm and 1625nm Long

    NASA Astrophysics Data System (ADS)

    Yurchenko, A. V.; Gorlov, N. I.; Alkina, A. D.; Mekhtiev, A. D.; Kovtun, A. A.

    2016-01-01

    Article is devoted to research of the additional losses occurring in the optical fiber at its multiple bends in the range waves of 1310 nanometers, 1550 nanometers and 1625 nanometers long. Article is directed on creation of the external factors methods which allow to estimate and eliminate negative influence. The automated way of calculation of losses at a bend is developed. Results of scientific researches are used by engineers of “Kazaktelekom” AS for practical definition of losses service conditions. For modeling the Wolfram|Alpha environment — the knowledge base and a set of computing algorithms was chosen. The greatest losses are noted on wavelength 1310nm and 1625nm. All dependences are nonlinear. Losses with each following excess are multiplicative.

  7. Fully Regressive Melanoma

    PubMed Central

    Ehrsam, Eric; Kallini, Joseph R.; Lebas, Damien; Modiano, Philippe; Cotten, Hervé

    2016-01-01

    Fully regressive melanoma is a phenomenon in which the primary cutaneous melanoma becomes completely replaced by fibrotic components as a result of host immune response. Although 10 to 35 percent of cases of cutaneous melanomas may partially regress, fully regressive melanoma is very rare; only 47 cases have been reported in the literature to date. AH of the cases of fully regressive melanoma reported in the literature were diagnosed in conjunction with metastasis on a patient. The authors describe a case of fully regressive melanoma without any metastases at the time of its diagnosis. Characteristic findings on dermoscopy, as well as the absence of melanoma on final biopsy, confirmed the diagnosis. PMID:27672418

  8. XRA image segmentation using regression

    NASA Astrophysics Data System (ADS)

    Jin, Jesse S.

    1996-04-01

    Segmentation is an important step in image analysis. Thresholding is one of the most important approaches. There are several difficulties in segmentation, such as automatic selecting threshold, dealing with intensity distortion and noise removal. We have developed an adaptive segmentation scheme by applying the Central Limit Theorem in regression. A Gaussian regression is used to separate the distribution of background from foreground in a single peak histogram. The separation will help to automatically determine the threshold. A small 3 by 3 widow is applied and the modal of the local histogram is used to overcome noise. Thresholding is based on local weighting, where regression is used again for parameter estimation. A connectivity test is applied to the final results to remove impulse noise. We have applied the algorithm to x-ray angiogram images to extract brain arteries. The algorithm works well for single peak distribution where there is no valley in the histogram. The regression provides a method to apply knowledge in clustering. Extending regression for multiple-level segmentation needs further investigation.

  9. Improved Regression Calibration

    ERIC Educational Resources Information Center

    Skrondal, Anders; Kuha, Jouni

    2012-01-01

    The likelihood for generalized linear models with covariate measurement error cannot in general be expressed in closed form, which makes maximum likelihood estimation taxing. A popular alternative is regression calibration which is computationally efficient at the cost of inconsistent estimation. We propose an improved regression calibration…

  10. Treatment failure of intrathecal baclofen and supra-additive effect of nabiximols in multiple sclerosis-related spasticity: a case report

    PubMed Central

    Trampe, Nadine; Chan, Andrew

    2013-01-01

    Multiple sclerosis (MS)-related spasticity is associated with disability and impairment in quality of life. We report on a patient with secondary progressive MS and spastic tetraparesis (Expanded Disability Status Scale score 8.5). The right arm exhibited flexor spasticity resulting in functional disability despite multimodal symptomatic treatment. Intrathecal baclofen led to side effects despite decreasing efficacy. Low-dose nabiximols improved spasticity and function with recovery of daily-life activities and spasticity-related symptoms. Reduction of intrathecal baclofen ameliorated adverse drug reactions. Add-on cannabinoid therapy was effective in therapy-refractory spasticity with supra-additive effect in combining intrathecal baclofen and nabiximols, hypothetically explained by mutually complementing mechanisms of action. PMID:23634192

  11. Determination of additivity of apparent and standardized ileal digestibility of amino acids in diets containing multiple protein sources fed to growing pigs.

    PubMed

    Xue, P C; Ragland, D; Adeola, O

    2014-09-01

    An experiment was conducted in growing pigs to investigate the additivity of apparent ileal digestibility (AID) or standardized ileal digestibility (SID) of CP and AA in mixed diets containing multiple protein sources. Using the determined AID or SID for CP and AA in corn, soybean meal (SBM), corn distillers' dried grains with solubles (DDGS), or canola meal (CM), the AID or SID for 4 mixed diets based on corn-SBM, corn-SBM-DDGS, corn-SBM-CM, or corn-SBM-DDGS-CM were predicted and compared with determined AID or SID, respectively. Eighteen growing pigs (initial BW = 61.3 ± 5.5 kg) were surgically fitted with T-cannulas and assigned to a duplicated 9 × 4 incomplete Latin square design with 9 diets and 4 periods. The 9 experimental diets consisted of a nitrogen-free diet (NFD) to estimate basal ileal endogenous loss (BEL) of AA, 4 semipurified diets to determine the AID and SID of CP and AA in the 4 ingredients, and 4 mixed diets to test the additivity of AID and SID. Chromic oxide was added as an indigestible marker. Pigs were fed 1 of the 9 diets during each 7-d period, and ileal digesta were collected on d 6 and 7, from 0800 to 1800 h. The analyzed AA levels for the mixed diets were close to the calculated values based on the AA composition of each ingredient. The results revealed that the predicted SID were consistent with determined values, except for Leu, Thr, Asp, Cys, Pro, and Ser in the corn-SBM diet and Met and Cys in the corn-SBM-DDGS diet. The determined AID for total AA and Arg, His, Trp, Gly, and Pro in the corn-SBM diet were greater (P < 0.05) than predicted. For the corn-SBM-DDGS diet, the determined AID were greater (P < 0.05) than predicted AID for CP, total AA, and all AA except for Arg, Leu, and Pro. In the corn-SBM-CM diet, the determined AID were greater (P < 0.05) than predicted AID for Arg, Cys, and Gly. When compared with determined values, predicted AID in the corn-SBM-DDGS-CM diet were lower (P < 0.05) for total AA and Arg, Met, Cys, and

  12. A Gibbs sampler for multivariate linear regression

    NASA Astrophysics Data System (ADS)

    Mantz, Adam B.

    2016-04-01

    Kelly described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modelled by a flexible mixture of Gaussians rather than assumed to be uniform. Here, I extend the Kelly algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Secondly, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamically relaxed galaxy clusters as a function of their mass and redshift. An implementation of the Gibbs sampler in the R language, called LRGS, is provided.

  13. Mapping geogenic radon potential by regression kriging.

    PubMed

    Pásztor, László; Szabó, Katalin Zsuzsanna; Szatmári, Gábor; Laborczi, Annamária; Horváth, Ákos

    2016-02-15

    Radon ((222)Rn) gas is produced in the radioactive decay chain of uranium ((238)U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly.

  14. George: Gaussian Process regression

    NASA Astrophysics Data System (ADS)

    Foreman-Mackey, Daniel

    2015-11-01

    George is a fast and flexible library, implemented in C++ with Python bindings, for Gaussian Process regression useful for accounting for correlated noise in astronomical datasets, including those for transiting exoplanet discovery and characterization and stellar population modeling.

  15. Use of probabilistic weights to enhance linear regression myoelectric control

    NASA Astrophysics Data System (ADS)

    Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

    2015-12-01

    Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

  16. Multiple Logistic Regression Analysis of Risk Factors Associated with Denture Plaque and Staining in Chinese Removable Denture Wearers over 40 Years Old in Xi’an – a Cross-Sectional Study

    PubMed Central

    Chai, Zhiguo; Chen, Jihua; Zhang, Shaofeng

    2014-01-01

    Background Removable dentures are subject to plaque and/or staining problems. Denture hygiene habits and risk factors differ among countries and regions. The aims of this study were to assess hygiene habits and denture plaque and staining risk factors in Chinese removable denture wearers aged >40 years in Xi’an through multiple logistic regression analysis (MLRA). Methods Questionnaires were administered to 222 patients whose removable dentures were examined clinically to assess wear status and levels of plaque and staining. Univariate analyses were performed to identify potential risk factors for denture plaque/staining. MLRA was performed to identify significant risk factors. Results Brushing (77.93%) was the most prevalent cleaning method in the present study. Only 16.4% of patients regularly used commercial cleansers. Most (81.08%) patients removed their dentures overnight. MLRA indicated that potential risk factors for denture plaque were the duration of denture use (reference, ≤0.5 years; 2.1–5 years: OR = 4.155, P = 0.001; >5 years: OR = 7.238, P<0.001) and cleaning method (reference, chemical cleanser; running water: OR = 7.081, P = 0.010; brushing: OR = 3.567, P = 0.005). Potential risk factors for denture staining were female gender (OR = 0.377, P = 0.013), smoking (OR = 5.471, P = 0.031), tea consumption (OR = 3.957, P = 0.002), denture scratching (OR = 4.557, P = 0.036), duration of denture use (reference, ≤0.5 years; 2.1–5 years: OR = 7.899, P = 0.001; >5 years: OR = 27.226, P<0.001), and cleaning method (reference, chemical cleanser; running water: OR = 29.184, P<0.001; brushing: OR = 4.236, P = 0.007). Conclusion Denture hygiene habits need further improvement. An understanding of the risk factors for denture plaque and staining may provide the basis for preventive efforts. PMID:24498369

  17. 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.

  18. [Understanding logistic regression].

    PubMed

    El Sanharawi, M; Naudet, F

    2013-10-01

    Logistic regression is one of the most common multivariate analysis models utilized in epidemiology. It allows the measurement of the association between the occurrence of an event (qualitative dependent variable) and factors susceptible to influence it (explicative variables). The choice of explicative variables that should be included in the logistic regression model is based on prior knowledge of the disease physiopathology and the statistical association between the variable and the event, as measured by the odds ratio. The main steps for the procedure, the conditions of application, and the essential tools for its interpretation are discussed concisely. We also discuss the importance of the choice of variables that must be included and retained in the regression model in order to avoid the omission of important confounding factors. Finally, by way of illustration, we provide an example from the literature, which should help the reader test his or her knowledge.

  19. Practical Session: Logistic Regression

    NASA Astrophysics Data System (ADS)

    Clausel, M.; Grégoire, G.

    2014-12-01

    An exercise is proposed to illustrate the logistic regression. One investigates the different risk factors in the apparition of coronary heart disease. It has been proposed in Chapter 5 of the book of D.G. Kleinbaum and M. Klein, "Logistic Regression", Statistics for Biology and Health, Springer Science Business Media, LLC (2010) and also by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr341.pdf). This example is based on data given in the file evans.txt coming from http://www.sph.emory.edu/dkleinb/logreg3.htm#data.

  20. Canonical Analysis as a Generalized Regression Technique for Multivariate Analysis.

    ERIC Educational Resources Information Center

    Williams, John D.

    The use of characteristic coding (dummy coding) is made in showing solutions to four multivariate problems using canonical analysis. The canonical variates can be themselves analyzed by the use of multiple linear regression. When the canonical variates are used as criteria in a multiple linear regression, the R2 values are equal to 0, where 0 is…

  1. An Effect Size for Regression Predictors in Meta-Analysis

    ERIC Educational Resources Information Center

    Aloe, Ariel M.; Becker, Betsy Jane

    2012-01-01

    A new effect size representing the predictive power of an independent variable from a multiple regression model is presented. The index, denoted as r[subscript sp], is the semipartial correlation of the predictor with the outcome of interest. This effect size can be computed when multiple predictor variables are included in the regression model…

  2. Modelling of filariasis in East Java with Poisson regression and generalized Poisson regression models

    NASA Astrophysics Data System (ADS)

    Darnah

    2016-04-01

    Poisson regression has been used if the response variable is count data that based on the Poisson distribution. The Poisson distribution assumed equal dispersion. In fact, a situation where count data are over dispersion or under dispersion so that Poisson regression inappropriate because it may underestimate the standard errors and overstate the significance of the regression parameters, and consequently, giving misleading inference about the regression parameters. This paper suggests the generalized Poisson regression model to handling over dispersion and under dispersion on the Poisson regression model. The Poisson regression model and generalized Poisson regression model will be applied the number of filariasis cases in East Java. Based regression Poisson model the factors influence of filariasis are the percentage of families who don't behave clean and healthy living and the percentage of families who don't have a healthy house. The Poisson regression model occurs over dispersion so that we using generalized Poisson regression. The best generalized Poisson regression model showing the factor influence of filariasis is percentage of families who don't have healthy house. Interpretation of result the model is each additional 1 percentage of families who don't have healthy house will add 1 people filariasis patient.

  3. Modern Regression Discontinuity Analysis

    ERIC Educational Resources Information Center

    Bloom, Howard S.

    2012-01-01

    This article provides a detailed discussion of the theory and practice of modern regression discontinuity (RD) analysis for estimating the effects of interventions or treatments. Part 1 briefly chronicles the history of RD analysis and summarizes its past applications. Part 2 explains how in theory an RD analysis can identify an average effect of…

  4. Explorations in Statistics: Regression

    ERIC Educational Resources Information Center

    Curran-Everett, Douglas

    2011-01-01

    Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This seventh installment of "Explorations in Statistics" explores regression, a technique that estimates the nature of the relationship between two things for which we may only surmise a mechanistic or predictive…

  5. Effect of sucrose availability on wheel-running as an operant and as a reinforcing consequence on a multiple schedule: Additive effects of extrinsic and automatic reinforcement.

    PubMed

    Belke, Terry W; Pierce, W David

    2015-07-01

    As a follow up to Belke and Pierce's (2014) study, we assessed the effects of repeated presentation and removal of sucrose solution on the behavior of rats responding on a two-component multiple schedule. Rats completed 15 wheel turns (FR 15) for either 15% or 0% sucrose solution in the manipulated component and lever pressed 10 times on average (VR 10) for an opportunity to complete 15 wheel turns (FR 15) in the other component. In contrast to our earlier study, the components advanced based on time (every 8min) rather than completed responses. Results showed that in the manipulated component wheel-running rates were higher and the latency to initiate running longer when sucrose was present (15%) compared to absent (0% or water); the number of obtained outcomes (sucrose/water), however, did not differ with the presentation and withdrawal of sucrose. For the wheel-running as reinforcement component, rates of wheel turns, overall lever-pressing rates, and obtained wheel-running reinforcements were higher, and postreinforcement pauses shorter, when sucrose was present (15%) than absent (0%) in manipulated component. Overall, our findings suggest that wheel-running rate regardless of its function (operant or reinforcement) is maintained by automatically generated consequences (automatic reinforcement) and is increased as an operant by adding experimentally arranged sucrose reinforcement (extrinsic reinforcement). This additive effect on operant wheel-running generalizes through induction or arousal to the wheel-running as reinforcement component, increasing the rate of responding for opportunities to run and the rate of wheel-running per opportunity.

  6. Different sleep onset criteria at the multiple sleep latency test (MSLT): an additional marker to differentiate central nervous system (CNS) hypersomnias.

    PubMed

    Pizza, Fabio; Vandi, Stefano; Detto, Stefania; Poli, Francesca; Franceschini, Christian; Montagna, Pasquale; Plazzi, Giuseppe

    2011-03-01

    Excessive daytime sleepiness (EDS) has different correlates in non-rapid eye movement (NREM) [idiopathic hypersomnia (IH) without long sleep time] and REM sleep [narcolepsy without cataplexy (NwoC) and narcolepsy with cataplexy (NC)]-related hypersomnias of central origin. We analysed sleep onset characteristics at the multiple sleep latency test (MSLT) applying simultaneously two sleep onset criteria in 44 NC, seven NwoC and 16 IH consecutive patients referred for subjective EDS complaint. Sleep latency (SL) at MSLT was assessed both as the time elapsed to the occurrence of a single epoch of sleep Stage 1 NREM (SL) and of unequivocal sleep [three sleep Stage 1 NREM epochs or any other sleep stage epoch, sustained SL (SusSL)]. Idiopathic hypersomnia patients showed significantly (P<0.0001) longer SusSL than SL (7.7±2.5 versus 5.6±1.3 min, respectively) compared to NwoC (5.8±2.5 versus 5.3±2.2 min) and NC patients (4.1±3 versus 3.9±3 min). A mean difference threshold between SusSL and SL ≥27 s reached a diagnostic value to discriminate IH versus NC and NwoC sufferers (sensitivity 88%; specificity 82%). Moreover, NC patients showed better subjective sleepiness perception than NwoC and IH cases in the comparison between naps with or without sleep occurrence. Simultaneous application of the two widely used sleep onset criteria differentiates IH further from NC and NwoC patients: IH fluctuate through a wake-Stage 1 NREM sleep state before the onset of sustained sleep, while NC and NwoC shift abruptly into a sustained sleep. The combination of SusSL and SL determination at MSLT should be tested as an additional objective differential criterion for EDS disorders.

  7. Astronomical Methods for Nonparametric Regression

    NASA Astrophysics Data System (ADS)

    Steinhardt, Charles L.; Jermyn, Adam

    2017-01-01

    I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.

  8. Locating the Extrema of Fungible Regression Weights

    ERIC Educational Resources Information Center

    Waller, Niels G.; Jones, Jeff A.

    2009-01-01

    In a multiple regression analysis with three or more predictors, every set of alternate weights belongs to an infinite class of "fungible weights" (Waller, Psychometrica, "in press") that yields identical "SSE" (sum of squared errors) and R[superscript 2] values. When the R[superscript 2] using the alternate weights is a fixed value, fungible…

  9. Commonality Analysis for the Regression Case.

    ERIC Educational Resources Information Center

    Murthy, Kavita

    Commonality analysis is a procedure for decomposing the coefficient of determination (R superscript 2) in multiple regression analyses into the percent of variance in the dependent variable associated with each independent variable uniquely, and the proportion of explained variance associated with the common effects of predictors in various…

  10. Moving the Bar: Transformations in Linear Regression.

    ERIC Educational Resources Information Center

    Miranda, Janet

    The assumption that is most important to the hypothesis testing procedure of multiple linear regression is the assumption that the residuals are normally distributed, but this assumption is not always tenable given the realities of some data sets. When normal distribution of the residuals is not met, an alternative method can be initiated. As an…

  11. A new bivariate negative binomial regression model

    NASA Astrophysics Data System (ADS)

    Faroughi, Pouya; Ismail, Noriszura

    2014-12-01

    This paper introduces a new form of bivariate negative binomial (BNB-1) regression which can be fitted to bivariate and correlated count data with covariates. The BNB regression discussed in this study can be fitted to bivariate and overdispersed count data with positive, zero or negative correlations. The joint p.m.f. of the BNB1 distribution is derived from the product of two negative binomial marginals with a multiplicative factor parameter. Several testing methods were used to check overdispersion and goodness-of-fit of the model. Application of BNB-1 regression is illustrated on Malaysian motor insurance dataset. The results indicated that BNB-1 regression has better fit than bivariate Poisson and BNB-2 models with regards to Akaike information criterion.

  12. Investigating bias in squared regression structure coefficients

    PubMed Central

    Nimon, Kim F.; Zientek, Linda R.; Thompson, Bruce

    2015-01-01

    The importance of structure coefficients and analogs of regression weights for analysis within the general linear model (GLM) has been well-documented. The purpose of this study was to investigate bias in squared structure coefficients in the context of multiple regression and to determine if a formula that had been shown to correct for bias in squared Pearson correlation coefficients and coefficients of determination could be used to correct for bias in squared regression structure coefficients. Using data from a Monte Carlo simulation, this study found that squared regression structure coefficients corrected with Pratt's formula produced less biased estimates and might be more accurate and stable estimates of population squared regression structure coefficients than estimates with no such corrections. While our findings are in line with prior literature that identified multicollinearity as a predictor of bias in squared regression structure coefficients but not coefficients of determination, the findings from this study are unique in that the level of predictive power, number of predictors, and sample size were also observed to contribute bias in squared regression structure coefficients. PMID:26217273

  13. Investigating bias in squared regression structure coefficients.

    PubMed

    Nimon, Kim F; Zientek, Linda R; Thompson, Bruce

    2015-01-01

    The importance of structure coefficients and analogs of regression weights for analysis within the general linear model (GLM) has been well-documented. The purpose of this study was to investigate bias in squared structure coefficients in the context of multiple regression and to determine if a formula that had been shown to correct for bias in squared Pearson correlation coefficients and coefficients of determination could be used to correct for bias in squared regression structure coefficients. Using data from a Monte Carlo simulation, this study found that squared regression structure coefficients corrected with Pratt's formula produced less biased estimates and might be more accurate and stable estimates of population squared regression structure coefficients than estimates with no such corrections. While our findings are in line with prior literature that identified multicollinearity as a predictor of bias in squared regression structure coefficients but not coefficients of determination, the findings from this study are unique in that the level of predictive power, number of predictors, and sample size were also observed to contribute bias in squared regression structure coefficients.

  14. 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)

  15. Calculating a Stepwise Ridge Regression.

    ERIC Educational Resources Information Center

    Morris, John D.

    1986-01-01

    Although methods for using ordinary least squares regression computer programs to calculate a ridge regression are available, the calculation of a stepwise ridge regression requires a special purpose algorithm and computer program. The correct stepwise ridge regression procedure is given, and a parallel FORTRAN computer program is described.…

  16. Orthogonal Regression: A Teaching Perspective

    ERIC Educational Resources Information Center

    Carr, James R.

    2012-01-01

    A well-known approach to linear least squares regression is that which involves minimizing the sum of squared orthogonal projections of data points onto the best fit line. This form of regression is known as orthogonal regression, and the linear model that it yields is known as the major axis. A similar method, reduced major axis regression, is…

  17. Marginal longitudinal semiparametric regression via penalized splines

    PubMed Central

    Kadiri, M. Al; Carroll, R.J.; Wand, M.P.

    2010-01-01

    We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models. PMID:21037941

  18. Steganalysis using logistic regression

    NASA Astrophysics Data System (ADS)

    Lubenko, Ivans; Ker, Andrew D.

    2011-02-01

    We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.

  19. Influence of Al³⁺ addition on the flocculation and sedimentation of activated sludge: comparison of single and multiple dosing patterns.

    PubMed

    Wen, Yue; Zheng, Wanlin; Yang, Yundi; Cao, Asheng; Zhou, Qi

    2015-05-15

    In this study, the flocculation and sedimentation performance of activated sludge (AS) with single and multiple dosing of trivalent aluminum (Al(3+)) were studied. The AS samples were cultivated in sequencing batch reactors at 22 °C. The dosages of Al(3+) were 0.00, 0.125, 0.5, 1.0, and 1.5 meq/L for single dosing, and 0.1 meq/L for multiple dosing. Under single dosing conditions, as Al(3+) dosage increased, the zeta potential, total interaction energy, and effluent turbidity decreased, whereas the sludge volume index (SVI) increased, indicating that single Al(3+) dosing could enhance sludge flocculation, but deteriorate sedimentation. By comparison, adding an equal amount of Al(3+) through multiple dosing achieved a similar reduction in turbidity, but the zeta potential was higher, while the loosely bound extracellular polymeric substances (LB-EPS) content and SVI remarkably declined. Although the difference in the flocculation performances between the two dosing patterns was not significant, the underlying mechanisms were quite distinct: the interaction energy played a more important role under single dosing conditions, whereas multiple dosing was more effective in reducing the EPS content. Multiple dosing, which allows sufficient time for sludge restructuring and floc aggregation, could simultaneously optimize sludge flocculation and sedimentation.

  20. Structural regression trees

    SciTech Connect

    Kramer, S.

    1996-12-31

    In many real-world domains the task of machine learning algorithms is to learn a theory for predicting numerical values. In particular several standard test domains used in Inductive Logic Programming (ILP) are concerned with predicting numerical values from examples and relational and mostly non-determinate background knowledge. However, so far no ILP algorithm except one can predict numbers and cope with nondeterminate background knowledge. (The only exception is a covering algorithm called FORS.) In this paper we present Structural Regression Trees (SRT), a new algorithm which can be applied to the above class of problems. SRT integrates the statistical method of regression trees into ILP. It constructs a tree containing a literal (an atomic formula or its negation) or a conjunction of literals in each node, and assigns a numerical value to each leaf. SRT provides more comprehensible results than purely statistical methods, and can be applied to a class of problems most other ILP systems cannot handle. Experiments in several real-world domains demonstrate that the approach is competitive with existing methods, indicating that the advantages are not at the expense of predictive accuracy.

  1. Regression Segmentation for M³ Spinal Images.

    PubMed

    Wang, Zhijie; Zhen, Xiantong; Tay, KengYeow; Osman, Said; Romano, Walter; Li, Shuo

    2015-08-01

    Clinical routine often requires to analyze spinal images of multiple anatomic structures in multiple anatomic planes from multiple imaging modalities (M(3)). Unfortunately, existing methods for segmenting spinal images are still limited to one specific structure, in one specific plane or from one specific modality (S(3)). In this paper, we propose a novel approach, Regression Segmentation, that is for the first time able to segment M(3) spinal images in one single unified framework. This approach formulates the segmentation task innovatively as a boundary regression problem: modeling a highly nonlinear mapping function from substantially diverse M(3) images directly to desired object boundaries. Leveraging the advancement of sparse kernel machines, regression segmentation is fulfilled by a multi-dimensional support vector regressor (MSVR) which operates in an implicit, high dimensional feature space where M(3) diversity and specificity can be systematically categorized, extracted, and handled. The proposed regression segmentation approach was thoroughly tested on images from 113 clinical subjects including both disc and vertebral structures, in both sagittal and axial planes, and from both MRI and CT modalities. The overall result reaches a high dice similarity index (DSI) 0.912 and a low boundary distance (BD) 0.928 mm. With our unified and expendable framework, an efficient clinical tool for M(3) spinal image segmentation can be easily achieved, and will substantially benefit the diagnosis and treatment of spinal diseases.

  2. Regression-kriging for characterizing soils with remotesensing data

    NASA Astrophysics Data System (ADS)

    Ge, Yufeng; Thomasson, J. Alex; Sui, Ruixiu; Wooten, James

    2011-09-01

    In precision agriculture regression has been used widely to quantify the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually violates a basic assumption of regression: sample independence. In this study, a regression-kriging method was attempted in relating soil properties to the remote sensing image of a cotton field near Vance, Mississippi, USA. The regression-kriging model was developed and tested by using 273 soil samples collected from the field. The result showed that by properly incorporating the spatial correlation information of regression residuals, the regression-kriging model generally achieved higher prediction accuracy than the stepwise multiple linear regression model. Most strikingly, a 50% increase in prediction accuracy was shown in soil sodium concentration. Potential usages of regression-kriging in future precision agriculture applications include real-time soil sensor development and digital soil mapping.

  3. Ridge regression processing

    NASA Technical Reports Server (NTRS)

    Kuhl, Mark R.

    1990-01-01

    Current navigation requirements depend on a geometric dilution of precision (GDOP) criterion. As long as the GDOP stays below a specific value, navigation requirements are met. The GDOP will exceed the specified value when the measurement geometry becomes too collinear. A new signal processing technique, called Ridge Regression Processing, can reduce the effects of nearly collinear measurement geometry; thereby reducing the inflation of the measurement errors. It is shown that the Ridge signal processor gives a consistently better mean squared error (MSE) in position than the Ordinary Least Mean Squares (OLS) estimator. The applicability of this technique is currently being investigated to improve the following areas: receiver autonomous integrity monitoring (RAIM), coverage requirements, availability requirements, and precision approaches.

  4. Spatial vulnerability assessments by regression kriging

    NASA Astrophysics Data System (ADS)

    Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor

    2016-04-01

    information representing IEW or GRP forming environmental factors were taken into account to support the spatial inference of the locally experienced IEW frequency and measured GRP values respectively. An efficient spatial prediction methodology was applied to construct reliable maps, namely regression kriging (RK) using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Application of RK also provides the possibility of inherent accuracy assessment. The resulting maps are characterized by global and local measures of its accuracy. Additionally the method enables interval estimation for spatial extension of the areas of predefined risk categories. All of these outputs provide useful contribution to spatial planning, action planning and decision making. Acknowledgement: Our work was partly supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

  5. Union Support Recovery in High-Dimensional Multivariate Regression

    DTIC Science & Technology

    2008-08-01

    view the Lasso as a shrinkage estimator to be compared to traditional least squares or ridge regression ; in this case, it is natural to study the `2... instance , in a hierarchical regression model, groups of regression coefficients may be required to be zero or non-zero in a blockwise manner; for example...Neural Information Processing Systems, 18. MIT Press, Cambridge, MA. Bach, F. (2008). Consistency of the group Lasso and multiple kernel learning

  6. Direct comparison between genomic constitution and flavonoid contents in Allium multiple alien addition lines reveals chromosomal locations of genes related to biosynthesis from dihydrokaempferol to quercetin glucosides in scaly leaf of shallot (Allium cepa L.).

    PubMed

    Masuzaki, S; Shigyo, M; Yamauchi, N

    2006-02-01

    The extrachromosome 5A of shallot (Allium cepa L., genomes AA) has an important role in flavonoid biosynthesis in the scaly leaf of Allium fistulosum-shallot monosomic addition lines (FF+nA). This study deals with the production and biochemical characterisation of A. fistulosum-shallot multiple alien addition lines carrying at least 5A to determine the chromosomal locations of genes for quercetin formation. The multiple alien additions were selected from the crossing between allotriploid FFA (female symbol) and A. fistulosum (male symbol). The 113 plants obtained from this cross were analysed by a chromosome 5A-specific PGI isozyme marker of shallot. Thirty plants were preliminarily selected for an alien addition carrying 5A. The chromosome numbers of the 30 plants varied from 18 to 23. The other extrachromosomes in 19 plants were completely identified by using seven other chromosome markers of shallot. High-performance liquid chromatography analyses of the 19 multiple additions were conducted to identify the flavonoid compounds produced in the scaly leaves. Direct comparisons between the chromosomal constitution and the flavonoid contents of the multiple alien additions revealed that a flavonoid 3'-hydroxylase (F3'H) gene for the synthesis of quercetin from kaempferol was located on 7A and that an anonymous gene involved in the glucosidation of quercetin was on 3A or 4A. As a result of supplemental SCAR analyses by using genomic DNAs from two complete sets of A. fistulosum-shallot monosomic additions, we have assigned F3'H to 7A and flavonol synthase to 4A.

  7. Regression Commonality Analysis: A Technique for Quantitative Theory Building

    ERIC Educational Resources Information Center

    Nimon, Kim; Reio, Thomas G., Jr.

    2011-01-01

    When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…

  8. Quantile Regression in the Study of Developmental Sciences

    ERIC Educational Resources Information Center

    Petscher, Yaacov; Logan, Jessica A. R.

    2014-01-01

    Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of…

  9. Least-Squares Linear Regression and Schrodinger's Cat: Perspectives on the Analysis of Regression Residuals.

    ERIC Educational Resources Information Center

    Hecht, Jeffrey B.

    The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…

  10. Survival analysis and Cox regression.

    PubMed

    Benítez-Parejo, N; Rodríguez del Águila, M M; Pérez-Vicente, S

    2011-01-01

    The data provided by clinical trials are often expressed in terms of survival. The analysis of survival comprises a series of statistical analytical techniques in which the measurements analysed represent the time elapsed between a given exposure and the outcome of a certain event. Despite the name of these techniques, the outcome in question does not necessarily have to be either survival or death, and may be healing versus no healing, relief versus pain, complication versus no complication, relapse versus no relapse, etc. The present article describes the analysis of survival from both a descriptive perspective, based on the Kaplan-Meier estimation method, and in terms of bivariate comparisons using the log-rank statistic. Likewise, a description is provided of the Cox regression models for the study of risk factors or covariables associated to the probability of survival. These models are defined in both simple and multiple forms, and a description is provided of how they are calculated and how the postulates for application are checked - accompanied by illustrating examples with the shareware application R.

  11. 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.

  12. Evaluating differential effects using regression interactions and regression mixture models

    PubMed Central

    Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung

    2015-01-01

    Research increasingly emphasizes understanding differential effects. This paper focuses on understanding regression mixture models, a relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their formulation, and their assumptions are compared using Monte Carlo simulations and real data analysis. The capabilities of regression mixture models are described and specific issues to be addressed when conducting regression mixtures are proposed. The paper aims to clarify the role that regression mixtures can take in the estimation of differential effects and increase awareness of the benefits and potential pitfalls of this approach. Regression mixture models are shown to be a potentially effective exploratory method for finding differential effects when these effects can be defined by a small number of classes of respondents who share a typical relationship between a predictor and an outcome. It is also shown that the comparison between regression mixture models and interactions becomes substantially more complex as the number of classes increases. It is argued that regression interactions are well suited for direct tests of specific hypotheses about differential effects and regression mixtures provide a useful approach for exploring effect heterogeneity given adequate samples and study design. PMID:26556903

  13. Censored partial regression.

    PubMed

    Orbe, Jesus; Ferreira, Eva; Núñez-Antón, Vicente

    2003-01-01

    In this work we study the effect of several covariates on a censored response variable with unknown probability distribution. A semiparametric model is proposed to consider situations where the functional form of the effect of one or more covariates is unknown, as is the case in the application presented in this work. We provide its estimation procedure and, in addition, a bootstrap technique to make inference on the parameters. A simulation study has been carried out to show the good performance of the proposed estimation process and to analyse the effect of the censorship. Finally, we present the results when the methodology is applied to AIDS diagnosed patients.

  14. Role of additional chromosomal changes in the prognostic value of t(4;14) and del(17p) in multiple myeloma: the IFM experience

    PubMed Central

    Hebraud, Benjamin; Magrangeas, Florence; Cleynen, Alice; Lauwers-Cances, Valerie; Chretien, Marie-Lorraine; Hulin, Cyrille; Leleu, Xavier; Yon, Edwige; Marit, Gerald; Karlin, Lionel; Roussel, Murielle; Stoppa, Anne-Marie; Belhadj, Karim; Voillat, Laurent; Garderet, Laurent; Macro, Margaret; Caillot, Denis; Mohty, Mohamad; Facon, Thierry; Moreau, Philippe; Attal, Michel; Munshi, Nikhil; Corre, Jill; Minvielle, Stephane

    2015-01-01

    In multiple myeloma, cytogenetic changes are important predictors of patient outcome. In this setting, the most important changes are deletion 17p, del(17p), and translocation of chromosomes 4 and 14, t(4;14), conferring a poor outcome. However, a certain degree of heterogeneity is observed in the survival of these high-risk patients. We hypothesized that other chromosomal changes may impact the outcome. We retrospectively analyzed a large series of 242 patients displaying either t(4;14) (157 patients) or del(17p) (110 patients), 25 patients presenting both abnormalities, using single nucleotide polymorphism array. In patients with t(4;14), del(1p32), del22q, and >30 chromosomal structural changes negatively impacted progression-free survival (PFS). For overall survival (OS), del(13q14), del(1p32), and the number of chromosomal structural changes worsened the prognosis of patients. For patients with del(17p), del6q worsened the prognosis of patients, whereas trisomy 15 and monosomy 14 were found to have a protective effect on PFS. For OS, del(1p32) worsened the prognosis of patients, whereas having >8 numerical changes was found to have a protective effect on survival. This study, which is the largest series of high-risk patients analyzed with the most modern genomic technique, identified 1 main factor negatively impacting survival: del(1p32). PMID:25636340

  15. Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models

    ERIC Educational Resources Information Center

    Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung

    2015-01-01

    Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…

  16. Error bounds in cascading regressions

    USGS Publications Warehouse

    Karlinger, M.R.; Troutman, B.M.

    1985-01-01

    Cascading regressions is a technique for predicting a value of a dependent variable when no paired measurements exist to perform a standard regression analysis. Biases in coefficients of a cascaded-regression line as well as error variance of points about the line are functions of the correlation coefficient between dependent and independent variables. Although this correlation cannot be computed because of the lack of paired data, bounds can be placed on errors through the required properties of the correlation coefficient. The potential meansquared error of a cascaded-regression prediction can be large, as illustrated through an example using geomorphologic data. ?? 1985 Plenum Publishing Corporation.

  17. 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.

  18. On regression adjustment for the propensity score.

    PubMed

    Vansteelandt, S; Daniel, R M

    2014-10-15

    Propensity scores are widely adopted in observational research because they enable adjustment for high-dimensional confounders without requiring models for their association with the outcome of interest. The results of statistical analyses based on stratification, matching or inverse weighting by the propensity score are therefore less susceptible to model extrapolation than those based solely on outcome regression models. This is attractive because extrapolation in outcome regression models may be alarming, yet difficult to diagnose, when the exposed and unexposed individuals have very different covariate distributions. Standard regression adjustment for the propensity score forms an alternative to the aforementioned propensity score methods, but the benefits of this are less clear because it still involves modelling the outcome in addition to the propensity score. In this article, we develop novel insights into the properties of this adjustment method. We demonstrate that standard tests of the null hypothesis of no exposure effect (based on robust variance estimators), as well as particular standardised effects obtained from such adjusted regression models, are robust against misspecification of the outcome model when a propensity score model is correctly specified; they are thus not vulnerable to the aforementioned problem of extrapolation. We moreover propose efficient estimators for these standardised effects, which retain a useful causal interpretation even when the propensity score model is misspecified, provided the outcome regression model is correctly specified.

  19. Logistic Regression: Concept and Application

    ERIC Educational Resources Information Center

    Cokluk, Omay

    2010-01-01

    The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…

  20. Rank regression: an alternative regression approach for data with outliers.

    PubMed

    Chen, Tian; Tang, Wan; Lu, Ying; Tu, Xin

    2014-10-01

    Linear regression models are widely used in mental health and related health services research. However, the classic linear regression analysis assumes that the data are normally distributed, an assumption that is not met by the data obtained in many studies. One method of dealing with this problem is to use semi-parametric models, which do not require that the data be normally distributed. But semi-parametric models are quite sensitive to outlying observations, so the generated estimates are unreliable when study data includes outliers. In this situation, some researchers trim the extreme values prior to conducting the analysis, but the ad-hoc rules used for data trimming are based on subjective criteria so different methods of adjustment can yield different results. Rank regression provides a more objective approach to dealing with non-normal data that includes outliers. This paper uses simulated and real data to illustrate this useful regression approach for dealing with outliers and compares it to the results generated using classical regression models and semi-parametric regression models.

  1. Matrix Multiplication Algorithm Selection with Support Vector Machines

    DTIC Science & Technology

    2015-05-01

    approaches. However, most literature on this topic focuses on autotuning for templated code optimization problems using regression models [7]. Our...evaluate additional linear algebra algorithms such as QR decomposition and sparse matrix multiplication. Further- more, we are interested in gathering

  2. A tutorial on Bayesian Normal linear regression

    NASA Astrophysics Data System (ADS)

    Klauenberg, Katy; Wübbeler, Gerd; Mickan, Bodo; Harris, Peter; Elster, Clemens

    2015-12-01

    Regression is a common task in metrology and often applied to calibrate instruments, evaluate inter-laboratory comparisons or determine fundamental constants, for example. Yet, a regression model cannot be uniquely formulated as a measurement function, and consequently the Guide to the Expression of Uncertainty in Measurement (GUM) and its supplements are not applicable directly. Bayesian inference, however, is well suited to regression tasks, and has the advantage of accounting for additional a priori information, which typically robustifies analyses. Furthermore, it is anticipated that future revisions of the GUM shall also embrace the Bayesian view. Guidance on Bayesian inference for regression tasks is largely lacking in metrology. For linear regression models with Gaussian measurement errors this tutorial gives explicit guidance. Divided into three steps, the tutorial first illustrates how a priori knowledge, which is available from previous experiments, can be translated into prior distributions from a specific class. These prior distributions have the advantage of yielding analytical, closed form results, thus avoiding the need to apply numerical methods such as Markov Chain Monte Carlo. Secondly, formulas for the posterior results are given, explained and illustrated, and software implementations are provided. In the third step, Bayesian tools are used to assess the assumptions behind the suggested approach. These three steps (prior elicitation, posterior calculation, and robustness to prior uncertainty and model adequacy) are critical to Bayesian inference. The general guidance given here for Normal linear regression tasks is accompanied by a simple, but real-world, metrological example. The calibration of a flow device serves as a running example and illustrates the three steps. It is shown that prior knowledge from previous calibrations of the same sonic nozzle enables robust predictions even for extrapolations.

  3. Learning regulatory programs by threshold SVD regression.

    PubMed

    Ma, Xin; Xiao, Luo; Wong, Wing Hung

    2014-11-04

    We formulate a statistical model for the regulation of global gene expression by multiple regulatory programs and propose a thresholding singular value decomposition (T-SVD) regression method for learning such a model from data. Extensive simulations demonstrate that this method offers improved computational speed and higher sensitivity and specificity over competing approaches. The method is used to analyze microRNA (miRNA) and long noncoding RNA (lncRNA) data from The Cancer Genome Atlas (TCGA) consortium. The analysis yields previously unidentified insights into the combinatorial regulation of gene expression by noncoding RNAs, as well as findings that are supported by evidence from the literature.

  4. Modeling confounding by half-sibling regression

    PubMed Central

    Schölkopf, Bernhard; Hogg, David W.; Wang, Dun; Foreman-Mackey, Daniel; Janzing, Dominik; Simon-Gabriel, Carl-Johann; Peters, Jonas

    2016-01-01

    We describe a method for removing the effect of confounders to reconstruct a latent quantity of interest. The method, referred to as “half-sibling regression,” is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification, discussing both independent and identically distributed as well as time series data, respectively, and illustrate the potential of the method in a challenging astronomy application. PMID:27382154

  5. Modeling confounding by half-sibling regression.

    PubMed

    Schölkopf, Bernhard; Hogg, David W; Wang, Dun; Foreman-Mackey, Daniel; Janzing, Dominik; Simon-Gabriel, Carl-Johann; Peters, Jonas

    2016-07-05

    We describe a method for removing the effect of confounders to reconstruct a latent quantity of interest. The method, referred to as "half-sibling regression," is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification, discussing both independent and identically distributed as well as time series data, respectively, and illustrate the potential of the method in a challenging astronomy application.

  6. Food additives

    PubMed Central

    Spencer, Michael

    1974-01-01

    Food additives are discussed from the food technology point of view. The reasons for their use are summarized: (1) to protect food from chemical and microbiological attack; (2) to even out seasonal supplies; (3) to improve their eating quality; (4) to improve their nutritional value. The various types of food additives are considered, e.g. colours, flavours, emulsifiers, bread and flour additives, preservatives, and nutritional additives. The paper concludes with consideration of those circumstances in which the use of additives is (a) justified and (b) unjustified. PMID:4467857

  7. Model building in nonproportional hazard regression.

    PubMed

    Rodríguez-Girondo, Mar; Kneib, Thomas; Cadarso-Suárez, Carmen; Abu-Assi, Emad

    2013-12-30

    Recent developments of statistical methods allow for a very flexible modeling of covariates affecting survival times via the hazard rate, including also the inspection of possible time-dependent associations. Despite their immediate appeal in terms of flexibility, these models typically introduce additional difficulties when a subset of covariates and the corresponding modeling alternatives have to be chosen, that is, for building the most suitable model for given data. This is particularly true when potentially time-varying associations are given. We propose to conduct a piecewise exponential representation of the original survival data to link hazard regression with estimation schemes based on of the Poisson likelihood to make recent advances for model building in exponential family regression accessible also in the nonproportional hazard regression context. A two-stage stepwise selection approach, an approach based on doubly penalized likelihood, and a componentwise functional gradient descent approach are adapted to the piecewise exponential regression problem. These three techniques were compared via an intensive simulation study. An application to prognosis after discharge for patients who suffered a myocardial infarction supplements the simulation to demonstrate the pros and cons of the approaches in real data analyses.

  8. Functional Generalized Additive Models.

    PubMed

    McLean, Mathew W; Hooker, Giles; Staicu, Ana-Maria; Scheipl, Fabian; Ruppert, David

    2014-01-01

    We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F{X(t), t} where F(·,·) is an unknown regression function and X(t) is a functional covariate. Rather than having an additive model in a finite number of principal components as in Müller and Yao (2008), our model incorporates the functional predictor directly and thus our model can be viewed as the natural functional extension of generalized additive models. We estimate F(·,·) using tensor-product B-splines with roughness penalties. A pointwise quantile transformation of the functional predictor is also considered to ensure each tensor-product B-spline has observed data on its support. The methods are evaluated using simulated data and their predictive performance is compared with other competing scalar-on-function regression alternatives. We illustrate the usefulness of our approach through an application to brain tractography, where X(t) is a signal from diffusion tensor imaging at position, t, along a tract in the brain. In one example, the response is disease-status (case or control) and in a second example, it is the score on a cognitive test. R code for performing the simulations and fitting the FGAM can be found in supplemental materials available online.

  9. The Selection of Variables in Multiple Regression Analysis

    ERIC Educational Resources Information Center

    Halinski, Ronald S.; Feldt, Leonard S.

    1970-01-01

    Four commonly employed procedures were repeatedly applied to computer-simulated samples to provide comparative data pertaining to two questions: (a) which procedure can be expected to produce and equation that yields the most accurate predictions for the population, and (b) which procedure is most likely to identify the optimal set of independent…

  10. Cross Validation of Selection of Variables in Multiple Regression.

    DTIC Science & Technology

    1979-12-01

    Bomber IBMNAV * BOMNAV Navigation-Cargo * * CARNAV Sensory-Fighter * SF FGTSEN Sensory - Bomber * SB BOMSEN Communication - Fighter IFGCOM CF FGTCOM...of Variables Variable No. Recode FGTNAV 1 0 LESS THAN 1 1 OR OVER BONNAV 2 0 LESS THAN S1 OR OVER CARNAV 3 0 LESS THAN S1 OR OVER FGTSEN 4 0 LESS THAN...cc x x x x x x x CARNAV X X X X X X x XMTR x X X X X x PD X x X X X X UP x- *Those which AID determined. 44 This value was lowered to 3 in the

  11. Using multiple linear regression model to estimate thunderstorm activity

    NASA Astrophysics Data System (ADS)

    Suparta, W.; Putro, W. S.

    2017-03-01

    This paper is aimed to develop a numerical model with the use of a nonlinear model to estimate the thunderstorm activity. Meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), cloud (C), Precipitable Water Vapor (PWV), and precipitation on a daily basis were used in the proposed method. The model was constructed with six configurations of input and one target output. The output tested in this work is the thunderstorm event when one-year data is used. Results showed that the model works well in estimating thunderstorm activities with the maximum epoch reaching 1000 iterations and the percent error was found below 50%. The model also found that the thunderstorm activities in May and October are detected higher than the other months due to the inter-monsoon season.

  12. Interpreting Multiple Logistic Regression Coefficients in Prospective Observational Studies

    DTIC Science & Technology

    1982-11-01

    prompted close examination of the issue at a workshop on hypertriglyceridemia where some of the cautions and perspectives given in this paper were...longevity," Circulation, 34, 679-697, (1966). 19. Lippel, K., Tyroler, H., Eder, H., Gotto, A., and Vahouny, G. "Rela- tionship of hypertriglyceridemia

  13. Estimating peak flow characteristics at ungaged sites by ridge regression

    USGS Publications Warehouse

    Tasker, Gary D.

    1982-01-01

    A regression simulation model, is combined with a multisite streamflow generator to simulate a regional regression of 50-year peak discharge against a set of basin characteristics. Monte Carlo experiments are used to compare the unbiased ordinary lease squares parameter estimator with Hoerl and Kennard's (1970a) ridge estimator in which the biasing parameter is that proposed by Hoerl, Kennard, and Baldwin (1975). The simulation results indicate a substantial improvement in parameter estimation using ridge regression when the correlation between basin characteristics is more than about 0.90. In addition, results indicate a strong potential for improving the mean square error of prediction of a peak-flow characteristic versus basin characteristics regression model when the basin characteristics are approximately colinear. The simulation covers a range of regression parameters, streamflow statistics, and basin characteristics commonly found in regional regression studies.

  14. An Explanation of the Effectiveness of Latent Semantic Indexing by Means of a Bayesian Regression Model.

    ERIC Educational Resources Information Center

    Story, Roger E.

    1996-01-01

    Discussion of the use of Latent Semantic Indexing to determine relevancy in information retrieval focuses on statistical regression and Bayesian methods. Topics include keyword searching; a multiple regression model; how the regression model can aid search methods; and limitations of this approach, including complexity, linearity, and…

  15. Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis

    ERIC Educational Resources Information Center

    Kim, Rae Seon

    2011-01-01

    When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…

  16. Demonstration of a Fiber Optic Regression Probe

    NASA Technical Reports Server (NTRS)

    Korman, Valentin; Polzin, Kurt A.

    2010-01-01

    The capability to provide localized, real-time monitoring of material regression rates in various applications has the potential to provide a new stream of data for development testing of various components and systems, as well as serving as a monitoring tool in flight applications. These applications include, but are not limited to, the regression of a combusting solid fuel surface, the ablation of the throat in a chemical rocket or the heat shield of an aeroshell, and the monitoring of erosion in long-life plasma thrusters. The rate of regression in the first application is very fast, while the second and third are increasingly slower. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor is optical, using two different, co-located fiber-optics to perform the regression measurement. The disparate optical transmission properties of the two fiber-optics makes it possible to measure the regression rate by monitoring the relative light attenuation through the fibers. As the fibers regress along with the parent material in which they are embedded, the relative light intensities through the two fibers changes, providing a measure of the regression rate. The optical nature of the system makes it relatively easy to use in a variety of harsh, high temperature environments, and it is also unaffected by the presence of electric and magnetic fields. In addition, the sensor could be used to perform optical spectroscopy on the light emitted by a process and collected by fibers, giving localized measurements of various properties. The capability to perform an in-situ measurement of material regression rates is useful in addressing a variety of physical issues in various applications. An in-situ measurement allows for real-time data regarding the erosion rates, providing a quick method for

  17. Regional Regression Equations to Estimate Flow-Duration Statistics at Ungaged Stream Sites in Connecticut

    USGS Publications Warehouse

    Ahearn, Elizabeth A.

    2010-01-01

    Multiple linear regression equations for determining flow-duration statistics were developed to estimate select flow exceedances ranging from 25- to 99-percent for six 'bioperiods'-Salmonid Spawning (November), Overwinter (December-February), Habitat Forming (March-April), Clupeid Spawning (May), Resident Spawning (June), and Rearing and Growth (July-October)-in Connecticut. Regression equations also were developed to estimate the 25- and 99-percent flow exceedances without reference to a bioperiod. In total, 32 equations were developed. The predictive equations were based on regression analyses relating flow statistics from streamgages to GIS-determined basin and climatic characteristics for the drainage areas of those streamgages. Thirty-nine streamgages (and an additional 6 short-term streamgages and 28 partial-record sites for the non-bioperiod 99-percent exceedance) in Connecticut and adjacent areas of neighboring States were used in the regression analysis. Weighted least squares regression analysis was used to determine the predictive equations; weights were assigned based on record length. The basin characteristics-drainage area, percentage of area with coarse-grained stratified deposits, percentage of area with wetlands, mean monthly precipitation (November), mean seasonal precipitation (December, January, and February), and mean basin elevation-are used as explanatory variables in the equations. Standard errors of estimate of the 32 equations ranged from 10.7 to 156 percent with medians of 19.2 and 55.4 percent to predict the 25- and 99-percent exceedances, respectively. Regression equations to estimate high and median flows (25- to 75-percent exceedances) are better predictors (smaller variability of the residual values around the regression line) than the equations to estimate low flows (less than 75-percent exceedance). The Habitat Forming (March-April) bioperiod had the smallest standard errors of estimate, ranging from 10.7 to 20.9 percent. In

  18. Regressive evolution in Astyanax cavefish.

    PubMed

    Jeffery, William R

    2009-01-01

    A diverse group of animals, including members of most major phyla, have adapted to life in the perpetual darkness of caves. These animals are united by the convergence of two regressive phenotypes, loss of eyes and pigmentation. The mechanisms of regressive evolution are poorly understood. The teleost Astyanax mexicanus is of special significance in studies of regressive evolution in cave animals. This species includes an ancestral surface dwelling form and many con-specific cave-dwelling forms, some of which have evolved their recessive phenotypes independently. Recent advances in Astyanax development and genetics have provided new information about how eyes and pigment are lost during cavefish evolution; namely, they have revealed some of the molecular and cellular mechanisms involved in trait modification, the number and identity of the underlying genes and mutations, the molecular basis of parallel evolution, and the evolutionary forces driving adaptation to the cave environment.

  19. Food additives

    MedlinePlus

    ... or natural. Natural food additives include: Herbs or spices to add flavor to foods Vinegar for pickling ... Certain colors improve the appearance of foods. Many spices, as well as natural and man-made flavors, ...

  20. Moderation analysis using a two-level regression model.

    PubMed

    Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

    2014-10-01

    Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

  1. Weighting Regressions by Propensity Scores

    ERIC Educational Resources Information Center

    Freedman, David A.; Berk, Richard A.

    2008-01-01

    Regressions can be weighted by propensity scores in order to reduce bias. However, weighting is likely to increase random error in the estimates, and to bias the estimated standard errors downward, even when selection mechanisms are well understood. Moreover, in some cases, weighting will increase the bias in estimated causal parameters. If…

  2. Quantile Regression with Censored Data

    ERIC Educational Resources Information Center

    Lin, Guixian

    2009-01-01

    The Cox proportional hazards model and the accelerated failure time model are frequently used in survival data analysis. They are powerful, yet have limitation due to their model assumptions. Quantile regression offers a semiparametric approach to model data with possible heterogeneity. It is particularly powerful for censored responses, where the…

  3. Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models

    ERIC Educational Resources Information Center

    Anderson, Carolyn J.; Verkuilen, Jay; Peyton, Buddy L.

    2010-01-01

    Survey items with multiple response categories and multiple-choice test questions are ubiquitous in psychological and educational research. We illustrate the use of log-multiplicative association (LMA) models that are extensions of the well-known multinomial logistic regression model for multiple dependent outcome variables to reanalyze a set of…

  4. Neither fixed nor random: weighted least squares meta-regression.

    PubMed

    Stanley, T D; Doucouliagos, Hristos

    2017-03-01

    Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the 'true' regression coefficient. Copyright © 2016 John Wiley & Sons, Ltd.

  5. Hierarchical Adaptive Regression Kernels for Regression with Functional Predictors

    PubMed Central

    Woodard, Dawn B.; Crainiceanu, Ciprian; Ruppert, David

    2013-01-01

    We propose a new method for regression using a parsimonious and scientifically interpretable representation of functional predictors. Our approach is designed for data that exhibit features such as spikes, dips, and plateaus whose frequency, location, size, and shape varies stochastically across subjects. We propose Bayesian inference of the joint functional and exposure models, and give a method for efficient computation. We contrast our approach with existing state-of-the-art methods for regression with functional predictors, and show that our method is more effective and efficient for data that include features occurring at varying locations. We apply our methodology to a large and complex dataset from the Sleep Heart Health Study, to quantify the association between sleep characteristics and health outcomes. Software and technical appendices are provided in online supplemental materials. PMID:24293988

  6. Regression Verification Using Impact Summaries

    NASA Technical Reports Server (NTRS)

    Backes, John; Person, Suzette J.; Rungta, Neha; Thachuk, Oksana

    2013-01-01

    Regression verification techniques are used to prove equivalence of syntactically similar programs. Checking equivalence of large programs, however, can be computationally expensive. Existing regression verification techniques rely on abstraction and decomposition techniques to reduce the computational effort of checking equivalence of the entire program. These techniques are sound but not complete. In this work, we propose a novel approach to improve scalability of regression verification by classifying the program behaviors generated during symbolic execution as either impacted or unimpacted. Our technique uses a combination of static analysis and symbolic execution to generate summaries of impacted program behaviors. The impact summaries are then checked for equivalence using an o-the-shelf decision procedure. We prove that our approach is both sound and complete for sequential programs, with respect to the depth bound of symbolic execution. Our evaluation on a set of sequential C artifacts shows that reducing the size of the summaries can help reduce the cost of software equivalence checking. Various reduction, abstraction, and compositional techniques have been developed to help scale software verification techniques to industrial-sized systems. Although such techniques have greatly increased the size and complexity of systems that can be checked, analysis of large software systems remains costly. Regression analysis techniques, e.g., regression testing [16], regression model checking [22], and regression verification [19], restrict the scope of the analysis by leveraging the differences between program versions. These techniques are based on the idea that if code is checked early in development, then subsequent versions can be checked against a prior (checked) version, leveraging the results of the previous analysis to reduce analysis cost of the current version. Regression verification addresses the problem of proving equivalence of closely related program

  7. Potlining Additives

    SciTech Connect

    Rudolf Keller

    2004-08-10

    In this project, a concept to improve the performance of aluminum production cells by introducing potlining additives was examined and tested. Boron oxide was added to cathode blocks, and titanium was dissolved in the metal pool; this resulted in the formation of titanium diboride and caused the molten aluminum to wet the carbonaceous cathode surface. Such wetting reportedly leads to operational improvements and extended cell life. In addition, boron oxide suppresses cyanide formation. This final report presents and discusses the results of this project. Substantial economic benefits for the practical implementation of the technology are projected, especially for modern cells with graphitized blocks. For example, with an energy savings of about 5% and an increase in pot life from 1500 to 2500 days, a cost savings of $ 0.023 per pound of aluminum produced is projected for a 200 kA pot.

  8. Phosphazene additives

    DOEpatents

    Harrup, Mason K; Rollins, Harry W

    2013-11-26

    An additive comprising a phosphazene compound that has at least two reactive functional groups and at least one capping functional group bonded to phosphorus atoms of the phosphazene compound. One of the at least two reactive functional groups is configured to react with cellulose and the other of the at least two reactive functional groups is configured to react with a resin, such as an amine resin of a polycarboxylic acid resin. The at least one capping functional group is selected from the group consisting of a short chain ether group, an alkoxy group, or an aryloxy group. Also disclosed are an additive-resin admixture, a method of treating a wood product, and a wood product.

  9. Embedded Sensors for Measuring Surface Regression

    NASA Technical Reports Server (NTRS)

    Gramer, Daniel J.; Taagen, Thomas J.; Vermaak, Anton G.

    2006-01-01

    non-eroding end of the sensor. The sensor signal can be transmitted from inside a high-pressure chamber to the ambient environment, using commercially available feedthrough connectors. Miniaturized internal recorders or wireless data transmission could also potentially be employed to eliminate the need for producing penetrations in the chamber case. The rungs are designed so that as each successive rung is eroded away, the resistance changes by an amount that yields a readily measurable signal larger than the background noise. (In addition, signal-conditioning techniques are used in processing the resistance readings to mitigate the effect of noise.) Hence, each discrete change of resistance serves to indicate the arrival of the regressing host material front at the known depth of the affected resistor rung. The average rate of regression between two adjacent resistors can be calculated simply as the distance between the resistors divided by the time interval between their resistance jumps. Advanced data reduction techniques have also been developed to establish the instantaneous surface position and regression rate when the regressing front is between rungs.

  10. Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.

    ERIC Educational Resources Information Center

    Smith, Kent W.; Sasaki, M. S.

    1979-01-01

    A method is proposed for overcoming the problem of multicollinearity in multiple regression equations where multiplicative independent terms are entered. The method is not a ridge regression solution. (JKS)

  11. Regression Analysis with Dummy Variables: Use and Interpretation.

    ERIC Educational Resources Information Center

    Hinkle, Dennis E.; Oliver, J. Dale

    1986-01-01

    Multiple regression analysis (MRA) may be used when both continuous and categorical variables are included as independent research variables. The use of MRA with categorical variables involves dummy coding, that is, assigning zeros and ones to levels of categorical variables. Caution is urged in results interpretation. (Author/CH)

  12. An Empirical Study of Eight Nonparametric Tests in Hierarchical Regression.

    ERIC Educational Resources Information Center

    Harwell, Michael; Serlin, Ronald C.

    When normality does not hold, nonparametric tests represent an important data-analytic alternative to parametric tests. However, the use of nonparametric tests in educational research has been limited by the absence of easily performed tests for complex experimental designs and analyses, such as factorial designs and multiple regression analyses,…

  13. Assessing Longitudinal Change: Adjustment for Regression to the Mean Effects

    ERIC Educational Resources Information Center

    Rocconi, Louis M.; Ethington, Corinna A.

    2009-01-01

    Pascarella (J Coll Stud Dev 47:508-520, 2006) has called for an increase in use of longitudinal data with pretest-posttest design when studying effects on college students. However, such designs that use multiple measures to document change are vulnerable to an important threat to internal validity, regression to the mean. Herein, we discuss a…

  14. Spontaneous regression of congenital cutaneous hemangiomas in a calf.

    PubMed

    Priestnall, S L; De Bellis, F; Bond, R; Alony-Gilboa, Y; Summers, B A

    2010-03-01

    Congenital vascular tumors of the skin have been described in people and a few animals, but unlike infantile hemangiomas in children, spontaneous regression has not been described in animals. A 2-day-old male Belgian Blue cross calf was presented for multiple congenital cutaneous masses that were soft, alopecic, and hyperemic; the calf had no other apparent abnormalities. Two weeks later, one mass had regressed. Surgical excision of one of the remaining masses was performed; histopathologic and immunohistochemical findings were considered diagnostic for epithelioid hemangioma. Eight months following initial presentation, all the masses had regressed spontaneously. This constitutes the first account in the veterinary literature of spontaneous regression in a congenital vascular tumor.

  15. Estimating the exceedance probability of rain rate by logistic regression

    NASA Technical Reports Server (NTRS)

    Chiu, Long S.; Kedem, Benjamin

    1990-01-01

    Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

  16. Regression Models For Saffron Yields in Iran

    NASA Astrophysics Data System (ADS)

    S. H, Sanaeinejad; S. N, Hosseini

    Saffron is an important crop in social and economical aspects in Khorassan Province (Northeast of Iran). In this research wetried to evaluate trends of saffron yield in recent years and to study the relationship between saffron yield and the climate change. A regression analysis was used to predict saffron yield based on 20 years of yield data in Birjand, Ghaen and Ferdows cities.Climatologically data for the same periods was provided by database of Khorassan Climatology Center. Climatologically data includedtemperature, rainfall, relative humidity and sunshine hours for ModelI, and temperature and rainfall for Model II. The results showed the coefficients of determination for Birjand, Ferdows and Ghaen for Model I were 0.69, 0.50 and 0.81 respectively. Also coefficients of determination for the same cities for model II were 0.53, 0.50 and 0.72 respectively. Multiple regression analysisindicated that among weather variables, temperature was the key parameter for variation ofsaffron yield. It was concluded that increasing temperature at spring was the main cause of declined saffron yield during recent years across the province. Finally, yield trend was predicted for the last 5 years using time series analysis.

  17. Shape regression for vertebra fracture quantification

    NASA Astrophysics Data System (ADS)

    Lund, Michael Tillge; de Bruijne, Marleen; Tanko, Laszlo B.; Nielsen, Mads

    2005-04-01

    Accurate and reliable identification and quantification of vertebral fractures constitute a challenge both in clinical trials and in diagnosis of osteoporosis. Various efforts have been made to develop reliable, objective, and reproducible methods for assessing vertebral fractures, but at present there is no consensus concerning a universally accepted diagnostic definition of vertebral fractures. In this project we want to investigate whether or not it is possible to accurately reconstruct the shape of a normal vertebra, using a neighbouring vertebra as prior information. The reconstructed shape can then be used to develop a novel vertebra fracture measure, by comparing the segmented vertebra shape with its reconstructed normal shape. The vertebrae in lateral x-rays of the lumbar spine were manually annotated by a medical expert. With this dataset we built a shape model, with equidistant point distribution between the four corner points. Based on the shape model, a multiple linear regression model of a normal vertebra shape was developed for each dataset using leave-one-out cross-validation. The reconstructed shape was calculated for each dataset using these regression models. The average prediction error for the annotated shape was on average 3%.

  18. Multivariate Alternatives to Regression Analysis in the Evaluation of Salary Equity-Parity. AIR 1983 Annual Forum Paper.

    ERIC Educational Resources Information Center

    Carter, Richard D.; And Others

    The use of canonical analysis and multiple discriminant analysis to analyze equity-parity in colleges and universities is assessed and distinguished from multiple regression analysis. Multiple regression analysis forces the variable weights throughout the salary structure to be uniform, permits only one criterion or dependent variable to be…

  19. Regression analysis of cytopathological data

    SciTech Connect

    Whittemore, A.S.; McLarty, J.W.; Fortson, N.; Anderson, K.

    1982-12-01

    Epithelial cells from the human body are frequently labelled according to one of several ordered levels of abnormality, ranging from normal to malignant. The label of the most abnormal cell in a specimen determines the score for the specimen. This paper presents a model for the regression of specimen scores against continuous and discrete variables, as in host exposure to carcinogens. Application to data and tests for adequacy of model fit are illustrated using sputum specimens obtained from a cohort of former asbestos workers.

  20. A rotor optimization using regression analysis

    NASA Technical Reports Server (NTRS)

    Giansante, N.

    1984-01-01

    The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.

  1. Multiatlas segmentation as nonparametric regression.

    PubMed

    Awate, Suyash P; Whitaker, Ross T

    2014-09-01

    This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.

  2. Multiatlas Segmentation as Nonparametric Regression

    PubMed Central

    Awate, Suyash P.; Whitaker, Ross T.

    2015-01-01

    This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator’s convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems. PMID:24802528

  3. 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

  4. Isotope labeling studies on the formation of multiple addition products of alanine in the pyrolysis residue of glucose/alanine mixtures by high-resolution ESI-TOF-MS.

    PubMed

    Chu, Fong Lam; Sleno, Lekha; Yaylayan, Varoujan A

    2011-11-09

    Pyrolysis was used as a microscale sample preparation tool to generate glucose/alanine reaction products to minimize the use of expensive labeled precursors in isotope labeling studies. The residue remaining after the pyrolysis at 250 °C was analyzed by electrospray time-of-flight mass spectrometry (ESI-TOF-MS). It was observed that a peak at m/z 199.1445 in the ESI-TOF-MS spectrum appeared only when the model system contained at least 2-fold excess alanine. The accurate mass determination indeed indicated the presence of two nitrogen atoms in the molecular formula (C(10)H(18)N(2)O(2)). To verify the origin of the carbon atoms in this unknown compound, model studies with [(13)U(6)]glucose, [(13)C-1]alanine, [(13)C-2]alanine, [(13)C-3]alanine, and [(15)N]alanine were also performed. Glucose furnished six carbon atoms, and alanine provides four carbon (2 × C-2 and 2 × C-3) and two nitrogen atoms. When commercially available fructosylalanine (N-attached to C-1) was reacted with only 1 mol of alanine, a peak at m/z 199.1445 was once again observed. In addition, when 3-deoxyglucosone (3-DG) was reacted with a 2-fold excess of alanine, a peak at m/z 199.1433 was also generated, confirming the points of attachment of the two amino acids at C-1 and C-2 atoms of 3-DG. These studies have indicated that amino acids can undergo multiple addition reactions with 1,2-dicarbonyl compounds such as 3-deoxyglucosone and eventually form a tetrahydropyrazine moiety.

  5. Oxaliplatin and Infliximab Combination Synergizes in Inducing Colon Cancer Regression

    PubMed Central

    Li, Wenya; Xu, Jian; Zhao, Jian; Zhang, Rui

    2017-01-01

    Background Colon cancer is one of the most common malignant cancers and causes millions of deaths each year. There are still no effective treatments for colon cancer patients who are at advanced stage. Tumor necrosis factor-alpha (TNF-α) might be a good therapy target due to its widely-accepted roles in regulating multiple important biological processes, especially in promoting inflammation. Material/Methods We evaluated the expression of TNF-α in 108 human colon cancer tissue samples and 2 colon cancer cell lines (CT26 and HCT116), and analyzed its prognostic values. Further, we explored the roles and mechanism of anti-TNF-α treatment in combination with chemotherapy in vitro and in vivo. Results We found that TNF-α was highly expressed in colon cancer cell lines. The survival analysis and Cox regression analysis indicated that high TNF-α was an independent adverse prognosticator of colon cancer. In addition, anti-TNF-α treatment enhanced the effects of chemotherapy in the xenograft mouse model through inducing ADCC and CDC effects. Conclusions We conclude that TNF-α is an independent adverse prognosticator of colon cancer, and anti-TNF-α might benefit colon cancer patients. PMID:28190020

  6. Multiple EDAS (encephalo-duro-arterio-synangiosis). Additional EDAS using the frontal branch of the superficial temporal artery (STA) and the occipital artery for pediatric moyamoya patients in whom EDAS using the parietal branch of STA was insufficient.

    PubMed

    Tenjin, H; Ueda, S

    1997-04-01

    Although parietal EDAS or STA-MCA anastomosis are effective in pediatric moyamoya disease, they do not adequately prevent ischemia in the frontal and occipital lobes. Some additional methods that can prevent ischemia in the frontal and occipital lobes are sometimes needed. We investigated whether EDAS using a frontal branch of the superficial temporal artery (frontal EDAS) or EDAS using the occipital artery (occipital EDAS) is preferable. Frontal or occipital EDAS was performed at 15 sites in seven patients with pediatric moyamoya disease. The outcome was estimated by angiography 3 months later, CT findings 3 months later, neurological findings during the follow up period and perioperative complications. The mean follow up period was 14 +/- 6 months after frontal or occipital EDAS. As results, good revascularization from frontal or occipital EDAS was shown in ten of fourteen surgical sites (71%) in angiography. None of the patients showed deterioration of symptoms after frontal or occipital EDAS during the follow up period. None of the patients developed surgical complications. In conclusion, multiple EDAS using the frontal branch of STA and the occipital artery is an effective and safe method for preventing ischemia in the frontal and occipital lobe in pediatric moyamoya disease.

  7. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

    PubMed

    Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

    2009-11-01

    G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

  8. Regression Splines with Longitudinal Data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In many clinical trial studies, patients are observed and/or measured on multiple occasions. To account for the longitudinal nature of the data, a mixed model analysis implemented using SAS PROC MIXED is commonly used. It is typical to make comparisons between dose or treatment groups, possibly cont...

  9. Recognition of caudal regression syndrome.

    PubMed

    Boulas, Mari M

    2009-04-01

    Caudal regression syndrome, also referred to as caudal dysplasia and sacral agenesis syndrome, is a rare congenital malformation characterized by varying degrees of developmental failure early in gestation. It involves the lower extremities, the lumbar and coccygeal vertebrae, and corresponding segments of the spinal cord. This is a rare disorder, and true pathogenesis is unclear. The etiology is thought to be related to maternal diabetes, genetic predisposition, and vascular hypoperfusion, but no true causative factor has been determined. Fetal diagnostic tools allow for early recognition of the syndrome, and careful examination of the newborn is essential to determine the extent of the disorder. Associated organ system dysfunction depends on the severity of the disease. Related defects are structural, and systematic problems including respiratory, cardiac, gastrointestinal, urinary, orthopedic, and neurologic can be present in varying degrees of severity and in different combinations. A multidisciplinary approach to management is crucial. Because the primary pathology is irreversible, treatment is only supportive.

  10. Lumbar herniated disc: spontaneous regression

    PubMed Central

    Yüksel, Kasım Zafer

    2017-01-01

    Background Low back pain is a frequent condition that results in substantial disability and causes admission of patients to neurosurgery clinics. To evaluate and present the therapeutic outcomes in lumbar disc hernia (LDH) patients treated by means of a conservative approach, consisting of bed rest and medical therapy. Methods This retrospective cohort was carried out in the neurosurgery departments of hospitals in Kahramanmaraş city and 23 patients diagnosed with LDH at the levels of L3−L4, L4−L5 or L5−S1 were enrolled. Results The average age was 38.4 ± 8.0 and the chief complaint was low back pain and sciatica radiating to one or both lower extremities. Conservative treatment was administered. Neurological examination findings, durations of treatment and intervals until symptomatic recovery were recorded. Laségue tests and neurosensory examination revealed that mild neurological deficits existed in 16 of our patients. Previously, 5 patients had received physiotherapy and 7 patients had been on medical treatment. The number of patients with LDH at the level of L3−L4, L4−L5, and L5−S1 were 1, 13, and 9, respectively. All patients reported that they had benefit from medical treatment and bed rest, and radiologic improvement was observed simultaneously on MRI scans. The average duration until symptomatic recovery and/or regression of LDH symptoms was 13.6 ± 5.4 months (range: 5−22). Conclusions It should be kept in mind that lumbar disc hernias could regress with medical treatment and rest without surgery, and there should be an awareness that these patients could recover radiologically. This condition must be taken into account during decision making for surgical intervention in LDH patients devoid of indications for emergent surgery. PMID:28119770

  11. Scientific Progress or Regress in Sports Physiology?

    PubMed

    Böning, Dieter

    2016-11-01

    In modern societies there is strong belief in scientific progress, but, unfortunately, a parallel partial regress occurs because of often avoidable mistakes. Mistakes are mainly forgetting, erroneous theories, errors in experiments and manuscripts, prejudice, selected publication of "positive" results, and fraud. An example of forgetting is that methods introduced decades ago are used without knowing the underlying theories: Basic articles are no longer read or cited. This omission may cause incorrect interpretation of results. For instance, false use of actual base excess instead of standard base excess for calculation of the number of hydrogen ions leaving the muscles raised the idea that an unknown fixed acid is produced in addition to lactic acid during exercise. An erroneous theory led to the conclusion that lactate is not the anion of a strong acid but a buffer. Mistakes occur after incorrect application of a method, after exclusion of unwelcome values, during evaluation of measurements by false calculations, or during preparation of manuscripts. Co-authors, as well as reviewers, do not always carefully read papers before publication. Peer reviewers might be biased against a hypothesis or an author. A general problem is selected publication of positive results. An example of fraud in sports medicine is the presence of doped subjects in groups of investigated athletes. To reduce regress, it is important that investigators search both original and recent articles on a topic and conscientiously examine the data. All co-authors and reviewers should read the text thoroughly and inspect all tables and figures in a manuscript.

  12. Probing for the Multiplicative Term in Modern Expectancy-Value Theory: A Latent Interaction Modeling Study

    ERIC Educational Resources Information Center

    Trautwein, Ulrich; Marsh, Herbert W.; Nagengast, Benjamin; Ludtke, Oliver; Nagy, Gabriel; Jonkmann, Kathrin

    2012-01-01

    In modern expectancy-value theory (EVT) in educational psychology, expectancy and value beliefs additively predict performance, persistence, and task choice. In contrast to earlier formulations of EVT, the multiplicative term Expectancy x Value in regression-type models typically plays no major role in educational psychology. The present study…

  13. Evaluation and application of regional turbidity-sediment regression models in Virginia

    USGS Publications Warehouse

    Hyer, Kenneth; Jastram, John D.; Moyer, Douglas; Webber, James S.; Chanat, Jeffrey G.

    2015-01-01

    Conventional thinking has long held that turbidity-sediment surrogate-regression equations are site specific and that regression equations developed at a single monitoring station should not be applied to another station; however, few studies have evaluated this issue in a rigorous manner. If robust regional turbidity-sediment models can be developed successfully, their applications could greatly expand the usage of these methods. Suspended sediment load estimation could occur as soon as flow and turbidity monitoring commence at a site, suspended sediment sampling frequencies for various projects potentially could be reduced, and special-project applications (sediment monitoring following dam removal, for example) could be significantly enhanced. The objective of this effort was to investigate the turbidity-suspended sediment concentration (SSC) relations at all available USGS monitoring sites within Virginia to determine whether meaningful turbidity-sediment regression models can be developed by combining the data from multiple monitoring stations into a single model, known as a “regional” model. Following the development of the regional model, additional objectives included a comparison of predicted SSCs between the regional model and commonly used site-specific models, as well as an evaluation of why specific monitoring stations did not fit the regional model.

  14. Early development and regression in Rett syndrome.

    PubMed

    Lee, J Y L; Leonard, H; Piek, J P; Downs, J

    2013-12-01

    This study utilized developmental profiling to examine symptoms in 14 girls with genetically confirmed Rett syndrome and whose families were participating in the Australian Rett syndrome or InterRett database. Regression was mostly characterized by loss of hand and/or communication skills (13/14) except one girl demonstrated slowing of skill development. Social withdrawal and inconsolable crying often developed simultaneously (9/14), with social withdrawal for shorter duration than inconsolable crying. Previously acquired gross motor skills declined in just over half of the sample (8/14), mostly observed as a loss of balance. Early abnormalities such as vomiting and strabismus were also seen. Our findings provide additional insight into the early clinical profile of Rett syndrome.

  15. Understanding and Interpreting Regression Parameter Estimates in Given Contexts: A Monte Carlo Study of Characteristics of Regression and Structural Coefficients, Effect Size R Squared and Significance Level of Predictors.

    ERIC Educational Resources Information Center

    Jiang, Ying Hong; Smith, Philip L.

    This Monte Carlo study explored relationships among standard and unstandardized regression coefficients, structural coefficients, multiple R_ squared, and significance level of predictors for a variety of linear regression scenarios. Ten regression models with three predictors were included, and four conditions were varied that were expected to…

  16. Genetics Home Reference: caudal regression syndrome

    MedlinePlus

    ... of a genetic condition? Genetic and Rare Diseases Information Center Frequency Caudal regression syndrome is estimated to occur in 1 to ... parts of the skeleton, gastrointestinal system, and genitourinary ... caudal regression syndrome results from the presence of an abnormal ...

  17. Semiparametric regression during 2003–2007*

    PubMed Central

    Ruppert, David; Wand, M.P.; Carroll, Raymond J.

    2010-01-01

    Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application. PMID:20305800

  18. [Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging].

    PubMed

    Yang, Shun-hua; Zhang, Hai-tao; Guo, Long; Ren, Yan

    2015-06-01

    Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control. The results indicated that soil or- ganic matter was significantly positively correlated with relative elevation whilst it had a significantly negative correlation with stream power index. Semivariance analysis showed that both soil organic matter content and its residuals (including ordinary least square regression residual and GWR resi- dual) had strong spatial autocorrelation. Interpolation accuracies by different methods were esti- mated based on a data set of 98 validation samples. Results showed that the mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) of RK were respectively 39.2%, 17.7% and 20.6% lower than the corresponding values of OK, with a relative-improvement (RI) of 20.63. GWRK showed a similar tendency, having its ME, MAE and RMSE to be respectively 60.6%, 23.7% and 27.6% lower than those of OK, with a RI of 59.79. Therefore, both RK and GWRK significantly improved the accuracy of OK interpolation of soil organic matter due to their in- corporation of auxiliary variables. In addition, GWRK performed obviously better than RK did in this study, and its improved performance should be attributed to the consideration of sample spatial locations.

  19. Bayesian Unimodal Density Regression for Causal Inference

    ERIC Educational Resources Information Center

    Karabatsos, George; Walker, Stephen G.

    2011-01-01

    Karabatsos and Walker (2011) introduced a new Bayesian nonparametric (BNP) regression model. Through analyses of real and simulated data, they showed that the BNP regression model outperforms other parametric and nonparametric regression models of common use, in terms of predictive accuracy of the outcome (dependent) variable. The other,…

  20. Developmental Regression in Autism Spectrum Disorders

    ERIC Educational Resources Information Center

    Rogers, Sally J.

    2004-01-01

    The occurrence of developmental regression in autism is one of the more puzzling features of this disorder. Although several studies have documented the validity of parental reports of regression using home videos, accumulating data suggest that most children who demonstrate regression also demonstrated previous, subtle, developmental differences.…