Hierarchical regression for analyses of multiple outcomes.
Richardson, David B; Hamra, Ghassan B; MacLehose, Richard F; Cole, Stephen R; Chu, Haitao
2015-09-01
In cohort mortality studies, there often is interest in associations between an exposure of primary interest and mortality due to a range of different causes. A standard approach to such analyses involves fitting a separate regression model for each type of outcome. However, the statistical precision of some estimated associations may be poor because of sparse data. In this paper, we describe a hierarchical regression model for estimation of parameters describing outcome-specific relative rate functions and associated credible intervals. The proposed model uses background stratification to provide flexible control for the outcome-specific associations of potential confounders, and it employs a hierarchical "shrinkage" approach to stabilize estimates of an exposure's associations with mortality due to different causes of death. The approach is illustrated in analyses of cancer mortality in 2 cohorts: a cohort of dioxin-exposed US chemical workers and a cohort of radiation-exposed Japanese atomic bomb survivors. Compared with standard regression estimates of associations, hierarchical regression yielded estimates with improved precision that tended to have less extreme values. The hierarchical regression approach also allowed the fitting of models with effect-measure modification. The proposed hierarchical approach can yield estimates of association that are more precise than conventional estimates when one wishes to estimate associations with multiple outcomes. PMID:26232395
Estimation of adjusted rate differences using additive negative binomial regression.
Donoghoe, Mark W; Marschner, Ian C
2016-08-15
Rate differences are an important effect measure in biostatistics and provide an alternative perspective to rate ratios. When the data are event counts observed during an exposure period, adjusted rate differences may be estimated using an identity-link Poisson generalised linear model, also known as additive Poisson regression. A problem with this approach is that the assumption of equality of mean and variance rarely holds in real data, which often show overdispersion. An additive negative binomial model is the natural alternative to account for this; however, standard model-fitting methods are often unable to cope with the constrained parameter space arising from the non-negativity restrictions of the additive model. In this paper, we propose a novel solution to this problem using a variant of the expectation-conditional maximisation-either algorithm. Our method provides a reliable way to fit an additive negative binomial regression model and also permits flexible generalisations using semi-parametric regression functions. We illustrate the method using a placebo-controlled clinical trial of fenofibrate treatment in patients with type II diabetes, where the outcome is the number of laser therapy courses administered to treat diabetic retinopathy. An R package is available that implements the proposed method. Copyright © 2016 John Wiley & Sons, Ltd. PMID:27073156
Applications of MIDAS regression in analysing trends in water quality
NASA Astrophysics Data System (ADS)
Penev, Spiridon; Leonte, Daniela; Lazarov, Zdravetz; Mann, Rob A.
2014-04-01
We discuss novel statistical methods in analysing trends in water quality. Such analysis uses complex data sets of different classes of variables, including water quality, hydrological and meteorological. We analyse the effect of rainfall and flow on trends in water quality utilising a flexible model called Mixed Data Sampling (MIDAS). This model arises because of the mixed frequency in the data collection. Typically, water quality variables are sampled fortnightly, whereas the rain data is sampled daily. The advantage of using MIDAS regression is in the flexible and parsimonious modelling of the influence of the rain and flow on trends in water quality variables. We discuss the model and its implementation on a data set from the Shoalhaven Supply System and Catchments in the state of New South Wales, Australia. Information criteria indicate that MIDAS modelling improves upon simplistic approaches that do not utilise the mixed data sampling nature of the data.
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…
Procedures for adjusting regional regression models of urban-runoff quality using local data
Hoos, Anne B.; Lizarraga, Joy S.
1996-01-01
Statistical operations termed model-adjustment procedures can be used to incorporate local data into existing regression modes to improve the predication of urban-runoff quality. Each procedure is a form of regression analysis in which the local data base is used as a calibration data set; the resulting adjusted regression models can then be used to predict storm-runoff quality at unmonitored sites. Statistical tests of the calibration data set guide selection among proposed procedures.
Adjustment of regional regression equations for urban storm-runoff quality using at-site data
Barks, C.S.
1996-01-01
Regional regression equations have been developed to estimate urban storm-runoff loads and mean concentrations using a national data base. Four statistical methods using at-site data to adjust the regional equation predictions were developed to provide better local estimates. The four adjustment procedures are a single-factor adjustment, a regression of the observed data against the predicted values, a regression of the observed values against the predicted values and additional local independent variables, and a weighted combination of a local regression with the regional prediction. Data collected at five representative storm-runoff sites during 22 storms in Little Rock, Arkansas, were used to verify, and, when appropriate, adjust the regional regression equation predictions. Comparison of observed values of stormrunoff loads and mean concentrations to the predicted values from the regional regression equations for nine constituents (chemical oxygen demand, suspended solids, total nitrogen as N, total ammonia plus organic nitrogen as N, total phosphorus as P, dissolved phosphorus as P, total recoverable copper, total recoverable lead, and total recoverable zinc) showed large prediction errors ranging from 63 percent to more than several thousand percent. Prediction errors for 6 of the 18 regional regression equations were less than 100 percent and could be considered reasonable for water-quality prediction equations. The regression adjustment procedure was used to adjust five of the regional equation predictions to improve the predictive accuracy. For seven of the regional equations the observed and the predicted values are not significantly correlated. Thus neither the unadjusted regional equations nor any of the adjustments were appropriate. The mean of the observed values was used as a simple estimator when the regional equation predictions and adjusted predictions were not appropriate.
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.
Lidauer, M H; Emmerling, R; Mäntysaari, E A
2008-06-01
A multiplicative random regression (M-RRM) test-day (TD) model was used to analyse daily milk yields from all available parities of German and Austrian Simmental dairy cattle. The method to account for heterogeneous variance (HV) was based on the multiplicative mixed model approach of Meuwissen. The variance model for the heterogeneity parameters included a fixed region x year x month x parity effect and a random herd x test-month effect with a within-herd first-order autocorrelation between test-months. Acceleration of variance model solutions after each multiplicative model cycle enabled fast convergence of adjustment factors and reduced total computing time significantly. Maximum Likelihood estimation of within-strata residual variances was enhanced by inclusion of approximated information on loss in degrees of freedom due to estimation of location parameters. This improved heterogeneity estimates for very small herds. The multiplicative model was compared with a model that assumed homogeneous variance. Re-estimated genetic variances, based on Mendelian sampling deviations, were homogeneous for the M-RRM TD model but heterogeneous for the homogeneous random regression TD model. Accounting for HV had large effect on cow ranking but moderate effect on bull ranking.
Hierarchical regression for epidemiologic analyses of multiple exposures.
Greenland, S
1994-01-01
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 "semi-Bayes" 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. PMID:7851328
The use of GLS regression in regional hydrologic analyses
NASA Astrophysics Data System (ADS)
Griffis, V. W.; Stedinger, J. R.
2007-09-01
SummaryTo estimate flood quantiles and other statistics at ungauged sites, many organizations employ an iterative generalized least squares (GLS) regression procedure to estimate the parameters of a model of the statistic of interest as a function of basin characteristics. The GLS regression procedure accounts for differences in available record lengths and spatial correlation in concurrent events by using an estimator of the sampling covariance matrix of available flood quantiles. Previous studies by the US Geological Survey using the LP3 distribution have neglected the impact of uncertainty in the weighted skew on quantile precision. The needed relationship is developed here and its use is illustrated in a regional flood study with 162 sites from South Carolina. The performance of a pooled regression model is compared to separate models for each hydrologic region: statistical tests recommend an interesting hybrid of the two which is both surprising and hydrologically reasonable. The statistical analysis is augmented with new diagnostic metrics including a condition number to check for multicollinearity, a new pseudo- R appropriate for use with GLS regression, and two error variance ratios. GLS regression for the standard deviation demonstrates that again a hybrid model is attractive, and that GLS rather than an OLS or WLS analysis is appropriate for the development of regional standard deviation models.
Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies
Vatcheva, Kristina P.; Lee, MinJae; McCormick, Joseph B.; Rahbar, Mohammad H.
2016-01-01
The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis. PMID:27274911
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,…
Operational Bayesian GLS Regression for Regional Hydrologic Analyses
NASA Astrophysics Data System (ADS)
Reis, D. S.; Stedinger, J. R.; Martins, E. S.
2004-05-01
Reis et al. (2003) introduced a Bayesian approach to Generalized Least Squares (GLS) regression which has several advantages over the method-of-moments (MM) and maximum likelihood estimators (MLEs) proposed by Stedinger and Tasker (1985). The Bayesian approach provides both a measure of precision of the model error variance that MM and MLEs lack, and a more reasonable description of the possible values of the model error variance, in cases where the MLE and MOM model error variance estimator is zero or nearly zero. This study further develops the quasi-analytic Bayesian regression model into a practical Generalized Least Squares (GLS) regional hydrologic regression methodology able to address estimation of flood quantiles, regional shape parameters, and other statistics. The paper also explores regression diagnostic statistics for Weighted Least Squares (WLS) and GLS models, including a pseudo coefficient of determination R2 that can be used for model evaluation, and leverage and influence that are proposed to identify rogue observations, address lack of fit, and to support gauge network design. Regionalization of the shape parameter of the Log-Pearson Type III distribution is attempted using data for the Illinois River basin, and the State of South Carolina. Results obtained from Ordinary Least Squares (OLS), WLS, and GLS regional regression procedures were compared, as well as the results from the Bayesian and method-of-moments estimators. The OLS results are misleading because they do not make any distinction between the variance due to the model error and the variance due to time sampling error. Both of these examples demonstrate that the true model error variance for regional skew models is on the order of 0.10 or less. Leverage and influence statistics were very useful in identifying stations that could or did have a significant impact on the analysis. Reis, D. S., Jr., J.R. Stedinger, and E.S. Martins, Bayesian GLS Regression with application to LP3 Regional
ERIC Educational Resources Information Center
Olejnik, Stephen; Mills, Jamie; Keselman, Harvey
2000-01-01
Evaluated the use of Mallow's C(p) and Wherry's adjusted R squared (R. Wherry, 1931) statistics to select a final model from a pool of model solutions using computer generated data. Neither statistic identified the underlying regression model any better than, and usually less well than, the stepwise selection method, which itself was poor for…
VanderWeele, Tyler J; Robinson, Whitney R
2014-07-01
We consider several possible interpretations of the "effect of race" when regressions are run with race as an exposure variable, controlling also for various confounding and mediating variables. When adjustment is made for socioeconomic status early in a person's life, we discuss under what contexts the regression coefficients for race can be interpreted as corresponding to the extent to which a racial inequality would remain if various socioeconomic distributions early in life across racial groups could be equalized. When adjustment is also made for adult socioeconomic status, we note how the overall racial inequality can be decomposed into the portion that would be eliminated by equalizing adult socioeconomic status across racial groups and the portion of the inequality that would remain even if adult socioeconomic status across racial groups were equalized. We also discuss a stronger interpretation of the effect of race (stronger in terms of assumptions) involving the joint effects of race-associated physical phenotype (eg, skin color), parental physical phenotype, genetic background, and cultural context when such variables are thought to be hypothetically manipulable and if adequate control for confounding were possible. We discuss some of the challenges with such an interpretation. Further discussion is given as to how the use of selected populations in examining racial disparities can additionally complicate the interpretation of the effects.
On causal interpretation of race in regressions adjusting for confounding and mediating variables
VanderWeele, Tyler J.; Robinson, Whitney R.
2014-01-01
We consider several possible interpretations of the “effect of race” when regressions are run with race as an exposure variable, controlling also for various confounding and mediating variables. When adjustment is made for socioeconomic status early in a person’s life, we discuss under what contexts the regression coefficients for race can be interpreted as corresponding to the extent to which a racial inequality would remain if various socioeconomic distributions early in life across racial groups could be equalized. When adjustment is also made for adult socioeconomic status, we note how the overall racial inequality can be decomposed into the portion that would be eliminated by equalizing adult socioeconomic status across racial groups and the portion of the inequality that would remain even if adult socioeconomic status across racial groups were equalized. We also discuss a stronger interpretation of the “effect of race” (stronger in terms of assumptions) involving the joint effects of race-associated physical phenotype (e.g. skin color), parental physical phenotype, genetic background and cultural context when such variables are thought to be hypothetically manipulable and if adequate control for confounding were possible. We discuss some of the challenges with such an interpretation. Further discussion is given as to how the use of selected populations in examining racial disparities can additionally complicate the interpretation of the effects. PMID:24887159
Algamal, Zakariya Yahya; Lee, Muhammad Hisyam
2015-12-01
Cancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. The adaptive elastic net originally used elastic net estimates as the initial weight, however, using this weight may not be preferable for certain reasons: First, the elastic net estimator is biased in selecting genes. Second, it does not perform well when the pairwise correlations between variables are not high. Adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously. The real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods. Additionally, the classification performance of AAElastic is comparable to the adaptive elastic net and better than other regularization methods. Thus, we can conclude that AAElastic is a reliable adaptive regularized logistic regression method in the field of high-dimensional cancer classification.
ERIC Educational Resources Information Center
Shafiq, M. Najeeb
2011-01-01
Using quantile regression analyses, this study examines gender gaps in mathematics, science, and reading in Azerbaijan, Indonesia, Jordan, the Kyrgyz Republic, Qatar, Tunisia, and Turkey among 15 year-old students. The analyses show that girls in Azerbaijan achieve as well as boys in mathematics and science and overachieve in reading. In Jordan,…
Lopez, Michael J; Gutman, Roee
2014-11-28
Propensity score methods are common for estimating a binary treatment effect when treatment assignment is not randomized. When exposure is measured on an ordinal scale (i.e. low-medium-high), however, propensity score inference requires extensions which have received limited attention. Estimands of possible interest with an ordinal exposure are the average treatment effects between each pair of exposure levels. Using these estimands, it is possible to determine an optimal exposure level. Traditional methods, including dichotomization of the exposure or a series of binary propensity score comparisons across exposure pairs, are generally inadequate for identification of optimal levels. We combine subclassification with regression adjustment to estimate transitive, unbiased average causal effects across an ordered exposure, and apply our method on the 2005-2006 National Health and Nutrition Examination Survey to estimate the effects of nutritional label use on body mass index.
A comparison of several regression models for analysing cost of CABG surgery.
Austin, Peter C; Ghali, William A; Tu, Jack V
2003-09-15
Investigators in clinical research are often interested in determining the association between patient characteristics and cost of medical or surgical treatment. However, there is no uniformly agreed upon regression model with which to analyse cost data. The objective of the current study was to compare the performance of linear regression, linear regression with log-transformed cost, generalized linear models with Poisson, negative binomial and gamma distributions, median regression, and proportional hazards models for analysing costs in a cohort of patients undergoing CABG surgery. The study was performed on data comprising 1959 patients who underwent CABG surgery in Calgary, Alberta, between June 1994 and March 1998. Ten of 21 patient characteristics were significantly associated with cost of surgery in all seven models. Eight variables were not significantly associated with cost of surgery in all seven models. Using mean squared prediction error as a loss function, proportional hazards regression and the three generalized linear models were best able to predict cost in independent validation data. Using mean absolute error, linear regression with log-transformed cost, proportional hazards regression, and median regression to predict median cost, were best able to predict cost in independent validation data. Since the models demonstrated good consistency in identifying factors associated with increased cost of CABG surgery, any of the seven models can be used for identifying factors associated with increased cost of surgery. However, the magnitude of, and the interpretation of, the coefficients vary across models. Researchers are encouraged to consider a variety of candidate models, including those better known in the econometrics literature, rather than begin data analysis with one regression model selected a priori. The final choice of regression model should be made after a careful assessment of how best to assess predictive ability and should be tailored to
A comparison of several regression models for analysing cost of CABG surgery.
Austin, Peter C; Ghali, William A; Tu, Jack V
2003-09-15
Investigators in clinical research are often interested in determining the association between patient characteristics and cost of medical or surgical treatment. However, there is no uniformly agreed upon regression model with which to analyse cost data. The objective of the current study was to compare the performance of linear regression, linear regression with log-transformed cost, generalized linear models with Poisson, negative binomial and gamma distributions, median regression, and proportional hazards models for analysing costs in a cohort of patients undergoing CABG surgery. The study was performed on data comprising 1959 patients who underwent CABG surgery in Calgary, Alberta, between June 1994 and March 1998. Ten of 21 patient characteristics were significantly associated with cost of surgery in all seven models. Eight variables were not significantly associated with cost of surgery in all seven models. Using mean squared prediction error as a loss function, proportional hazards regression and the three generalized linear models were best able to predict cost in independent validation data. Using mean absolute error, linear regression with log-transformed cost, proportional hazards regression, and median regression to predict median cost, were best able to predict cost in independent validation data. Since the models demonstrated good consistency in identifying factors associated with increased cost of CABG surgery, any of the seven models can be used for identifying factors associated with increased cost of surgery. However, the magnitude of, and the interpretation of, the coefficients vary across models. Researchers are encouraged to consider a variety of candidate models, including those better known in the econometrics literature, rather than begin data analysis with one regression model selected a priori. The final choice of regression model should be made after a careful assessment of how best to assess predictive ability and should be tailored to
New ventures require accurate risk analyses and adjustments.
Eastaugh, S R
2000-01-01
For new business ventures to succeed, healthcare executives need to conduct robust risk analyses and develop new approaches to balance risk and return. Risk analysis involves examination of objective risks and harder-to-quantify subjective risks. Mathematical principles applied to investment portfolios also can be applied to a portfolio of departments or strategic business units within an organization. The ideal business investment would have a high expected return and a low standard deviation. Nonetheless, both conservative and speculative strategies should be considered in determining an organization's optimal service line and helping the organization manage risk.
Holsclaw, Tracy; Hallgren, Kevin A; Steyvers, Mark; Smyth, Padhraic; Atkins, David C
2015-12-01
Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials.
Holsclaw, Tracy; Hallgren, Kevin A; Steyvers, Mark; Smyth, Padhraic; Atkins, David C
2015-12-01
Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials. PMID:26098126
Direkvand-Moghadam, Ashraf; Suhrabi, Zainab; Akbari, Malihe
2016-01-01
Background Female sexual dysfunction, which can occur during any stage of a normal sexual activity, is a serious condition for individuals and couples. The present study aimed to determine the prevalence and predictive factors of female sexual dysfunction in women referred to health centers in Ilam, the Western Iran, in 2014. Methods In the present cross-sectional study, 444 women who attended health centers in Ilam were enrolled from May to September 2014. Participants were selected according to the simple random sampling method. Univariate and multivariate logistic regression analyses were used to predict the risk factors of female sexual dysfunction. Diffe rences with an alpha error of 0.05 were regarded as statistically significant. Results Overall, 75.9% of the study population exhibited sexual dysfunction. Univariate logistic regression analysis demonstrated that there was a significant association between female sexual dysfunction and age, menarche age, gravidity, parity, and education (P<0.05). Multivariate logistic regression analysis indicated that, menarche age (odds ratio, 1.26), education level (odds ratio, 1.71), and gravida (odds ratio, 1.59) were independent predictive variables for female sexual dysfunction. Conclusion The majority of Iranian women suffer from sexual dysfunction. A lack of awareness of Iranian women's sexual pleasure and formal training on sexual function and its influencing factors, such as menarche age, gravida, and level of education, may lead to a high prevalence of female sexual dysfunction. PMID:27688863
Direkvand-Moghadam, Ashraf; Suhrabi, Zainab; Akbari, Malihe
2016-01-01
Background Female sexual dysfunction, which can occur during any stage of a normal sexual activity, is a serious condition for individuals and couples. The present study aimed to determine the prevalence and predictive factors of female sexual dysfunction in women referred to health centers in Ilam, the Western Iran, in 2014. Methods In the present cross-sectional study, 444 women who attended health centers in Ilam were enrolled from May to September 2014. Participants were selected according to the simple random sampling method. Univariate and multivariate logistic regression analyses were used to predict the risk factors of female sexual dysfunction. Diffe rences with an alpha error of 0.05 were regarded as statistically significant. Results Overall, 75.9% of the study population exhibited sexual dysfunction. Univariate logistic regression analysis demonstrated that there was a significant association between female sexual dysfunction and age, menarche age, gravidity, parity, and education (P<0.05). Multivariate logistic regression analysis indicated that, menarche age (odds ratio, 1.26), education level (odds ratio, 1.71), and gravida (odds ratio, 1.59) were independent predictive variables for female sexual dysfunction. Conclusion The majority of Iranian women suffer from sexual dysfunction. A lack of awareness of Iranian women's sexual pleasure and formal training on sexual function and its influencing factors, such as menarche age, gravida, and level of education, may lead to a high prevalence of female sexual dysfunction.
Tu, Y-K; Kellett, M; Clerehugh, V; Gilthorpe, M S
2005-10-01
Multivariable analysis is a widely used statistical methodology for investigating associations amongst clinical variables. However, the problems of collinearity and multicollinearity, which can give rise to spurious results, have in the past frequently been disregarded in dental research. This article illustrates and explains the problems which may be encountered, in the hope of increasing awareness and understanding of these issues, thereby improving the quality of the statistical analyses undertaken in dental research. Three examples from different clinical dental specialties are used to demonstrate how to diagnose the problem of collinearity/multicollinearity in multiple regression analyses and to illustrate how collinearity/multicollinearity can seriously distort the model development process. Lack of awareness of these problems can give rise to misleading results and erroneous interpretations. Multivariable analysis is a useful tool for dental research, though only if its users thoroughly understand the assumptions and limitations of these methods. It would benefit evidence-based dentistry enormously if researchers were more aware of both the complexities involved in multiple regression when using these methods and of the need for expert statistical consultation in developing study design and selecting appropriate statistical methodologies.
Barks, C.S.
1995-01-01
Storm-runoff water-quality data were used to verify and, when appropriate, adjust regional regression models previously developed to estimate urban storm- runoff loads and mean concentrations in Little Rock, Arkansas. Data collected at 5 representative sites during 22 storms from June 1992 through January 1994 compose the Little Rock data base. Comparison of observed values (0) of storm-runoff loads and mean concentrations to the predicted values (Pu) from the regional regression models for nine constituents (chemical oxygen demand, suspended solids, total nitrogen, total ammonia plus organic nitrogen as nitrogen, total phosphorus, dissolved phosphorus, total recoverable copper, total recoverable lead, and total recoverable zinc) shows large prediction errors ranging from 63 to several thousand percent. Prediction errors for six of the regional regression models are less than 100 percent, and can be considered reasonable for water-quality models. Differences between 0 and Pu are due to variability in the Little Rock data base and error in the regional models. Where applicable, a model adjustment procedure (termed MAP-R-P) based upon regression with 0 against Pu was applied to improve predictive accuracy. For 11 of the 18 regional water-quality models, 0 and Pu are significantly correlated, that is much of the variation in 0 is explained by the regional models. Five of these 11 regional models consistently overestimate O; therefore, MAP-R-P can be used to provide a better estimate. For the remaining seven regional models, 0 and Pu are not significanfly correlated, thus neither the unadjusted regional models nor the MAP-R-P is appropriate. A simple estimator, such as the mean of the observed values may be used if the regression models are not appropriate. Standard error of estimate of the adjusted models ranges from 48 to 130 percent. Calibration results may be biased due to the limited data set sizes in the Little Rock data base. The relatively large values of
ERIC Educational Resources Information Center
Thatcher, Greg W.; Henson, Robin K.
This study examined research in training and development to determine effect size reporting practices. It focused on the reporting of corrected effect sizes in research articles using multiple regression analyses. When possible, researchers calculated corrected effect sizes and determine if the associated shrinkage could have impacted researcher…
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.
Leushuis, Esther; Wetzels, Alex; van der Steeg, Jan Willem; Steures, Pieternel; Bossuyt, Patrick M.M.; van Trooyen, Netty; Repping, Sjoerd; van der Horst, Frans A.L.; Hompes, Peter G.A. Hompes; Mol, Ben Willem J.; van der Veen, Fulco
2016-01-01
Background Standardization of the semen analysis may improve reproducibility. We assessed variability between laboratories in semen analyses and evaluated whether a transformation using Z scores and regression statistics was able to reduce this variability. Materials and Methods We performed a retrospective cohort study. We calculated between-laboratory coefficients of variation (CVB) for sperm concentration and for morphology. Subsequently, we standardized the semen analysis results by calculating laboratory specific Z scores, and by using regression. We used analysis of variance for four semen parameters to assess systematic differences between laboratories before and after the transformations, both in the circulation samples and in the samples obtained in the prospective cohort study in the Netherlands between January 2002 and February 2004. Results The mean CVBwas 7% for sperm concentration (range 3 to 13%) and 32% for sperm morphology (range 18 to 51%). The differences between the laboratories were statistically significant for all semen parameters (all P<0.001). Standardization using Z scores did not reduce the differences in semen analysis results between the laboratories (all P<0.001). Conclusion There exists large between-laboratory variability for sperm morphology and small, but statistically significant, between-laboratory variation for sperm concentration. Standardization using Z scores does not eliminate between-laboratory variability. PMID:26985342
Dimensionality of the Chinese Dyadic Adjustment Scale Based on Confirmatory Factor Analyses
ERIC Educational Resources Information Center
Shek, Daniel T. L.; Cheung, C. K.
2008-01-01
Based on the responses of 1,501 Chinese married adults to the Chinese version of the Dyadic Adjustment Scale (C-DAS), confirmatory factor analyses showed that four factors were abstracted from the C-DAS (Dyadic Consensus, Dyadic Cohesion, Dyadic Satisfaction and Affectional Expression) and these four primary factors were subsumed under a…
Li, Li; Brumback, Babette A; Weppelmann, Thomas A; Morris, J Glenn; Ali, Afsar
2016-08-15
Motivated by an investigation of the effect of surface water temperature on the presence of Vibrio cholerae in water samples collected from different fixed surface water monitoring sites in Haiti in different months, we investigated methods to adjust for unmeasured confounding due to either of the two crossed factors site and month. In the process, we extended previous methods that adjust for unmeasured confounding due to one nesting factor (such as site, which nests the water samples from different months) to the case of two crossed factors. First, we developed a conditional pseudolikelihood estimator that eliminates fixed effects for the levels of each of the crossed factors from the estimating equation. Using the theory of U-Statistics for independent but non-identically distributed vectors, we show that our estimator is consistent and asymptotically normal, but that its variance depends on the nuisance parameters and thus cannot be easily estimated. Consequently, we apply our estimator in conjunction with a permutation test, and we investigate use of the pigeonhole bootstrap and the jackknife for constructing confidence intervals. We also incorporate our estimator into a diagnostic test for a logistic mixed model with crossed random effects and no unmeasured confounding. For comparison, we investigate between-within models extended to two crossed factors. These generalized linear mixed models include covariate means for each level of each factor in order to adjust for the unmeasured confounding. We conduct simulation studies, and we apply the methods to the Haitian data. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26892025
Hoos, Anne B.; Patel, Anant R.
1996-01-01
Model-adjustment procedures were applied to the combined data bases of storm-runoff quality for Chattanooga, Knoxville, and Nashville, Tennessee, to improve predictive accuracy for storm-runoff quality for urban watersheds in these three cities and throughout Middle and East Tennessee. Data for 45 storms at 15 different sites (five sites in each city) constitute the data base. Comparison of observed values of storm-runoff load and event-mean concentration to the predicted values from the regional regression models for 10 constituents shows prediction errors, as large as 806,000 percent. Model-adjustment procedures, which combine the regional model predictions with local data, are applied to improve predictive accuracy. Standard error of estimate after model adjustment ranges from 67 to 322 percent. Calibration results may be biased due to sampling error in the Tennessee data base. The relatively large values of standard error of estimate for some of the constituent models, although representing significant reduction (at least 50 percent) in prediction error compared to estimation with unadjusted regional models, may be unacceptable for some applications. The user may wish to collect additional local data for these constituents and repeat the analysis, or calibrate an independent local regression model.
Stratton, Kelly G; Cook, Andrea J; Jackson, Lisa A; Nelson, Jennifer C
2015-03-30
Sequential methods are well established for randomized clinical trials (RCTs), and their use in observational settings has increased with the development of national vaccine and drug safety surveillance systems that monitor large healthcare databases. Observational safety monitoring requires that sequential testing methods be better equipped to incorporate confounder adjustment and accommodate rare adverse events. New methods designed specifically for observational surveillance include a group sequential likelihood ratio test that uses exposure matching and generalized estimating equations approach that involves regression adjustment. However, little is known about the statistical performance of these methods or how they compare to RCT methods in both observational and rare outcome settings. We conducted a simulation study to determine the type I error, power and time-to-surveillance-end of group sequential likelihood ratio test, generalized estimating equations and RCT methods that construct group sequential Lan-DeMets boundaries using data from a matched (group sequential Lan-DeMets-matching) or unmatched regression (group sequential Lan-DeMets-regression) setting. We also compared the methods using data from a multisite vaccine safety study. All methods had acceptable type I error, but regression methods were more powerful, faster at detecting true safety signals and less prone to implementation difficulties with rare events than exposure matching methods. Method performance also depended on the distribution of information and extent of confounding by site. Our results suggest that choice of sequential method, especially the confounder control strategy, is critical in rare event observational settings. These findings provide guidance for choosing methods in this context and, in particular, suggest caution when conducting exposure matching.
Methods for Adjusting U.S. Geological Survey Rural Regression Peak Discharges in an Urban Setting
Moglen, Glenn E.; Shivers, Dorianne E.
2006-01-01
A study was conducted of 78 U.S. Geological Survey gaged streams that have been subjected to varying degrees of urbanization over the last three decades. Flood-frequency analysis coupled with nonlinear regression techniques were used to generate a set of equations for converting peak discharge estimates determined from rural regression equations to a set of peak discharge estimates that represent known urbanization. Specifically, urban regression equations for the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year return periods were calibrated as a function of the corresponding rural peak discharge and the percentage of impervious area in a watershed. The results of this study indicate that two sets of equations, one set based on imperviousness and one set based on population density, performed well. Both sets of equations are dependent on rural peak discharges, a measure of development (average percentage of imperviousness or average population density), and a measure of homogeneity of development within a watershed. Average imperviousness was readily determined by using geographic information system methods and commonly available land-cover data. Similarly, average population density was easily determined from census data. Thus, a key advantage to the equations developed in this study is that they do not require field measurements of watershed characteristics as did the U.S. Geological Survey urban equations developed in an earlier investigation. During this study, the U.S. Geological Survey PeakFQ program was used as an integral tool in the calibration of all equations. The scarcity of historical land-use data, however, made exclusive use of flow records necessary for the 30-year period from 1970 to 2000. Such relatively short-duration streamflow time series required a nonstandard treatment of the historical data function of the PeakFQ program in comparison to published guidelines. Thus, the approach used during this investigation does not fully comply with the
Methodological uncertainties in multi-regression analyses of middle-atmospheric data series.
Kerzenmacher, Tobias E; Keckhut, Philippe; Hauchecorne, Alain; Chanin, Marie-Lise
2006-07-01
Multi-regression analyses have often been used recently to detect trends, in particular in ozone or temperature data sets in the stratosphere. The confidence in detecting trends depends on a number of factors which generate uncertainties. Part of these uncertainties comes from the random variability and these are what is usually considered. They can be statistically estimated from residual deviations between the data and the fitting model. However, interferences between different sources of variability affecting the data set, such as the Quasi-Biennal Oscillation (QBO), volcanic aerosols, solar flux variability and the trend can also be a critical source of errors. This type of error has hitherto not been well quantified. In this work an artificial data series has been generated to carry out such estimates. The sources of errors considered here are: the length of the data series, the dependence on the choice of parameters used in the fitting model and the time evolution of the trend in the data series. Curves provided here, will permit future studies to test the magnitude of the methodological bias expected for a given case, as shown in several real examples. It is found that, if the data series is shorter than a decade, the uncertainties are very large, whatever factors are chosen to identify the source of the variability. However the errors can be limited when dealing with natural variability, if a sufficient number of periods (for periodic forcings) are covered by the analysed dataset. However when analysing the trend, the response to volcanic eruption induces a bias, whatever the length of the data series. The signal to noise ratio is a key factor: doubling the noise increases the period for which data is required in order to obtain an error smaller than 10%, from 1 to 3-4 decades. Moreover, if non-linear trends are superimposed on the data, and if the length of the series is longer than five years, a non-linear function has to be used to estimate trends. When
Ho Hoang, Khai-Long; Mombaur, Katja
2015-10-15
Dynamic modeling of the human body is an important tool to investigate the fundamentals of the biomechanics of human movement. To model the human body in terms of a multi-body system, it is necessary to know the anthropometric parameters of the body segments. For young healthy subjects, several data sets exist that are widely used in the research community, e.g. the tables provided by de Leva. None such comprehensive anthropometric parameter sets exist for elderly people. It is, however, well known that body proportions change significantly during aging, e.g. due to degenerative effects in the spine, such that parameters for young people cannot be used for realistically simulating the dynamics of elderly people. In this study, regression equations are derived from the inertial parameters, center of mass positions, and body segment lengths provided by de Leva to be adjustable to the changes in proportion of the body parts of male and female humans due to aging. Additional adjustments are made to the reference points of the parameters for the upper body segments as they are chosen in a more practicable way in the context of creating a multi-body model in a chain structure with the pelvis representing the most proximal segment.
Ho Hoang, Khai-Long; Mombaur, Katja
2015-10-15
Dynamic modeling of the human body is an important tool to investigate the fundamentals of the biomechanics of human movement. To model the human body in terms of a multi-body system, it is necessary to know the anthropometric parameters of the body segments. For young healthy subjects, several data sets exist that are widely used in the research community, e.g. the tables provided by de Leva. None such comprehensive anthropometric parameter sets exist for elderly people. It is, however, well known that body proportions change significantly during aging, e.g. due to degenerative effects in the spine, such that parameters for young people cannot be used for realistically simulating the dynamics of elderly people. In this study, regression equations are derived from the inertial parameters, center of mass positions, and body segment lengths provided by de Leva to be adjustable to the changes in proportion of the body parts of male and female humans due to aging. Additional adjustments are made to the reference points of the parameters for the upper body segments as they are chosen in a more practicable way in the context of creating a multi-body model in a chain structure with the pelvis representing the most proximal segment. PMID:26338096
ERIC Educational Resources Information Center
Wu, Dane W.
2002-01-01
The year 2000 US presidential election between Al Gore and George Bush has been the most intriguing and controversial one in American history. The state of Florida was the trigger for the controversy, mainly, due to the use of the misleading "butterfly ballot". Using prediction (or confidence) intervals for least squares regression lines on the…
Multiple regression analyses in artificial-grammar learning: the importance of control groups.
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.
ERIC Educational Resources Information Center
Tay, Louis; Drasgow, Fritz
2012-01-01
Two Monte Carlo simulation studies investigated the effectiveness of the mean adjusted X[superscript 2]/df statistic proposed by Drasgow and colleagues and, because of problems with the method, a new approach for assessing the goodness of fit of an item response theory model was developed. It has been previously recommended that mean adjusted…
Genetic analyses of stillbirth in relation to litter size using random regression models.
Chen, C Y; Misztal, I; Tsuruta, S; Herring, W O; Holl, J; Culbertson, M
2010-12-01
Estimates of genetic parameters for number of stillborns (NSB) in relation to litter size (LS) were obtained with random regression models (RRM). Data were collected from 4 purebred Duroc nucleus farms between 2004 and 2008. Two data sets with 6,575 litters for the first parity (P1) and 6,259 litters for the second to fifth parity (P2-5) with a total of 8,217 and 5,066 animals in the pedigree were analyzed separately. Number of stillborns was studied as a trait on sow level. Fixed effects were contemporary groups (farm-year-season) and fixed cubic regression coefficients on LS with Legendre polynomials. Models for P2-5 included the fixed effect of parity. Random effects were additive genetic effects for both data sets with permanent environmental effects included for P2-5. Random effects modeled with Legendre polynomials (RRM-L), linear splines (RRM-S), and degree 0 B-splines (RRM-BS) with regressions on LS were used. For P1, the order of polynomial, the number of knots, and the number of intervals used for respective models were quadratic, 3, and 3, respectively. For P2-5, the same parameters were linear, 2, and 2, respectively. Heterogeneous residual variances were considered in the models. For P1, estimates of heritability were 12 to 15%, 5 to 6%, and 6 to 7% in LS 5, 9, and 13, respectively. For P2-5, estimates were 15 to 17%, 4 to 5%, and 4 to 6% in LS 6, 9, and 12, respectively. For P1, average estimates of genetic correlations between LS 5 to 9, 5 to 13, and 9 to 13 were 0.53, -0.29, and 0.65, respectively. For P2-5, same estimates averaged for RRM-L and RRM-S were 0.75, -0.21, and 0.50, respectively. For RRM-BS with 2 intervals, the correlation was 0.66 between LS 5 to 7 and 8 to 13. Parameters obtained by 3 RRM revealed the nonlinear relationship between additive genetic effect of NSB and the environmental deviation of LS. The negative correlations between the 2 extreme LS might possibly indicate different genetic bases on incidence of stillbirth.
Use of discriminant and regression analyses to modify a clinical certification board examination.
Gerrow, J D; Boyd, M A; Scott, D A; Boulais, A P
1999-06-01
The National Dental Examining Board of Canada (NDEB) conducts mandatory, high stakes, pass/fail, certification examinations for dental licensure. One of these examinations was a seven-part, simulated clinical examination in which candidates were required to perform procedures on typodonts. These requirements were two intracoronal and two extracoronal preparations, an amalgam restoration, a provisional crown, and a diagnostic wax-up. Feedback from candidates and examiners indicated that one or more of the requirements may not have been contributing effectively to the overall evaluation of candidates. The NDEB's Clinical Examination Committee therefore requested that an in-depth statistical analysis be performed to identify potential areas of concern and to provide a basis for modifying the examinations. The results of two examination sessions with a total of 168 candidates were subjected to both a discriminant and a logistic regression analysis. Every candidate had results for each of the seven requirements, and no candidate participated in both sessions of the examination. The discriminant analysis revealed that six of the seven requirements could be used to reliably assign examinees according to their true pass/fail classifications. Stepwise discriminant analysis resulted in a 98.81 percent classification success rate with a corresponding 2.50 percent false-positive classification error rate. The logistic regression analysis showed that five components correctly predicted 99.40 percent with a 1.25 percent false-positive rate. The Clinical Examination Committee concluded that one requirement (diagnostic wax-up) should be eliminated and that a second requirement (PFM preparation) be significantly modified and reevaluated. This study demonstrates the usefulness of statistical methods in the analysis and modification of a clinical certification board examination.
Oka, Masayoshi; Wong, David W S
2016-06-01
Area-based measures of neighborhood characteristics simply derived from enumeration units (e.g., census tracts or block groups) ignore the potential of spatial spillover effects, and thus incorporating such measures into multilevel regression models may underestimate the neighborhood effects on health. To overcome this limitation, we describe the concept and method of areal median filtering to spatialize area-based measures of neighborhood characteristics for multilevel regression analyses. The areal median filtering approach provides a means to specify or formulate "neighborhoods" as meaningful geographic entities by removing enumeration unit boundaries as the absolute barriers and by pooling information from the neighboring enumeration units. This spatializing process takes into account for the potential of spatial spillover effects and also converts aspatial measures of neighborhood characteristics into spatial measures. From a conceptual and methodological standpoint, incorporating the derived spatial measures into multilevel regression analyses allows us to more accurately examine the relationships between neighborhood characteristics and health. To promote and set the stage for informative research in the future, we provide a few important conceptual and methodological remarks, and discuss possible applications, inherent limitations, and practical solutions for using the areal median filtering approach in the study of neighborhood effects on health.
NASA Astrophysics Data System (ADS)
Bai, Shibiao; Glade, Thomas; Bell, Rainer; Wang, Jian
2010-05-01
Earthquake triggered landslides are very common throughout the world. In particular the last events, e.g. in Pakistan and in China 2008 have demonstrated, that this trigger should not been underestimated. In order to determine the most fragile landslide areas in the future for a similar earthquake, it is important to calculate for these areas landslide susceptibility maps. In this paper, firstly, the earthquake triggered landslide distribution inventory at Longnan, a case study in China, is build up by field investigation and interpretation of remote-sensing image data (SPOT 5 and ALOS). Then we presented the approach for the analysis and modeling of landslide data using rare events logistic regression. Data include digital orthophotomaps (DOM), digital elevation models (DEM), topographical parameters (e.g. altitude, slope, aspect, profile curvature, plan curvature, sediment transport capacity index, stream power index, topographic wetness index), geological information and further different GIS layers including settlement, road net and rivers. Landslides were identified by monoscopic manual interpretation, and validated during the field investigation. The quality of susceptibility mapping was validated by splitting the study area into a training and a validation set. The prediction capability analysis showed that the landslide susceptibility map could be used for land planning in this region as well as emergency planning by local authorities. The study are of Longnan is located in southern Gansu province bordering Shanxi in the east and Sichuan in the south. The major geographic features in Longnan are the Qinba Mountains in the east, the Loess Plateau in the north, and the Tibetan Plateau in the west. It is part of the Central Han basin in the east and the Sichuan basin in the south. The geological environment is in particular determined by regional fault zones. Neotectonic movements are active, and seismic activities are frequent. The length from east to west is
ERIC Educational Resources Information Center
Tipton, Elizabeth; Pustejovsky, James E.
2015-01-01
Randomized experiments are commonly used to evaluate the effectiveness of educational interventions. The goal of the present investigation is to develop small-sample corrections for multiple contrast hypothesis tests (i.e., F-tests) such as the omnibus test of meta-regression fit or a test for equality of three or more levels of a categorical…
Breiterman-White, Randee; Reznicek, Jacci
2008-01-01
Holding doses of epoetin alfa (Epogen) alters the balance between red blood cell production and death rates, and leads to a decrease in hemoglobin (Hb) levels. Although clinical circumstances sometimes require that epoetin alfa doses be held, this can be minimized by monitoring longitudinal trends, predicting the probable future course of Hb, and intervening to proactively adjust epoetin alfa doses before holding is required.
Short-Term Effects of Particulate Matter on Stroke Attack: Meta-Regression and Meta-Analyses
Li, Xiu-Yang; Chen, Gao
2014-01-01
Background and Purpose Currently there are more and more studies on the association between short-term effects of exposure to particulate matter (PM) and the morbidity of stroke attack, but few have focused on stroke subtypes. The objective of this study is to assess the relationship between PM and stroke subtypes attack, which is uncertain now. Methods Meta-analyses, meta-regression and subgroup analyses were conducted to investigate the association between short-term effects of exposure to PM and the morbidity of different stroke subtypes from a number of epidemiologic studies (from 1997 to 2012). Results Nineteen articles were identified. Odds ratio (OR) of stroke attack associated with particular matter (“thoracic particles” [PM10]<10 µm in aerodynamic diameter, “fine particles” [PM2.5]<2.5 µm in aerodynamic diameter) increment of 10 µg/m3 was as effect size. PM10 exposure was related to an increase in risk of stroke attack (OR per 10 µg/m3 = 1.004, 95%CI: 1.001∼1.008) and PM2.5 exposure was not significantly associated with stroke attack (OR per 10 µg/m3 = 0.999, 95%CI: 0.994∼1.003). But when focused on stroke subtypes, PM2.5 (OR per 10 µg/m3 = 1.025; 95%CI, 1.001∼1.049) and PM10 (OR per 10 µg/m3 = 1.013; 95%CI, 1.001∼1.025) exposure were statistically significantly associated with an increased risk of ischemic stroke attack, while PM2.5 (all the studies showed no significant association) and PM10 (OR per 10 µg/m3 = 1.007; 95%CI, 0.992∼1.022) exposure were not associated with an increased risk of hemorrhagic stroke attack. Meta-regression found study design and area were two effective covariates. Conclusion PM2.5 and PM10 had different effects on different stroke subtypes. In the future, it's worthwhile to study the effects of PM to ischemic stroke and hemorrhagic stroke, respectively. PMID:24802512
ERIC Educational Resources Information Center
Tipton, Elizabeth; Pustejovsky, James E.
2015-01-01
Meta-analyses often include studies that report multiple effect sizes based on a common pool of subjects or that report effect sizes from several samples that were treated with very similar research protocols. The inclusion of such studies introduces dependence among the effect size estimates. When the number of studies is large, robust variance…
Kjelstrom, L.C.
1995-01-01
Previously developed U.S. Geological Survey regional regression models of runoff and 11 chemical constituents were evaluated to assess their suitability for use in urban areas in Boise and Garden City. Data collected in the study area were used to develop adjusted regional models of storm-runoff volumes and mean concentrations and loads of chemical oxygen demand, dissolved and suspended solids, total nitrogen and total ammonia plus organic nitrogen as nitrogen, total and dissolved phosphorus, and total recoverable cadmium, copper, lead, and zinc. Explanatory variables used in these models were drainage area, impervious area, land-use information, and precipitation data. Mean annual runoff volume and loads at the five outfalls were estimated from 904 individual storms during 1976 through 1993. Two methods were used to compute individual storm loads. The first method used adjusted regional models of storm loads and the second used adjusted regional models for mean concentration and runoff volume. For large storms, the first method seemed to produce excessively high loads for some constituents and the second method provided more reliable results for all constituents except suspended solids. The first method provided more reliable results for large storms for suspended solids.
Yoshiyama, R.M.; Van Winkle, W.; Kirk, B.L.; Stevens, D.E.
1981-06-01
The statistical dependence of recruitment level upon stock size and selected environmental variables was examined for three fish stocks: California striped bass (Morone saxatilis), Atlantic menhaden (Brevoortia tyrannus), and American shad (Alosa sapidissima). The analysis involved: (1) single and multiple linear regressions of recruitment against stock size and environmental variables; (b) nonlinear regressions of recruitment against stock size using unmodified Ricker and Beverton-Holt stock-recruitment models, followed by linear regression of residuals on environmental variables; and (c) nonlinear regressions using Ricker and Beverton-Holt models modified to include an environmental variable. The relative effectiveness of these three regression approaches in describing variation in recruitment level of the three fish stocks was evaluated, with effectiveness of regression models gauged by the magnitude of residual mean square values and by whether or not regression models reduced to simpler forms (due to parameter estimates not significantly different from 0.0) after being fitted to data. No single regression approach was consistently superior to the others in explaining variation in recruitment for all three fish stocks. Linear models appeared more effective than the other two regression approaches for striped bass, while modified stock-recruitment models showed the best fit to data for Atlantic menhaden and American shad. Although detailed aspects of the results may be specific to the analytical procedures and time series of data utilized, general features are still evident. Striped bass and Atlantic menhaden recruitment showed stronger statistical relationships to environmental variables than to stock size, whereas stock size apparently has been an important determinant of recruitment variation in American shad. 24 refs., 4 figs., 4 tabs.
Should we adjust for gestational age when analysing birth weights? The use of z-scores revisited.
Delbaere, Ilse; Vansteelandt, Stijn; De Bacquer, Dirk; Verstraelen, Hans; Gerris, Jan; De Sutter, Petra; Temmerman, Marleen
2007-08-01
Birth weight is the single most important risk indicator for neonatal and infant mortality and morbidity, which has led to the idiom that 'every ounce counts'. Birth weight in turn, however, tends to vary widely across populations as a result of differential fetal growth velocity with such demographic factors as ethnicity, maternal and paternal height and altitude of residence. Accordingly, it has been acknowledged that the appraisal of birth weight should rely on its position relative to the birth weight distribution of the background population. This is commonly done by standardizing birth weight through its deviation from the population mean in the given gestational age stratum, as can be obtained from population-customized birth weight nomograms. This issue was recently revisited in 'Human Reproduction' through a plea for reporting birth weight as z-scores. In this article, we argue that adjustment for factors, such as gestational age, which may lie on the causal pathway from exposures present at the time of conception [e.g. single-embryo transfer (SET) versus double-embryo transfer (DET)] to birth weight, may induce bias, regardless of whether the adjustment happens via stratification, regression or through the use of z-scores.
Asquith, William H.; Roussel, Meghan C.
2009-01-01
Annual peak-streamflow frequency estimates are needed for flood-plain management; for objective assessment of flood risk; for cost-effective design of dams, levees, and other flood-control structures; and for design of roads, bridges, and culverts. Annual peak-streamflow frequency represents the peak streamflow for nine recurrence intervals of 2, 5, 10, 25, 50, 100, 200, 250, and 500 years. Common methods for estimation of peak-streamflow frequency for ungaged or unmonitored watersheds are regression equations for each recurrence interval developed for one or more regions; such regional equations are the subject of this report. The method is based on analysis of annual peak-streamflow data from U.S. Geological Survey streamflow-gaging stations (stations). Beginning in 2007, the U.S. Geological Survey, in cooperation with the Texas Department of Transportation and in partnership with Texas Tech University, began a 3-year investigation concerning the development of regional equations to estimate annual peak-streamflow frequency for undeveloped watersheds in Texas. The investigation focuses primarily on 638 stations with 8 or more years of data from undeveloped watersheds and other criteria. The general approach is explicitly limited to the use of L-moment statistics, which are used in conjunction with a technique of multi-linear regression referred to as PRESS minimization. The approach used to develop the regional equations, which was refined during the investigation, is referred to as the 'L-moment-based, PRESS-minimized, residual-adjusted approach'. For the approach, seven unique distributions are fit to the sample L-moments of the data for each of 638 stations and trimmed means of the seven results of the distributions for each recurrence interval are used to define the station specific, peak-streamflow frequency. As a first iteration of regression, nine weighted-least-squares, PRESS-minimized, multi-linear regression equations are computed using the watershed
NASA Astrophysics Data System (ADS)
Wessollek, Christine; Karrasch, Pierre; Osunmadewa, Babatunde
2015-10-01
It seems to be obvious that precipitation has a major impact on greening during the rainy season in semi-arid regions. First results1 imply a strong dependence of NDVI on rainfall. Therefore it will be necessary to consider specific rainfall events besides the known ordinary annual cycle. Based on this fundamental idea, the paper will introduce the development of a rain adjusted vegetation index (RAVI). The index is based on the enhancement of the well-known normalized difference vegetation index (NDVI2) by means of TAMSAT rainfall data and includes a 3-step procedure of determining RAVI. Within the first step both time series were analysed over a period of 29 years to find best cross correlation values between TAMSAT rainfall and NDVI signal itself. The results indicate the strongest correlation for a weighted mean rainfall for a period of three months before the corresponding NDVI value. Based on these results different mathematical models (linear, logarithmic, square root, etc.) are tested to find a functional relation between the NDVI value and the 3-months rainfall period before (0.8). Finally, the resulting NDVI-Rain-Model can be used to determine a spatially individual correction factor to transform every NDVI value into an appropriate rain adjusted vegetation index (RAVI).
ERIC Educational Resources Information Center
Larzelere, Robert E.; Ferrer, Emilio; Kuhn, Brett R.; Danelia, Ketevan
2010-01-01
This study estimates the causal effects of six corrective actions for children's problem behaviors, comparing four types of longitudinal analyses that correct for pre-existing differences in a cohort of 1,464 4- and 5-year-olds from Canadian National Longitudinal Survey of Children and Youth (NLSCY) data. Analyses of residualized gain scores found…
Robertson, D.M.; Saad, D.A.; Heisey, D.M.
2006-01-01
Various approaches are used to subdivide large areas into regions containing streams that have similar reference or background water quality and that respond similarly to different factors. For many applications, such as establishing reference conditions, it is preferable to use physical characteristics that are not affected by human activities to delineate these regions. However, most approaches, such as ecoregion classifications, rely on land use to delineate regions or have difficulties compensating for the effects of land use. Land use not only directly affects water quality, but it is often correlated with the factors used to define the regions. In this article, we describe modifications to SPARTA (spatial regression-tree analysis), a relatively new approach applied to water-quality and environmental characteristic data to delineate zones with similar factors affecting water quality. In this modified approach, land-use-adjusted (residualized) water quality and environmental characteristics are computed for each site. Regression-tree analysis is applied to the residualized data to determine the most statistically important environmental characteristics describing the distribution of a specific water-quality constituent. Geographic information for small basins throughout the study area is then used to subdivide the area into relatively homogeneous environmental water-quality zones. For each zone, commonly used approaches are subsequently used to define its reference water quality and how its water quality responds to changes in land use. SPARTA is used to delineate zones of similar reference concentrations of total phosphorus and suspended sediment throughout the upper Midwestern part of the United States. ?? 2006 Springer Science+Business Media, Inc.
Mizukami, Akira; Matsue, Yuya; Naruse, Yoshihisa; Kowase, Shinya; Kurosaki, Kenji; Suzuki, Makoto; Matsumura, Akihiko; Nogami, Akihiko; Aonuma, Kazutaka; Hashimoto, Yuji
2016-09-01
The presented data were obtained from 982 consecutive patients receiving their first pacemaker implantation with right ventricular (RV) lead placement between January 2008 and December 2013 at two centers in Japan. Patients were divided into RV apical and septal pacing groups. Data of Kaplan-Meier survival analysis and Cox regression analysis are presented. Refer to the research article "Implications of right ventricular septal pacing for medium-term prognosis: propensity-matched analysis" (Mizukami et al., in press) [1] for further interpretation and discussion. PMID:27570808
2016-01-01
We estimate models of consumer food waste awareness and attitudes using responses from a national survey of U.S. residents. Our models are interpreted through the lens of several theories that describe how pro-social behaviors relate to awareness, attitudes and opinions. Our analysis of patterns among respondents’ food waste attitudes yields a model with three principal components: one that represents perceived practical benefits households may lose if food waste were reduced, one that represents the guilt associated with food waste, and one that represents whether households feel they could be doing more to reduce food waste. We find our respondents express significant agreement that some perceived practical benefits are ascribed to throwing away uneaten food, e.g., nearly 70% of respondents agree that throwing away food after the package date has passed reduces the odds of foodborne illness, while nearly 60% agree that some food waste is necessary to ensure meals taste fresh. We identify that these attitudinal responses significantly load onto a single principal component that may represent a key attitudinal construct useful for policy guidance. Further, multivariate regression analysis reveals a significant positive association between the strength of this component and household income, suggesting that higher income households most strongly agree with statements that link throwing away uneaten food to perceived private benefits. PMID:27441687
Qi, Danyi; Roe, Brian E
2016-01-01
We estimate models of consumer food waste awareness and attitudes using responses from a national survey of U.S. residents. Our models are interpreted through the lens of several theories that describe how pro-social behaviors relate to awareness, attitudes and opinions. Our analysis of patterns among respondents' food waste attitudes yields a model with three principal components: one that represents perceived practical benefits households may lose if food waste were reduced, one that represents the guilt associated with food waste, and one that represents whether households feel they could be doing more to reduce food waste. We find our respondents express significant agreement that some perceived practical benefits are ascribed to throwing away uneaten food, e.g., nearly 70% of respondents agree that throwing away food after the package date has passed reduces the odds of foodborne illness, while nearly 60% agree that some food waste is necessary to ensure meals taste fresh. We identify that these attitudinal responses significantly load onto a single principal component that may represent a key attitudinal construct useful for policy guidance. Further, multivariate regression analysis reveals a significant positive association between the strength of this component and household income, suggesting that higher income households most strongly agree with statements that link throwing away uneaten food to perceived private benefits.
Qi, Danyi; Roe, Brian E
2016-01-01
We estimate models of consumer food waste awareness and attitudes using responses from a national survey of U.S. residents. Our models are interpreted through the lens of several theories that describe how pro-social behaviors relate to awareness, attitudes and opinions. Our analysis of patterns among respondents' food waste attitudes yields a model with three principal components: one that represents perceived practical benefits households may lose if food waste were reduced, one that represents the guilt associated with food waste, and one that represents whether households feel they could be doing more to reduce food waste. We find our respondents express significant agreement that some perceived practical benefits are ascribed to throwing away uneaten food, e.g., nearly 70% of respondents agree that throwing away food after the package date has passed reduces the odds of foodborne illness, while nearly 60% agree that some food waste is necessary to ensure meals taste fresh. We identify that these attitudinal responses significantly load onto a single principal component that may represent a key attitudinal construct useful for policy guidance. Further, multivariate regression analysis reveals a significant positive association between the strength of this component and household income, suggesting that higher income households most strongly agree with statements that link throwing away uneaten food to perceived private benefits. PMID:27441687
de Vet, Emely; Chinapaw, Mai JM; de Boer, Michiel; Seidell, Jacob C; Brug, Johannes
2014-01-01
Background Playing video games contributes substantially to sedentary behavior in youth. A new generation of video games—active games—seems to be a promising alternative to sedentary games to promote physical activity and reduce sedentary behavior. At this time, little is known about correlates of active and non-active gaming among adolescents. Objective The objective of this study was to examine potential personal, social, and game-related correlates of both active and non-active gaming in adolescents. Methods A survey assessing game behavior and potential personal, social, and game-related correlates was conducted among adolescents (12-16 years, N=353) recruited via schools. Multivariable, multilevel logistic regression analyses, adjusted for demographics (age, sex and educational level of adolescents), were conducted to examine personal, social, and game-related correlates of active gaming ≥1 hour per week (h/wk) and non-active gaming >7 h/wk. Results Active gaming ≥1 h/wk was significantly associated with a more positive attitude toward active gaming (OR 5.3, CI 2.4-11.8; P<.001), a less positive attitude toward non-active games (OR 0.30, CI 0.1-0.6; P=.002), a higher score on habit strength regarding gaming (OR 1.9, CI 1.2-3.2; P=.008) and having brothers/sisters (OR 6.7, CI 2.6-17.1; P<.001) and friends (OR 3.4, CI 1.4-8.4; P=.009) who spend more time on active gaming and a little bit lower score on game engagement (OR 0.95, CI 0.91-0.997; P=.04). Non-active gaming >7 h/wk was significantly associated with a more positive attitude toward non-active gaming (OR 2.6, CI 1.1-6.3; P=.035), a stronger habit regarding gaming (OR 3.0, CI 1.7-5.3; P<.001), having friends who spend more time on non-active gaming (OR 3.3, CI 1.46-7.53; P=.004), and a more positive image of a non-active gamer (OR 2, CI 1.07–3.75; P=.03). Conclusions Various factors were significantly associated with active gaming ≥1 h/wk and non-active gaming >7 h/wk. Active gaming is most
Norström, Madelaine; Kristoffersen, Anja Bråthen; Görlach, Franziska Sophie; Nygård, Karin; Hopp, Petter
2015-01-01
In order to facilitate foodborne outbreak investigations there is a need to improve the methods for identifying the food products that should be sampled for laboratory analysis. The aim of this study was to examine the applicability of a likelihood ratio approach previously developed on simulated data, to real outbreak data. We used human case and food product distribution data from the Norwegian enterohaemorrhagic Escherichia coli outbreak in 2006. The approach was adjusted to include time, space smoothing and to handle missing or misclassified information. The performance of the adjusted likelihood ratio approach on the data originating from the HUS outbreak and control data indicates that the adjusted approach is promising and indicates that the adjusted approach could be a useful tool to assist and facilitate the investigation of food borne outbreaks in the future if good traceability are available and implemented in the distribution chain. However, the approach needs to be further validated on other outbreak data and also including other food products than meat products in order to make a more general conclusion of the applicability of the developed approach. PMID:26237468
Botha, J; de Ridder, J H; Potgieter, J C; Steyn, H S; Malan, L
2013-10-01
A recently proposed model for waist circumference cut points (RPWC), driven by increased blood pressure, was demonstrated in an African population. We therefore aimed to validate the RPWC by comparing the RPWC and the Joint Statement Consensus (JSC) models via Logistic Regression (LR) and Neural Networks (NN) analyses. Urban African gender groups (N=171) were stratified according to the JSC and RPWC cut point models. Ultrasound carotid intima media thickness (CIMT), blood pressure (BP) and fasting bloods (glucose, high density lipoprotein (HDL) and triglycerides) were obtained in a well-controlled setting. The RPWC male model (LR ROC AUC: 0.71, NN ROC AUC: 0.71) was practically equal to the JSC model (LR ROC AUC: 0.71, NN ROC AUC: 0.69) to predict structural vascular -disease. Similarly, the female RPWC model (LR ROC AUC: 0.84, NN ROC AUC: 0.82) and JSC model (LR ROC AUC: 0.82, NN ROC AUC: 0.81) equally predicted CIMT as surrogate marker for structural vascular disease. Odds ratios supported validity where prediction of CIMT revealed -clinical -significance, well over 1, for both the JSC and RPWC models in African males and females (OR 3.75-13.98). In conclusion, the proposed RPWC model was substantially validated utilizing linear and non-linear analyses. We therefore propose ethnic-specific WC cut points (African males, ≥90 cm; -females, ≥98 cm) to predict a surrogate marker for structural vascular disease.
Peluso, Marco E M; Munnia, Armelle; Ceppi, Marcello
2014-11-01
Exposures to bisphenol-A, a weak estrogenic chemical, largely used for the production of plastic containers, can affect the rodent behaviour. Thus, we examined the relationships between bisphenol-A and the anxiety-like behaviour, spatial skills, and aggressiveness, in 12 toxicity studies of rodent offspring from females orally exposed to bisphenol-A, while pregnant and/or lactating, by median and linear splines analyses. Subsequently, the meta-regression analysis was applied to quantify the behavioural changes. U-shaped, inverted U-shaped and J-shaped dose-response curves were found to describe the relationships between bisphenol-A with the behavioural outcomes. The occurrence of anxiogenic-like effects and spatial skill changes displayed U-shaped and inverted U-shaped curves, respectively, providing examples of effects that are observed at low-doses. Conversely, a J-dose-response relationship was observed for aggressiveness. When the proportion of rodents expressing certain traits or the time that they employed to manifest an attitude was analysed, the meta-regression indicated that a borderline significant increment of anxiogenic-like effects was present at low-doses regardless of sexes (β)=-0.8%, 95% C.I. -1.7/0.1, P=0.076, at ≤120 μg bisphenol-A. Whereas, only bisphenol-A-males exhibited a significant inhibition of spatial skills (β)=0.7%, 95% C.I. 0.2/1.2, P=0.004, at ≤100 μg/day. A significant increment of aggressiveness was observed in both the sexes (β)=67.9,C.I. 3.4, 172.5, P=0.038, at >4.0 μg. Then, bisphenol-A treatments significantly abrogated spatial learning and ability in males (P<0.001 vs. females). Overall, our study showed that developmental exposures to low-doses of bisphenol-A, e.g. ≤120 μg/day, were associated to behavioural aberrations in offspring.
Turner, Rebecca M.; Lloyd-Jones, Myfanwy; Anumba, Dilly O. C.; Smith, Gordon C. S.; Spiegelhalter, David J.; Squires, Hazel; Stevens, John W.; Sweeting, Michael J.; Urbaniak, Stanislaw J.; Webster, Robert; Thompson, Simon G.
2012-01-01
Background To estimate the effectiveness of routine antenatal anti-D prophylaxis for preventing sensitisation in pregnant Rhesus negative women, and to explore whether this depends on the treatment regimen adopted. Methods Ten studies identified in a previous systematic literature search were included. Potential sources of bias were systematically identified using bias checklists, and their impact and uncertainty were quantified using expert opinion. Study results were adjusted for biases and combined, first in a random-effects meta-analysis and then in a random-effects meta-regression analysis. Results In a conventional meta-analysis, the pooled odds ratio for sensitisation was estimated as 0.25 (95% CI 0.18, 0.36), comparing routine antenatal anti-D prophylaxis to control, with some heterogeneity (I2 = 19%). However, this naïve analysis ignores substantial differences in study quality and design. After adjusting for these, the pooled odds ratio for sensitisation was estimated as 0.31 (95% CI 0.17, 0.56), with no evidence of heterogeneity (I2 = 0%). A meta-regression analysis was performed, which used the data available from the ten anti-D prophylaxis studies to inform us about the relative effectiveness of three licensed treatments. This gave an 83% probability that a dose of 1250 IU at 28 and 34 weeks is most effective and a 76% probability that a single dose of 1500 IU at 28–30 weeks is least effective. Conclusion There is strong evidence for the effectiveness of routine antenatal anti-D prophylaxis for prevention of sensitisation, in support of the policy of offering routine prophylaxis to all non-sensitised pregnant Rhesus negative women. All three licensed dose regimens are expected to be effective. PMID:22319580
NASA Astrophysics Data System (ADS)
Liberman, Neomi; Ben-David Kolikant, Yifat; Beeri, Catriel
2012-09-01
Due to a program reform in Israel, experienced CS high-school teachers faced the need to master and teach a new programming paradigm. This situation served as an opportunity to explore the relationship between teachers' content knowledge (CK) and their pedagogical content knowledge (PCK). This article focuses on three case studies, with emphasis on one of them. Using observations and interviews, we examine how the teachers, we observed taught and what development of their teaching occurred as a result of their teaching experience, if at all. Our findings suggest that this situation creates a new hybrid state of teachers, which we term "regressed experts." These teachers incorporate in their professional practice some elements typical of novices and some typical of experts. We also found that these teachers' experience, although established when teaching a different CK, serve as a leverage to improve their knowledge and understanding of aspects of the new content.
[Structural adjustment, cultural adjustment?].
Dujardin, B; Dujardin, M; Hermans, I
2003-12-01
Over the last two decades, multiple studies have been conducted and many articles published about Structural Adjustment Programmes (SAPs). These studies mainly describe the characteristics of SAPs and analyse their economic consequences as well as their effects upon a variety of sectors: health, education, agriculture and environment. However, very few focus on the sociological and cultural effects of SAPs. Following a summary of SAP's content and characteristics, the paper briefly discusses the historical course of SAPs and the different critiques which have been made. The cultural consequences of SAPs are introduced and are described on four different levels: political, community, familial, and individual. These levels are analysed through examples from the literature and individual testimonies from people in the Southern Hemisphere. The paper concludes that SAPs, alongside economic globalisation processes, are responsible for an acute breakdown of social and cultural structures in societies in the South. It should be a priority, not only to better understand the situation and its determining factors, but also to intervene and act with strategies that support and reinvest in the social and cultural sectors, which is vital in order to allow for individuals and communities in the South to strengthen their autonomy and identify.
ERIC Educational Resources Information Center
Ashworth, Kristen E.; Pullen, Paige C.
2015-01-01
The purpose of this study was to compare the results of a regression discontinuity design (RDD) with those of an experimental design of a tiered vocabulary intervention for children at risk for reading disability to determine RDD's feasibility as a research methodology for this type of study. Researchers reanalyzed an archival dataset of a…
ERIC Educational Resources Information Center
Land, Kenneth C.; And Others
1994-01-01
Advantages of using logistic and hazards regression techniques in assessing the overall impact of a treatment program and the differential impact on client subgroups are examined and compared using data from a juvenile court program for status offenders. Implications are drawn for management and effectiveness of intensive supervision programs.…
Prediction in Multiple Regression.
ERIC Educational Resources Information Center
Osborne, Jason W.
2000-01-01
Presents the concept of prediction via multiple regression (MR) and discusses the assumptions underlying multiple regression analyses. Also discusses shrinkage, cross-validation, and double cross-validation of prediction equations and describes how to calculate confidence intervals around individual predictions. (SLD)
Nazarzadeh, Milad; Bidel, Zeinab; Mosavi Jarahi, Alireza; Esmaeelpour, Keihan; Menati, Walieh; Shakeri, Ali Asghar; Menati, Rostam; Kikhavani, Sattar; Saki, Kourosh
2015-09-01
Cannabis is the most widely used substance in the world. This study aimed to estimate the prevalence of cannabis lifetime use (CLU) in high school and college students of Iran and also to determine factors related to changes in prevalence. A systematic review of literature on cannabis use in Iran was conducted according to MOOSE guideline. Domestic scientific databases, PubMed/Medline, ISI Web of Knowledge, and Google Scholar, relevant reference lists, and relevant journals were searched up to April, 2014. Prevalences were calculated using the variance stabilizing double arcsine transformation and confidence intervals (CIs) estimated using the Wilson method. Heterogeneity was assessed by Cochran's Q statistic and I(2) index and causes of heterogeneity were evaluated using meta-regression model. In electronic database search, 4,000 citations were retrieved, producing a total of 33 studies. CLU was reported with a random effects pooled prevalence of 4.0% (95% CI = 3.0% to 5.0%). In subgroups of high school and college students, prevalences were 5.0% (95% CI = 3.0% to -7.0%) and 2.0% (95% CI = 2.0% to -3.0%), respectively. Meta-regression model indicated that prevalence is higher in college students (β = 0.089, p < .001), male gender (β = 0.017, p < .001), and is lower in studies with sampling versus census studies (β = -0.096, p < .001). This study reported that prevalence of CLU in Iranian students are lower than industrialized countries. In addition, gender, level of education, and methods of sampling are highly associated with changes in the prevalence of CLU across provinces.
Bjelakovic, Goran; Nikolova, Dimitrinka; Gluud, Christian
2013-01-01
Background and Aims Evidence shows that antioxidant supplements may increase mortality. Our aims were to assess whether different doses of beta-carotene, vitamin A, and vitamin E affect mortality in primary and secondary prevention randomized clinical trials with low risk of bias. Methods The present study is based on our 2012 Cochrane systematic review analyzing beneficial and harmful effects of antioxidant supplements in adults. Using random-effects meta-analyses, meta-regression analyses, and trial sequential analyses, we examined the association between beta-carotene, vitamin A, and vitamin E, and mortality according to their daily doses and doses below and above the recommended daily allowances (RDA). Results We included 53 randomized trials with low risk of bias (241,883 participants, aged 18 to 103 years, 44.6% women) assessing beta-carotene, vitamin A, and vitamin E. Meta-regression analysis showed that the dose of vitamin A was significantly positively associated with all-cause mortality. Beta-carotene in a dose above 9.6 mg significantly increased mortality (relative risk (RR) 1.06, 95% confidence interval (CI) 1.02 to 1.09, I2 = 13%). Vitamin A in a dose above the RDA (> 800 µg) did not significantly influence mortality (RR 1.08, 95% CI 0.98 to 1.19, I2 = 53%). Vitamin E in a dose above the RDA (> 15 mg) significantly increased mortality (RR 1.03, 95% CI 1.00 to 1.05, I2 = 0%). Doses below the RDAs did not affect mortality, but data were sparse. Conclusions Beta-carotene and vitamin E in doses higher than the RDA seem to significantly increase mortality, whereas we lack information on vitamin A. Dose of vitamin A was significantly associated with increased mortality in meta-regression. We lack information on doses below the RDA. Background All essential compounds to stay healthy cannot be synthesized in our body. Therefore, these compounds must be taken through our diet or obtained in other ways [1]. Oxidative stress has been suggested to cause a
Bailey-Wilson, Joan E.; Brennan, Jennifer S.; Bull, Shelley B; Culverhouse, Robert; Kim, Yoonhee; Jiang, Yuan; Jung, Jeesun; Li, Qing; Lamina, Claudia; Liu, Ying; Mägi, Reedik; Niu, Yue S.; Simpson, Claire L.; Wang, Libo; Yilmaz, Yildiz E.; Zhang, Heping; Zhang, Zhaogong
2012-01-01
Group 14 of Genetic Analysis Workshop 17 examined several issues related to analysis of complex traits using DNA sequence data. These issues included novel methods for analyzing rare genetic variants in an aggregated manner (often termed collapsing rare variants), evaluation of various study designs to increase power to detect effects of rare variants, and the use of machine learning approaches to model highly complex heterogeneous traits. Various published and novel methods for analyzing traits with extreme locus and allelic heterogeneity were applied to the simulated quantitative and disease phenotypes. Overall, we conclude that power is (as expected) dependent on locus-specific heritability or contribution to disease risk, large samples will be required to detect rare causal variants with small effect sizes, extreme phenotype sampling designs may increase power for smaller laboratory costs, methods that allow joint analysis of multiple variants per gene or pathway are more powerful in general than analyses of individual rare variants, population-specific analyses can be optimal when different subpopulations harbor private causal mutations, and machine learning methods may be useful for selecting subsets of predictors for follow-up in the presence of extreme locus heterogeneity and large numbers of potential predictors. PMID:22128066
NASA Technical Reports Server (NTRS)
Duda, David P.; Minnis, Patrick
2009-01-01
Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.
ERIC Educational Resources Information Center
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
NASA Technical Reports Server (NTRS)
Duda, David P.; Minnis, Patrick
2009-01-01
Straightforward application of the Schmidt-Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy, the percent correct (PC) and the Hanssen-Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher when the climatological frequency of contrail occurrence is used as the critical threshold, while the PC scores are higher when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85 percent for both the prediction of contrail occurrence and non-occurrence, although in practice, larger errors would be anticipated.
Shrinkage regression-based methods for microarray missing value imputation
2013-01-01
Background Missing values commonly occur in the microarray data, which usually contain more than 5% missing values with up to 90% of genes affected. Inaccurate missing value estimation results in reducing the power of downstream microarray data analyses. Many types of methods have been developed to estimate missing values. Among them, the regression-based methods are very popular and have been shown to perform better than the other types of methods in many testing microarray datasets. Results To further improve the performances of the regression-based methods, we propose shrinkage regression-based methods. Our methods take the advantage of the correlation structure in the microarray data and select similar genes for the target gene by Pearson correlation coefficients. Besides, our methods incorporate the least squares principle, utilize a shrinkage estimation approach to adjust the coefficients of the regression model, and then use the new coefficients to estimate missing values. Simulation results show that the proposed methods provide more accurate missing value estimation in six testing microarray datasets than the existing regression-based methods do. Conclusions Imputation of missing values is a very important aspect of microarray data analyses because most of the downstream analyses require a complete dataset. Therefore, exploring accurate and efficient methods for estimating missing values has become an essential issue. Since our proposed shrinkage regression-based methods can provide accurate missing value estimation, they are competitive alternatives to the existing regression-based methods. PMID:24565159
Mediating Effects of Relationships with Mentors on College Adjustment
ERIC Educational Resources Information Center
Lenz, A. Stephen
2014-01-01
This study examined the relationship between student adjustment to college and relational health with peers, mentors, and the community. Data were collected from 80 undergraduate students completing their first semester of course work at a large university in the mid-South. A series of simultaneous multiple regression analyses indicated that…
Huang, Dong; Cabral, Ricardo; De la Torre, Fernando
2016-02-01
Discriminative methods (e.g., kernel regression, SVM) have been extensively used to solve problems such as object recognition, image alignment and pose estimation from images. These methods typically map image features ( X) to continuous (e.g., pose) or discrete (e.g., object category) values. A major drawback of existing discriminative methods is that samples are directly projected onto a subspace and hence fail to account for outliers common in realistic training sets due to occlusion, specular reflections or noise. It is important to notice that existing discriminative approaches assume the input variables X to be noise free. Thus, discriminative methods experience significant performance degradation when gross outliers are present. Despite its obvious importance, the problem of robust discriminative learning has been relatively unexplored in computer vision. This paper develops the theory of robust regression (RR) and presents an effective convex approach that uses recent advances on rank minimization. The framework applies to a variety of problems in computer vision including robust linear discriminant analysis, regression with missing data, and multi-label classification. Several synthetic and real examples with applications to head pose estimation from images, image and video classification and facial attribute classification with missing data are used to illustrate the benefits of RR. PMID:26761740
Racial identity and reflected appraisals as influences on Asian Americans' racial adjustment.
Alvarez, A N; Helms, J E
2001-08-01
J. E. Helms's (1990) racial identity psychodiagnostic model was used to examine the contribution of racial identity schemas and reflected appraisals to the development of healthy racial adjustment of Asian American university students (N = 188). Racial adjustment was operationally defined as collective self-esteem and awareness of anti-Asian racism. Multiple regression analyses suggested that racial identity schemas and reflected appraisals were significantly predictive of Asian Americans' racial adjustment. Implications for counseling and future research are discussed.
Regression problems for magnitudes
NASA Astrophysics Data System (ADS)
Castellaro, S.; Mulargia, F.; Kagan, Y. Y.
2006-06-01
Least-squares linear regression is so popular that it is sometimes applied without checking whether its basic requirements are satisfied. In particular, in studying earthquake phenomena, the conditions (a) that the uncertainty on the independent variable is at least one order of magnitude smaller than the one on the dependent variable, (b) that both data and uncertainties are normally distributed and (c) that residuals are constant are at times disregarded. This may easily lead to wrong results. As an alternative to least squares, when the ratio between errors on the independent and the dependent variable can be estimated, orthogonal regression can be applied. We test the performance of orthogonal regression in its general form against Gaussian and non-Gaussian data and error distributions and compare it with standard least-square regression. General orthogonal regression is found to be superior or equal to the standard least squares in all the cases investigated and its use is recommended. We also compare the performance of orthogonal regression versus standard regression when, as often happens in the literature, the ratio between errors on the independent and the dependent variables cannot be estimated and is arbitrarily set to 1. We apply these results to magnitude scale conversion, which is a common problem in seismology, with important implications in seismic hazard evaluation, and analyse it through specific tests. Our analysis concludes that the commonly used standard regression may induce systematic errors in magnitude conversion as high as 0.3-0.4, and, even more importantly, this can introduce apparent catalogue incompleteness, as well as a heavy bias in estimates of the slope of the frequency-magnitude distributions. All this can be avoided by using the general orthogonal regression in magnitude conversions.
ERIC Educational Resources Information Center
Murdock, Tamera B.; Bolch, Megan B.
2005-01-01
This study examined the relations between school climate and school adjustment among 101 lesbian, gay, and bisexual (LGB) high school students and the moderating influence of social support on those relations. Students completed surveys to assess three aspects of the school climate (the school's exclusion/inclusion of LGB people, personal…
NASA Astrophysics Data System (ADS)
Agustí-Panareda, Anna; Massart, Sébastien; Chevallier, Frédéric; Balsamo, Gianpaolo; Boussetta, Souhail; Dutra, Emanuel; Beljaars, Anton
2016-08-01
Forecasting atmospheric CO2 daily at the global scale with a good accuracy like it is done for the weather is a challenging task. However, it is also one of the key areas of development to bridge the gaps between weather, air quality and climate models. The challenge stems from the fact that atmospheric CO2 is largely controlled by the CO2 fluxes at the surface, which are difficult to constrain with observations. In particular, the biogenic fluxes simulated by land surface models show skill in detecting synoptic and regional-scale disturbances up to sub-seasonal time-scales, but they are subject to large seasonal and annual budget errors at global scale, usually requiring a posteriori adjustment. This paper presents a scheme to diagnose and mitigate model errors associated with biogenic fluxes within an atmospheric CO2 forecasting system. The scheme is an adaptive scaling procedure referred to as a biogenic flux adjustment scheme (BFAS), and it can be applied automatically in real time throughout the forecast. The BFAS method generally improves the continental budget of CO2 fluxes in the model by combining information from three sources: (1) retrospective fluxes estimated by a global flux inversion system, (2) land-use information, (3) simulated fluxes from the model. The method is shown to produce enhanced skill in the daily CO2 10-day forecasts without requiring continuous manual intervention. Therefore, it is particularly suitable for near-real-time CO2 analysis and forecasting systems.
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.…
Adolescent suicide attempts and adult adjustment
Brière, Frédéric N.; Rohde, Paul; Seeley, John R.; Klein, Daniel; Lewinsohn, Peter M.
2014-01-01
Background Adolescent suicide attempts are disproportionally prevalent and frequently of low severity, raising questions regarding their long-term prognostic implications. In this study, we examined whether adolescent attempts were associated with impairments related to suicidality, psychopathology, and psychosocial functioning in adulthood (objective 1) and whether these impairments were better accounted for by concurrent adolescent confounders (objective 2). Method 816 adolescents were assessed using interviews and questionnaires at four time points from adolescence to adulthood. We examined whether lifetime suicide attempts in adolescence (by T2, mean age 17) predicted adult outcomes (by T4, mean age 30) using linear and logistic regressions in unadjusted models (objective 1) and adjusting for sociodemographic background, adolescent psychopathology, and family risk factors (objective 2). Results In unadjusted analyses, adolescent suicide attempts predicted poorer adjustment on all outcomes, except those related to social role status. After adjustment, adolescent attempts remained predictive of axis I and II psychopathology (anxiety disorder, antisocial and borderline personality disorder symptoms), global and social adjustment, risky sex, and psychiatric treatment utilization. However, adolescent attempts no longer predicted most adult outcomes, notably suicide attempts and major depressive disorder. Secondary analyses indicated that associations did not differ by sex and attempt characteristics (intent, lethality, recurrence). Conclusions Adolescent suicide attempters are at high risk of protracted and wide-ranging impairments, regardless of the characteristics of their attempt. Although attempts specifically predict (and possibly influence) several outcomes, results suggest that most impairments reflect the confounding contributions of other individual and family problems or vulnerabilites in adolescent attempters. PMID:25421360
Application and Interpretation of Hierarchical Multiple Regression.
Jeong, Younhee; Jung, Mi Jung
2016-01-01
The authors reported the association between motivation and self-management behavior of individuals with chronic low back pain after adjusting control variables using hierarchical multiple regression (). This article describes details of the hierarchical regression applying the actual data used in the article by , including how to test assumptions, run the statistical tests, and report the results. PMID:27648796
Urinary arsenic concentration adjustment factors and malnutrition.
Nermell, Barbro; Lindberg, Anna-Lena; Rahman, Mahfuzar; Berglund, Marika; Persson, Lars Ake; El Arifeen, Shams; Vahter, Marie
2008-02-01
This study aims at evaluating the suitability of adjusting urinary concentrations of arsenic, or any other urinary biomarker, for variations in urine dilution by creatinine and specific gravity in a malnourished population. We measured the concentrations of metabolites of inorganic arsenic, creatinine and specific gravity in spot urine samples collected from 1466 individuals, 5-88 years of age, in Matlab, rural Bangladesh, where arsenic-contaminated drinking water and malnutrition are prevalent (about 30% of the adults had body mass index (BMI) below 18.5 kg/m(2)). The urinary concentrations of creatinine were low; on average 0.55 g/L in the adolescents and adults and about 0.35 g/L in the 5-12 years old children. Therefore, adjustment by creatinine gave much higher numerical values for the urinary arsenic concentrations than did the corresponding data expressed as microg/L, adjusted by specific gravity. As evaluated by multiple regression analyses, urinary creatinine, adjusted by specific gravity, was more affected by body size, age, gender and season than was specific gravity. Furthermore, urinary creatinine was found to be significantly associated with urinary arsenic, which further disqualifies the creatinine adjustment. PMID:17900556
A Study of Perfectionism, Attachment, and College Student Adjustment: Testing Mediational Models.
ERIC Educational Resources Information Center
Hood, Camille A.; Kubal, Anne E.; Pfaller, Joan; Rice, Kenneth G.
Mediational models predicting college students' adjustment were tested using regression analyses. Contemporary adult attachment theory was employed to explore the cognitive/affective mechanisms by which adult attachment and perfectionism affect various aspects of psychological functioning. Consistent with theoretical expectations, results…
Native American Racial Identity Development and College Adjustment at Two-Year Institutions
ERIC Educational Resources Information Center
Watson, Joshua C.
2009-01-01
In this study, a series of simultaneous multiple regression analyses were conducted to examine the relationship between racial identity development and college adjustment for a sample of 76 Choctaw community college students in the South. Results indicated that 3 of the 4 racial identity statuses (dissonance, immersion-emersion, and…
Disability and Coping as Predictors of Psychological Adjustment to Rheumatoid Arthritis.
ERIC Educational Resources Information Center
Revenson, Tracey A.; Felton, Barbara J.
1989-01-01
Examined degree to which self-reported functional disability and coping efforts contributed to psychological adjustment among 45 rheumatoid arthritis patients over six months. Hierarchical multiple regression analyses indicated that increases in disability were related to decreased acceptance of illness and increased negative affect, while coping…
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,…
Exploring Mexican American adolescent romantic relationship profiles and adjustment.
Moosmann, Danyel A V; Roosa, Mark W
2015-08-01
Although Mexican Americans are the largest ethnic minority group in the nation, knowledge is limited regarding this population's adolescent romantic relationships. This study explored whether 12th grade Mexican Americans' (N = 218; 54% female) romantic relationship characteristics, cultural values, and gender created unique latent classes and if so, whether they were linked to adjustment. Latent class analyses suggested three profiles including, relatively speaking, higher, satisfactory, and lower quality romantic relationships. Regression analyses indicated these profiles had distinct associations with adjustment. Specifically, adolescents with higher and satisfactory quality romantic relationships reported greater future family expectations, higher self-esteem, and fewer externalizing symptoms than those with lower quality romantic relationships. Similarly, adolescents with higher quality romantic relationships reported greater academic self-efficacy and fewer sexual partners than those with lower quality romantic relationships. Overall, results suggested higher quality romantic relationships were most optimal for adjustment. Future research directions and implications are discussed. PMID:26141198
Na, Hyunjoo; Dancy, Barbara L; Park, Chang
2015-06-01
The study's purpose was to explore whether frequency of cyberbullying victimization, cognitive appraisals, and coping strategies were associated with psychological adjustments among college student cyberbullying victims. A convenience sample of 121 students completed questionnaires. Linear regression analyses found frequency of cyberbullying victimization, cognitive appraisals, and coping strategies respectively explained 30%, 30%, and 27% of the variance in depression, anxiety, and self-esteem. Frequency of cyberbullying victimization and approach and avoidance coping strategies were associated with psychological adjustments, with avoidance coping strategies being associated with all three psychological adjustments. Interventions should focus on teaching cyberbullying victims to not use avoidance coping strategies. PMID:26001714
Life adjustment correlates of physical self-concepts.
Sonstroem, R J; Potts, S A
1996-05-01
This research tested relationships between physical self-concepts and contemporary measures of life adjustment. University students (119 females, 126 males) completed the Physical Self-Perception Profile assessing self-concepts of sport competence, physical condition, attractive body, strength, and general physical self-worth. Multiple regression found significant associations (P < 0.05 to P < 0.001) in hypothesized directions between physical self-concepts and positive affect, negative affect, depression, and health complaints in 17 of 20 analyses. Thirteen of these relationships remained significant when controlling for the Bonferroni effect. Hierarchical multiple regression examined the unique contribution of physical self-perceptions in predicting each adjustment variable after accounting for the effects of global self-esteem and two measures of social desirability. Physical self-concepts significantly improved associations with life adjustment (P < 0.05 to P < 0.05) in three of the eight analyses across gender and approached significance in three others. These data demonstrate that self-perceptions of physical competence in college students are essentially related to life adjustment, independent of the effects of social desirability and global self-esteem. These links are mainly with perceptions of sport competence in males and with perceptions of physical condition, attractive body, and general physical self-worth in both males and females. PMID:9148094
Psychosocial adjustment to ALS: a longitudinal study
Matuz, Tamara; Birbaumer, Niels; Hautzinger, Martin; Kübler, Andrea
2015-01-01
For the current study the Lazarian stress-coping theory and the appendant model of psychosocial adjustment to chronic illness and disabilities (Pakenham, 1999) has shaped the foundation for identifying determinants of adjustment to ALS. We aimed to investigate the evolution of psychosocial adjustment to ALS and to determine its long-term predictors. A longitudinal study design with four measurement time points was therefore, used to assess patients' quality of life, depression, and stress-coping model related aspects, such as illness characteristics, social support, cognitive appraisals, and coping strategies during a period of 2 years. Regression analyses revealed that 55% of the variance of severity of depressive symptoms and 47% of the variance in quality of life at T2 was accounted for by all the T1 predictor variables taken together. On the level of individual contributions, protective buffering, and appraisal of own coping potential accounted for a significant percentage in the variance in severity of depressive symptoms, whereas problem management coping strategies explained variance in quality of life scores. Illness characteristics at T2 did not explain any variance of both adjustment outcomes. Overall, the pattern of the longitudinal results indicated stable depressive symptoms and quality of life indices reflecting a successful adjustment to the disease across four measurement time points during a period of about two years. Empirical evidence is provided for the predictive value of social support, cognitive appraisals, and coping strategies, but not illness parameters such as severity and duration for adaptation to ALS. The current study contributes to a better conceptualization of adjustment, allowing us to provide evidence-based support beyond medical and physical intervention for people with ALS. PMID:26441696
ERIC Educational Resources Information Center
Benson, Paul R.; Kersh, Joanne
2011-01-01
Using data drawn from a longitudinal study of families of children with ASD, the current study examined the impact of marital quality on three indicators of maternal psychological adjustment: depressed mood, parenting efficacy, and subjective well-being. Multiple regression analyses indicated marital quality to be a significant cross-sectional and…
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…
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.
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.
Harry, H.H.
1988-03-11
Abstract and method for the adjustment and alignment of shafts in high power devices. A plurality of adjacent rotatable angled cylinders are positioned between a base and the shaft to be aligned which when rotated introduce an axial offset. The apparatus is electrically conductive and constructed of a structurally rigid material. The angled cylinders allow the shaft such as the center conductor in a pulse line machine to be offset in any desired alignment position within the range of the apparatus. 3 figs.
Harry, Herbert H.
1989-01-01
Apparatus and method for the adjustment and alignment of shafts in high power devices. A plurality of adjacent rotatable angled cylinders are positioned between a base and the shaft to be aligned which when rotated introduce an axial offset. The apparatus is electrically conductive and constructed of a structurally rigid material. The angled cylinders allow the shaft such as the center conductor in a pulse line machine to be offset in any desired alignment position within the range of the apparatus.
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.
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
NASA Astrophysics Data System (ADS)
Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany
2016-07-01
Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.
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…
A Simulation Investigation of Principal Component Regression.
ERIC Educational Resources Information Center
Allen, David E.
Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…
Turner, Elizabeth L.; Perel, Pablo; Clayton, Tim; Edwards, Phil; Hernández, Adrian V.; Roberts, Ian; Shakur, Haleema; Steyerberg, Ewout W.
2013-01-01
Objective We aimed to determine to what extent covariate adjustment could affect power in a randomized controlled trial (RCT) of a heterogeneous population with traumatic brain injury (TBI). Study Design and Setting We analyzed 14-day mortality in 9497 participants in the Corticosteroid Randomisation After Significant Head Injury (CRASH) RCT of corticosteroid vs. placebo. Adjustment was made using logistic regression for baseline covariates of two validated risk models derived from external data (IMPACT) and from the CRASH data. The relative sample size (RESS) measure, defined as the ratio of the sample size required by an adjusted analysis to attain the same power as the unadjusted reference analysis, was used to assess the impact of adjustment. Results Corticosteroid was associated with higher mortality compared to placebo (OR=1.25, 95% CI: 1.13, 1.39). RESS of 0.79 and 0.73 were obtained by adjustment using the IMPACT and CRASH models, respectively, which for example implies an increase from 80% to 88% and 91% power, respectively. Conclusion Moderate gains in power may be obtained using covariate adjustment from logistic regression in heterogeneous conditions such as TBI. Although analyses of RCTs might consider covariate adjustment to improve power, we caution against this approach in the planning of RCTs. PMID:22169080
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…
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.
Arruda Viani, Gustavo; Stefano, Eduardo Jose; Vendito Soares, Francisco; Afonso, Sergio Luis
2011-07-15
Purpose: To evaluate whether the risk of local recurrence depends on the biologic effective dose (BED) or fractionation dose in patients with resectable rectal cancer undergoing preoperative radiotherapy (RT) compared with surgery alone. Methods and Materials: A meta-analysis of randomized controlled trials (RCTs) was performed. The MEDLINE, Embase, CancerLit, and Cochrane Library databases were systematically searched for evidence. To evaluate the dose-response relationship, we conducted a meta-regression analysis. Four subgroups were created: Group 1, RCTs with a BED >30 Gy{sub 10} and a short RT schedule; Group 2, RCTs with BED >30 Gy{sub 10} and a long RT schedule; Group 3, RCTs with BED {<=}30 Gy{sub 10} and a short RT schedule; and Group 4, RCTs with BED {<=}30 Gy{sub 10} and a long RT schedule. Results: Our review identified 21 RCTs, yielding 9,097 patients. The pooled results from these 21 randomized trials of preoperative RT showed a significant reduction in mortality for groups 1 (p = .004) and 2 (p = .03). For local recurrence, the results were also significant in groups 1 (p = .00001) and 2 (p = .00001).The only subgroup that showed a greater sphincter preservation (SP) rate than surgery was group 2 (p = .03). The dose-response curve was linear (p = .006), and RT decreased the risk of local recurrence by about 1.7% for each Gy{sub 10} of BED. Conclusion: Our data have shown that RT with a BED of >30 Gy{sub 10} is more efficient in reducing local recurrence and mortality rates than a BED of {<=}30 Gy{sub 10}, independent of the schedule of fractionation used. A long RT schedule with a BED of >30 Gy{sub 10} should be recommended for sphincter preservation.
Time series regression model for infectious disease and weather.
Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro
2015-10-01
Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context.
Multivariate Regression with Calibration*
Liu, Han; Wang, Lie; Zhao, Tuo
2014-01-01
We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smoothed proximal gradient algorithm which has a worst-case iteration complexity O(1/ε), where ε is a pre-specified numerical accuracy. Theoretically, we prove that CMR achieves the optimal rate of convergence in parameter estimation. We illustrate the usefulness of CMR by thorough numerical simulations and show that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR on a brain activity prediction problem and find that CMR is as competitive as the handcrafted model created by human experts. PMID:25620861
Metamorphic geodesic regression.
Hong, Yi; Joshi, Sarang; Sanchez, Mar; Styner, Martin; Niethammer, Marc
2012-01-01
We propose a metamorphic geodesic regression approach approximating spatial transformations for image time-series while simultaneously accounting for intensity changes. Such changes occur for example in magnetic resonance imaging (MRI) studies of the developing brain due to myelination. To simplify computations we propose an approximate metamorphic geodesic regression formulation that only requires pairwise computations of image metamorphoses. The approximated solution is an appropriately weighted average of initial momenta. To obtain initial momenta reliably, we develop a shooting method for image metamorphosis.
Logistic regression: a brief primer.
Stoltzfus, Jill C
2011-10-01
Regression techniques are versatile in their application to medical research because they can measure associations, predict outcomes, and control for confounding variable effects. As one such technique, logistic regression is an efficient and powerful way to analyze the effect of a group of independent variables on a binary outcome by quantifying each independent variable's unique contribution. Using components of linear regression reflected in the logit scale, logistic regression iteratively identifies the strongest linear combination of variables with the greatest probability of detecting the observed outcome. Important considerations when conducting logistic regression include selecting independent variables, ensuring that relevant assumptions are met, and choosing an appropriate model building strategy. For independent variable selection, one should be guided by such factors as accepted theory, previous empirical investigations, clinical considerations, and univariate statistical analyses, with acknowledgement of potential confounding variables that should be accounted for. Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers. Additionally, there should be an adequate number of events per independent variable to avoid an overfit model, with commonly recommended minimum "rules of thumb" ranging from 10 to 20 events per covariate. Regarding model building strategies, the three general types are direct/standard, sequential/hierarchical, and stepwise/statistical, with each having a different emphasis and purpose. Before reaching definitive conclusions from the results of any of these methods, one should formally quantify the model's internal validity (i.e., replicability within the same data set) and external validity (i.e., generalizability beyond the current sample). The resulting logistic regression model
Predictors of sociocultural adjustment among sojourning Malaysian students in Britain.
Swami, Viren
2009-08-01
The process of cross-cultural migration may be particularly difficult for students travelling overseas for further or higher education, especially where qualitative differences exist between the home and host nations. The present study examined the sociocultural adjustment of sojourning Malaysian students in Britain. Eighty-one Malay and 110 Chinese students enrolled in various courses answered a self-report questionnaire that examined various aspects of sociocultural adjustment. A series of one-way analyses of variance showed that Malay participants experienced poorer sociocultural adjustment in comparison with their Chinese counterparts. They were also less likely than Chinese students to have contact with co-nationals and host nationals, more likely to perceive their actual experience in Britain as worse than they had expected, and more likely to perceive greater cultural distance and greater discrimination. The results of regression analyses showed that, for Malay participants, perceived discrimination accounted for the greatest proportion of variance in sociocultural adjustment (73%), followed by English language proficiency (10%) and contact with host nationals (4%). For Chinese participants, English language proficiency was the strongest predictor of sociocultural adjustment (54%), followed by expectations of life in Britain (18%) and contact with host nationals (3%). By contrast, participants' sex, age, and length of residence failed to emerge as significant predictors for either ethnic group. Possible explanations for this pattern of findings are discussed, including the effects of Islamophobia on Malay-Muslims in Britain, possible socioeconomic differences between Malay and Chinese students, and personality differences between the two ethnic groups. The results are further discussed in relation to practical steps that can be taken to improve the sociocultural adjustment of sojourning students in Britain. PMID:22029555
Tarpey, Thaddeus; Petkova, Eva
2010-07-01
Finite mixture models have come to play a very prominent role in modelling data. The finite mixture model is predicated on the assumption that distinct latent groups exist in the population. The finite mixture model therefore is based on a categorical latent variable that distinguishes the different groups. Often in practice distinct sub-populations do not actually exist. For example, disease severity (e.g. depression) may vary continuously and therefore, a distinction of diseased and not-diseased may not be based on the existence of distinct sub-populations. Thus, what is needed is a generalization of the finite mixture's discrete latent predictor to a continuous latent predictor. We cast the finite mixture model as a regression model with a latent Bernoulli predictor. A latent regression model is proposed by replacing the discrete Bernoulli predictor by a continuous latent predictor with a beta distribution. Motivation for the latent regression model arises from applications where distinct latent classes do not exist, but instead individuals vary according to a continuous latent variable. The shapes of the beta density are very flexible and can approximate the discrete Bernoulli distribution. Examples and a simulation are provided to illustrate the latent regression model. In particular, the latent regression model is used to model placebo effect among drug treated subjects in a depression study. PMID:20625443
Semiparametric Regression Pursuit.
Huang, Jian; Wei, Fengrong; Ma, Shuangge
2012-10-01
The semiparametric partially linear model allows flexible modeling of covariate effects on the response variable in regression. It combines the flexibility of nonparametric regression and parsimony of linear regression. The most important assumption in the existing methods for the estimation in this model is to assume a priori that it is known which covariates have a linear effect and which do not. However, in applied work, this is rarely known in advance. We consider the problem of estimation in the partially linear models without assuming a priori which covariates have linear effects. We propose a semiparametric regression pursuit method for identifying the covariates with a linear effect. Our proposed method is a penalized regression approach using a group minimax concave penalty. Under suitable conditions we show that the proposed approach is model-pursuit consistent, meaning that it can correctly determine which covariates have a linear effect and which do not with high probability. The performance of the proposed method is evaluated using simulation studies, which support our theoretical results. A real data example is used to illustrated the application of the proposed method. PMID:23559831
[Understanding logistic regression].
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.
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…
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.
Corbin, Marine; Richiardi, Lorenzo; Vermeulen, Roel; Kromhout, Hans; Merletti, Franco; Peters, Susan; Simonato, Lorenzo; Steenland, Kyle; Pearce, Neil; Maule, Milena
2012-01-01
Background Occupational studies often involve multiple comparisons and therefore suffer from false positive findings. Semi-Bayes adjustment methods have sometimes been used to address this issue. Hierarchical regression is a more general approach, including Semi-Bayes adjustment as a special case, that aims at improving the validity of standard maximum-likelihood estimates in the presence of multiple comparisons by incorporating similarities between the exposures of interest in a second-stage model. Methodology/Principal Findings We re-analysed data from an occupational case-control study of lung cancer, applying hierarchical regression. In the second-stage model, we included the exposure to three known lung carcinogens (asbestos, chromium and silica) for each occupation, under the assumption that occupations entailing similar carcinogenic exposures are associated with similar risks of lung cancer. Hierarchical regression estimates had smaller confidence intervals than maximum-likelihood estimates. The shrinkage toward the null was stronger for extreme, less stable estimates (e.g., “specialised farmers”: maximum-likelihood OR: 3.44, 95%CI 0.90–13.17; hierarchical regression OR: 1.53, 95%CI 0.63–3.68). Unlike Semi-Bayes adjustment toward the global mean, hierarchical regression did not shrink all the ORs towards the null (e.g., “Metal smelting, converting and refining furnacemen”: maximum-likelihood OR: 1.07, Semi-Bayes OR: 1.06, hierarchical regression OR: 1.26). Conclusions/Significance Hierarchical regression could be a valuable tool in occupational studies in which disease risk is estimated for a large amount of occupations when we have information available on the key carcinogenic exposures involved in each occupation. With the constant progress in exposure assessment methods in occupational settings and the availability of Job Exposure Matrices, it should become easier to apply this approach. PMID:22701732
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 connection.…
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…
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.
Mechanisms of neuroblastoma regression
Brodeur, Garrett M.; Bagatell, Rochelle
2014-01-01
Recent genomic and biological studies of neuroblastoma have shed light on the dramatic heterogeneity in the clinical behaviour of this disease, which spans from spontaneous regression or differentiation in some patients, to relentless disease progression in others, despite intensive multimodality therapy. This evidence also suggests several possible mechanisms to explain the phenomena of spontaneous regression in neuroblastomas, including neurotrophin deprivation, humoral or cellular immunity, loss of telomerase activity and alterations in epigenetic regulation. A better understanding of the mechanisms of spontaneous regression might help to identify optimal therapeutic approaches for patients with these tumours. Currently, the most druggable mechanism is the delayed activation of developmentally programmed cell death regulated by the tropomyosin receptor kinase A pathway. Indeed, targeted therapy aimed at inhibiting neurotrophin receptors might be used in lieu of conventional chemotherapy or radiation in infants with biologically favourable tumours that require treatment. Alternative approaches consist of breaking immune tolerance to tumour antigens or activating neurotrophin receptor pathways to induce neuronal differentiation. These approaches are likely to be most effective against biologically favourable tumours, but they might also provide insights into treatment of biologically unfavourable tumours. We describe the different mechanisms of spontaneous neuroblastoma regression and the consequent therapeutic approaches. PMID:25331179
Regression Verification Using Impact Summaries
NASA Technical Reports Server (NTRS)
Backes, John; Person, Suzette J.; Rungta, Neha; Thachuk, Oksana
2013-01-01
versions [19]. These techniques compare two programs with a large degree of syntactic similarity to prove that portions of one program version are equivalent to the other. Regression verification can be used for guaranteeing backward compatibility, and for showing behavioral equivalence in programs with syntactic differences, e.g., when a program is refactored to improve its performance, maintainability, or readability. Existing regression verification techniques leverage similarities between program versions by using abstraction and decomposition techniques to improve scalability of the analysis [10, 12, 19]. The abstractions and decomposition in the these techniques, e.g., summaries of unchanged code [12] or semantically equivalent methods [19], compute an over-approximation of the program behaviors. The equivalence checking results of these techniques are sound but not complete-they may characterize programs as not functionally equivalent when, in fact, they are equivalent. In this work we describe a novel approach that leverages the impact of the differences between two programs for scaling regression verification. We partition program behaviors of each version into (a) behaviors impacted by the changes and (b) behaviors not impacted (unimpacted) by the changes. Only the impacted program behaviors are used during equivalence checking. We then prove that checking equivalence of the impacted program behaviors is equivalent to checking equivalence of all program behaviors for a given depth bound. In this work we use symbolic execution to generate the program behaviors and leverage control- and data-dependence information to facilitate the partitioning of program behaviors. The impacted program behaviors are termed as impact summaries. The dependence analyses that facilitate the generation of the impact summaries, we believe, could be used in conjunction with other abstraction and decomposition based approaches, [10, 12], as a complementary reduction technique. An
Ridge Regression Signal Processing
NASA Technical Reports Server (NTRS)
Kuhl, Mark R.
1990-01-01
The introduction of the Global Positioning System (GPS) into the National Airspace System (NAS) necessitates the development of Receiver Autonomous Integrity Monitoring (RAIM) techniques. In order to guarantee a certain level of integrity, a thorough understanding of modern estimation techniques applied to navigational problems is required. The extended Kalman filter (EKF) is derived and analyzed under poor geometry conditions. It was found that the performance of the EKF is difficult to predict, since the EKF is designed for a Gaussian environment. A novel approach is implemented which incorporates ridge regression to explain the behavior of an EKF in the presence of dynamics under poor geometry conditions. The basic principles of ridge regression theory are presented, followed by the derivation of a linearized recursive ridge estimator. Computer simulations are performed to confirm the underlying theory and to provide a comparative analysis of the EKF and the recursive ridge estimator.
Chen, C.J.; Wang, C.J. )
1990-09-01
A significant dose-response relation between ingested arsenic and several cancers has recently been reported in four townships of the endemic area of blackfoot disease, a unique peripheral artery disease related to the chronic arsenic exposure in southwestern Taiwan. This study was carried out to examine ecological correlations between arsenic level of well water and mortality from various malignant neoplasms in 314 precincts and townships of Taiwan. The arsenic content in water of 83,656 wells was determined by a standard mercuric bromide stain method from 1974 to 1976, while mortality rates of 21 malignant neoplasms among residents in study precincts and townships from 1972 to 1983 were standardized to the world population in 1976. A significant association with the arsenic level in well water was observed for cancers of the liver, nasal cavity, lung, skin, bladder and kidney in both males and females as well as for the prostate cancer in males. These associations remained significant after adjusting for indices of urbanization and industrialization through multiple regression analyses. The multivariate-adjusted regression coefficient indicating an increase in age-adjusted mortality per 100,000 person-years for every 0.1 ppm increase in arsenic level of well water was 6.8 and 2.0, 0.7 and 0.4, 5.3 and 5.3, 0.9 and 1.0, 3.9 and 4.2, as well as 1.1 and 1.7, respectively, in males and females for cancers of the liver, nasal cavity, lung, skin, bladder and kidney. The multivariate-adjusted regression coefficient for the prostate cancer was 0.5. These weighted regression coefficients were found to increase or remain unchanged in further analyses in which only 170 southwestern townships were included.
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…
Code System to Calculate Correlation & Regression Coefficients.
1999-11-23
Version 00 PCC/SRC is designed for use in conjunction with sensitivity analyses of complex computer models. PCC/SRC calculates the partial correlation coefficients (PCC) and the standardized regression coefficients (SRC) from the multivariate input to, and output from, a computer model.
Monson, Candice M; Macdonald, Alexandra; Vorstenbosch, Valerie; Shnaider, Philippe; Goldstein, Elizabeth S R; Ferrier-Auerbach, Amanda G; Mocciola, Katharine E
2012-10-01
The current study sought to determine if different spheres of social adjustment, social and leisure, family, and work and income improved immediately following a course of cognitive processing therapy (CPT) when compared with those on a waiting list in a sample of 46 U.S. veterans diagnosed with posttraumatic stress disorder (PTSD). We also sought to determine whether changes in different PTSD symptom clusters were associated with changes in these spheres of social adjustment. Overall social adjustment, extended family relationships, and housework completion significantly improved in the CPT versus waiting-list condition, η(2) = .08 to .11. Hierarchical multiple regression analyses revealed that improvements in total clinician-rated PTSD symptoms were associated with improvements in overall social and housework adjustment. When changes in reexperiencing, avoidance, emotional numbing, and hyperarousal were all in the model accounting for changes in total social adjustment, improvements in emotional numbing symptoms were associated with improvements in overall social, extended family, and housework adjustment (β = .38 to .55). In addition, improvements in avoidance symptoms were associated with improvements in housework adjustment (β = .30), but associated with declines in extended family adjustment (β = -.34). Results suggest that it is important to consider the extent to which PTSD treatments effectively reduce specific types of symptoms, particularly emotional numbing and avoidance, to generally improve social adjustment.
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…
Leffondré, Karen; Jager, Kitty J; Boucquemont, Julie; Stel, Vianda S; Heinze, Georg
2014-10-01
Regression models are being used to quantify the effect of an exposure on an outcome, while adjusting for potential confounders. While the type of regression model to be used is determined by the nature of the outcome variable, e.g. linear regression has to be applied for continuous outcome variables, all regression models can handle any kind of exposure variables. However, some fundamentals of representation of the exposure in a regression model and also some potential pitfalls have to be kept in mind in order to obtain meaningful interpretation of results. The objective of this educational paper was to illustrate these fundamentals and pitfalls, using various multiple regression models applied to data from a hypothetical cohort of 3000 patients with chronic kidney disease. In particular, we illustrate how to represent different types of exposure variables (binary, categorical with two or more categories and continuous), and how to interpret the regression coefficients in linear, logistic and Cox models. We also discuss the linearity assumption in these models, and show how wrongly assuming linearity may produce biased results and how flexible modelling using spline functions may provide better estimates.
Chen, X; Liu, M; Li, D
2000-09-01
A sample of children, initially 12 years old, in the People's Republic of China participated in this 2-year longitudinal study. Data on parental warmth, control, and indulgence were collected from children's self-reports. Information concerning social, academic, and psychological adjustment was obtained from multiple sources. The results indicated that parenting styles might be a function of child gender and change with age. Regression analyses revealed that parenting styles of fathers and mothers predicted different outcomes. Whereas maternal warmth had significant contributions to the prediction of emotional adjustment, paternal warmth significantly predicted later social and school achievement. It was also found that paternal, but not maternal, indulgence significantly predicted children's adjustment difficulties. The contributions of the parenting variables might be moderated by the child's initial conditions. PMID:11025932
Chen, X; Liu, M; Li, D
2000-09-01
A sample of children, initially 12 years old, in the People's Republic of China participated in this 2-year longitudinal study. Data on parental warmth, control, and indulgence were collected from children's self-reports. Information concerning social, academic, and psychological adjustment was obtained from multiple sources. The results indicated that parenting styles might be a function of child gender and change with age. Regression analyses revealed that parenting styles of fathers and mothers predicted different outcomes. Whereas maternal warmth had significant contributions to the prediction of emotional adjustment, paternal warmth significantly predicted later social and school achievement. It was also found that paternal, but not maternal, indulgence significantly predicted children's adjustment difficulties. The contributions of the parenting variables might be moderated by the child's initial conditions.
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.
CSWS-related autistic regression versus autistic regression without CSWS.
Tuchman, Roberto
2009-08-01
Continuous spike-waves during slow-wave sleep (CSWS) and Landau-Kleffner syndrome (LKS) are two clinical epileptic syndromes that are associated with the electroencephalography (EEG) pattern of electrical status epilepticus during slow wave sleep (ESES). Autistic regression occurs in approximately 30% of children with autism and is associated with an epileptiform EEG in approximately 20%. The behavioral phenotypes of CSWS, LKS, and autistic regression overlap. However, the differences in age of regression, degree and type of regression, and frequency of epilepsy and EEG abnormalities suggest that these are distinct phenotypes. CSWS with autistic regression is rare, as is autistic regression associated with ESES. The pathophysiology and as such the treatment implications for children with CSWS and autistic regression are distinct from those with autistic regression without CSWS.
Computing measures of explained variation for logistic regression models.
Mittlböck, M; Schemper, M
1999-01-01
The proportion of explained variation (R2) is frequently used in the general linear model but in logistic regression no standard definition of R2 exists. We present a SAS macro which calculates two R2-measures based on Pearson and on deviance residuals for logistic regression. Also, adjusted versions for both measures are given, which should prevent the inflation of R2 in small samples. PMID:10195643
Gradus, Jaimie L; Qin, Ping; Lincoln, Alisa K; Miller, Matthew; Lawler, Elizabeth; Lash, Timothy L
2010-01-01
Adjustment disorder is a diagnosis given following a significant psychosocial stressor from which an individual has difficulty recovering. The individual's reaction to this event must exceed what would be observed among similar people experiencing the same stressor. Adjustment disorder is associated with suicidal ideation and suicide attempt. However the association between adjustment disorder and completed suicide has yet to be examined. The current study is a population-based case control study examining this association in the population of Denmark aged 15 to 90 years. All suicides in Denmark from 1994 to 2006 were included, resulting in 9,612 cases. For each case, up to 30 controls were matched on gender, exact date of birth, and calendar time, yielding 199,306 controls. Adjustment disorder diagnosis was found in 7.6% of suicide cases and 0.52% of controls. Conditional logistic regression analyses revealed that those diagnosed with adjustment disorder had 12 times the rate of suicide as those without an adjustment disorder diagnosis, after controlling for history of depression diagnosis, marital status, income, and the matched factors. PMID:20865099
Weine, Stevan Merrill; Ware, Norma; Tugenberg, Toni; Hakizimana, Leonce; Dahnweih, Gonwo; Currie, Madeleine; Wagner, Maureen; Levin, Elise
2013-01-01
Objectives The purpose of this mixed method study was to characterize the patterns of psychosocial adjustment among adolescent African refugees in U.S. resettlement. Methods A purposive sample of 73 recently resettled refugee adolescents from Burundi and Liberia were followed for two years and qualitative and quantitative data was analyzed using a mixed methods exploratory design. Results Protective resources identified were the family and community capacities that can promote youth psychosocial adjustment through: 1) Finances for necessities; 2) English proficiency; 3) Social support networks; 4) Engaged parenting; 5) Family cohesion; 6) Cultural adherence and guidance; 7) Educational support; and, 8) Faith and religious involvement. The researchers first inductively identified 19 thriving, 29 managing, and 25 struggling youths based on review of cases. Univariate analyses then indicated significant associations with country of origin, parental education, and parental employment. Multiple regressions indicated that better psychosocial adjustment was associated with Liberians and living with both parents. Logistic regressions showed that thriving was associated with Liberians and higher parental education, managing with more parental education, and struggling with Burundians and living parents. Qualitative analysis identified how these factors were proxy indicators for protective resources in families and communities. Conclusion These three trajectories of psychosocial adjustment and six domains of protective resources could assist in developing targeted prevention programs and policies for refugee youth. Further rigorous longitudinal mixed-methods study of adolescent refugees in U.S. resettlement are needed. PMID:24205467
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
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.
Wild bootstrap for quantile regression.
Feng, Xingdong; He, Xuming; Hu, Jianhua
2011-12-01
The existing theory of the wild bootstrap has focused on linear estimators. In this note, we broaden its validity by providing a class of weight distributions that is asymptotically valid for quantile regression estimators. As most weight distributions in the literature lead to biased variance estimates for nonlinear estimators of linear regression, we propose a modification of the wild bootstrap that admits a broader class of weight distributions for quantile regression. A simulation study on median regression is carried out to compare various bootstrap methods. With a simple finite-sample correction, the wild bootstrap is shown to account for general forms of heteroscedasticity in a regression model with fixed design points.
ADJUSTABLE DOUBLE PULSE GENERATOR
Gratian, J.W.; Gratian, A.C.
1961-08-01
>A modulator pulse source having adjustable pulse width and adjustable pulse spacing is described. The generator consists of a cross coupled multivibrator having adjustable time constant circuitry in each leg, an adjustable differentiating circuit in the output of each leg, a mixing and rectifying circuit for combining the differentiated pulses and generating in its output a resultant sequence of negative pulses, and a final amplifying circuit for inverting and square-topping the pulses. (AEC)
Augmenting Data with Published Results in Bayesian Linear Regression
ERIC Educational Resources Information Center
de Leeuw, Christiaan; Klugkist, Irene
2012-01-01
In most research, linear regression analyses are performed without taking into account published results (i.e., reported summary statistics) of similar previous studies. Although the prior density in Bayesian linear regression could accommodate such prior knowledge, formal models for doing so are absent from the literature. The goal of this…
Streamflow forecasting using functional regression
NASA Astrophysics Data System (ADS)
Masselot, Pierre; Dabo-Niang, Sophie; Chebana, Fateh; Ouarda, Taha B. M. J.
2016-07-01
Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More precisely, functional linear models are regression models based on curves instead of single values. They allow to consider the whole process instead of a limited number of time points or features. We apply these models to analyse the flow volume and the whole streamflow curve during a given period by using precipitations curves. The functional model is shown to lead to encouraging results. The potential of functional linear models to detect special features that would have been hard to see otherwise is pointed out. The functional model is also compared to the artificial neural network approach and the advantages and disadvantages of both models are discussed. Finally, future research directions involving the functional model in hydrology are presented.
Teaching and hospital production: the use of regression estimates.
Lehner, L A; Burgess, J F
1995-01-01
Medicare's Prospective Payment System pays U.S. teaching hospitals for the indirect costs of medical education based on a regression coefficient in a cost function. In regression studies using health care data, it is common for explanatory variables to be measured imperfectly, yet the potential for measurement error is often ignored. In this paper, U.S. Department of Veterans Affairs data is used to examine issues of health care production estimation and the use of regression estimates like the teaching adjustment factor. The findings show that measurement error and persistent multicollinearity confound attempts to have a large degree of confidence in the precise magnitude of parameter estimates.
Morris, Amanda Sheffield; John, Aesha; Halliburton, Amy L.; Morris, Michael D. S.; Robinson, Lara R.; Myers, Sonya S.; Aucoin, Katherine J.; Keyes, Angela W.; Terranova, Andrew
2013-01-01
This study examined the role of effortful control, behavior problems, and peer relations in the academic adjustment of 74 kindergarten children from primarily low-income families using a short-term longitudinal design. Teachers completed standardized measures of children’s effortful control, internalizing and externalizing problems, school readiness, and academic skills. Children participated in a sociometric interview to assess peer relations. Research Findings: Correlational analyses indicate that children’s effortful control, behavior problems in school, and peer relations are associated with academic adjustment variables at the end of the school year, including school readiness, reading skills, and math skills. Results of regression analyses indicate that household income and children’s effortful control primarily account for variation in children’s academic adjustment. The associations between children’s effortful control and academic adjustment did not vary across sex of the child or ethnicity. Mediational analyses indicate an indirect effect of effortful control on school readiness, through children’s internalizing problems. Practice or Policy: Effortful control emerged as a strong predictor of academic adjustment among kindergarten children from low-income families. Strategies for enhancing effortful control and school readiness among low-income children are discussed. PMID:24163572
Morris, Amanda Sheffield; John, Aesha; Halliburton, Amy L; Morris, Michael D S; Robinson, Lara R; Myers, Sonya S; Aucoin, Katherine J; Keyes, Angela W; Terranova, Andrew
2013-01-01
This study examined the role of effortful control, behavior problems, and peer relations in the academic adjustment of 74 kindergarten children from primarily low-income families using a short-term longitudinal design. Teachers completed standardized measures of children's effortful control, internalizing and externalizing problems, school readiness, and academic skills. Children participated in a sociometric interview to assess peer relations. Research Findings: Correlational analyses indicate that children's effortful control, behavior problems in school, and peer relations are associated with academic adjustment variables at the end of the school year, including school readiness, reading skills, and math skills. Results of regression analyses indicate that household income and children's effortful control primarily account for variation in children's academic adjustment. The associations between children's effortful control and academic adjustment did not vary across sex of the child or ethnicity. Mediational analyses indicate an indirect effect of effortful control on school readiness, through children's internalizing problems. Practice or Policy: Effortful control emerged as a strong predictor of academic adjustment among kindergarten children from low-income families. Strategies for enhancing effortful control and school readiness among low-income children are discussed. PMID:24163572
Evaluating differential effects using regression interactions and regression mixture models
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
Linear regression in astronomy. II
NASA Technical Reports Server (NTRS)
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Quantile regression for climate data
NASA Astrophysics Data System (ADS)
Marasinghe, Dilhani Shalika
Quantile regression is a developing statistical tool which is used to explain the relationship between response and predictor variables. This thesis describes two examples of climatology using quantile regression.Our main goal is to estimate derivatives of a conditional mean and/or conditional quantile function. We introduce a method to handle autocorrelation in the framework of quantile regression and used it with the temperature data. Also we explain some properties of the tornado data which is non-normally distributed. Even though quantile regression provides a more comprehensive view, when talking about residuals with the normality and the constant variance assumption, we would prefer least square regression for our temperature analysis. When dealing with the non-normality and non constant variance assumption, quantile regression is a better candidate for the estimation of the derivative.
Risk-adjusted monitoring of survival times
Sego, Landon H.; Reynolds, Marion R.; Woodall, William H.
2009-02-26
We consider the monitoring of clinical outcomes, where each patient has a di®erent risk of death prior to undergoing a health care procedure.We propose a risk-adjusted survival time CUSUM chart (RAST CUSUM) for monitoring clinical outcomes where the primary endpoint is a continuous, time-to-event variable that may be right censored. Risk adjustment is accomplished using accelerated failure time regression models. We compare the average run length performance of the RAST CUSUM chart to the risk-adjusted Bernoulli CUSUM chart, using data from cardiac surgeries to motivate the details of the comparison. The comparisons show that the RAST CUSUM chart is more efficient at detecting a sudden decrease in the odds of death than the risk-adjusted Bernoulli CUSUM chart, especially when the fraction of censored observations is not too high. We also discuss the implementation of a prospective monitoring scheme using the RAST CUSUM chart.
Ingoldsby, Erin M.; Kohl, Gwynne O.; McMahon, Robert J.; Lengua, Liliana
2009-01-01
The present study investigated patterns in the development of conduct problems (CP), depressive symptoms, and their co-occurrence, and relations to adjustment problems, over the transition from late childhood to early adolescence. Rates of depressive symptoms and CP during this developmental period vary by gender, yet, few studies involving non-clinical samples have examined co-occurring problems and adjustment outcomes across boys and girls. This study investigates the manifestation and change in CP and depressive symptom patterns in a large, multisite, gender- and ethnically-diverse sample of 431 youth from 5th to 7th grade. Indicators of CP, depressive symptoms, their co-occurrence, and adjustment outcomes were created from multiple reporters and measures. Hypotheses regarding gender differences were tested utilizing both categorical (i.e., elevated symptom groups) and continuous analyses (i.e., regressions predicting symptomatology and adjustment outcomes). Results were partially supportive of the dual failure model (Capaldi, 1991, 1992), with youth with co-occurring problems in 5th grade demonstrating significantly lower academic adjustment and social competence two years later. Both depressive symptoms and CP were risk factors for multiple negative adjustment outcomes. Co-occurring symptomatology and CP demonstrated more stability and was associated with more severe adjustment problems than depressive symptoms over time. Categorical analyses suggested that, in terms of adjustment problems, youth with co-occurring symptomatology were generally no worse off than those with CP-alone, and those with depressive symptoms-alone were similar over time to those showing no symptomatology at all. Few gender differences were noted in the relations among CP, depressive symptoms, and adjustment over time. PMID:16967336
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…
Retro-regression--another important multivariate regression improvement.
Randić, M
2001-01-01
We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA. PMID:11410035
Transfer Learning Based on Logistic Regression
NASA Astrophysics Data System (ADS)
Paul, A.; Rottensteiner, F.; Heipke, C.
2015-08-01
In this paper we address the problem of classification of remote sensing images in the framework of transfer learning with a focus on domain adaptation. The main novel contribution is a method for transductive transfer learning in remote sensing on the basis of logistic regression. Logistic regression is a discriminative probabilistic classifier of low computational complexity, which can deal with multiclass problems. This research area deals with methods that solve problems in which labelled training data sets are assumed to be available only for a source domain, while classification is needed in the target domain with different, yet related characteristics. Classification takes place with a model of weight coefficients for hyperplanes which separate features in the transformed feature space. In term of logistic regression, our domain adaptation method adjusts the model parameters by iterative labelling of the target test data set. These labelled data features are iteratively added to the current training set which, at the beginning, only contains source features and, simultaneously, a number of source features are deleted from the current training set. Experimental results based on a test series with synthetic and real data constitutes a first proof-of-concept of the proposed method.
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.
Spatial correlation in Bayesian logistic regression with misclassification.
Bihrmann, Kristine; Toft, Nils; Nielsen, Søren Saxmose; Ersbøll, Annette Kjær
2014-06-01
Standard logistic regression assumes that the outcome is measured perfectly. In practice, this is often not the case, which could lead to biased estimates if not accounted for. This study presents Bayesian logistic regression with adjustment for misclassification of the outcome applied to data with spatial correlation. The models assessed include a fixed effects model, an independent random effects model, and models with spatially correlated random effects modelled using conditional autoregressive prior distributions (ICAR and ICAR(ρ)). Performance of these models was evaluated in a simulation study. Parameters were estimated by Markov Chain Monte Carlo methods, using slice sampling to improve convergence. The results demonstrated that adjustment for misclassification must be included to produce unbiased regression estimates. With strong correlation the ICAR model performed best. With weak or moderate correlation the ICAR(ρ) performed best. With unknown spatial correlation the recommended model would be the ICAR(ρ), assuming convergence can be obtained. PMID:24889989
Missing Work After Retirement: The Role of Life Histories in the Retirement Adjustment Process
Damman, Marleen; Henkens, Kène; Kalmijn, Matthijs
2015-01-01
Purpose of the Study: Although the process of adjustment to retirement is often assumed to be related to experiences earlier in life, quantitative empirical insights regarding these relationships are limited. This study aims to improve our understanding of adjustment to the loss of the work role, by conceptualizing retirement as a multidimensional process embedded in the individual life course. Design and Methods: Analyses are based on panel data collected in 2001, 2006–2007, and 2011 among Dutch retirees (N = 1,004). The extent to which retirees miss aspects of the work role (money/income, social contacts, status) is regressed on information about earlier life experiences, resources, and retirement transition characteristics. Results: The incidence of adjustment difficulties varies across dimensions. Predictors differ as well. A steep upward career path is associated with fewer financial adjustment difficulties but with more difficulties adjusting to the loss of status. Compared with continuously married retirees, divorced retirees without a partner are more likely to miss the social dimensions of work and those who repartnered are more likely to miss financial resources. The longer individuals are retired, the less likely they are to miss work-related social contacts. Implications: Changing life course experiences might have important consequences for retirement processes of future retirees. PMID:24381175
ERIC Educational Resources Information Center
Shih, Ching-Lin; Liu, Tien-Hsiang; Wang, Wen-Chung
2014-01-01
The simultaneous item bias test (SIBTEST) method regression procedure and the differential item functioning (DIF)-free-then-DIF strategy are applied to the logistic regression (LR) method simultaneously in this study. These procedures are used to adjust the effects of matching true score on observed score and to better control the Type I error…
Regression models of sprint, vertical jump, and change of direction performance.
Swinton, Paul A; Lloyd, Ray; Keogh, Justin W L; Agouris, Ioannis; Stewart, Arthur D
2014-07-01
It was the aim of the present study to expand on previous correlation analyses that have attempted to identify factors that influence performance of jumping, sprinting, and changing direction. This was achieved by using a regression approach to obtain linear models that combined anthropometric, strength, and other biomechanical variables. Thirty rugby union players participated in the study (age: 24.2 ± 3.9 years; stature: 181.2 ± 6.6 cm; mass: 94.2 ± 11.1 kg). The athletes' ability to sprint, jump, and change direction was assessed using a 30-m sprint, vertical jump, and 505 agility test, respectively. Regression variables were collected during maximum strength tests (1 repetition maximum [1RM] deadlift and squat) and performance of fast velocity resistance exercises (deadlift and jump squat) using submaximum loads (10-70% 1RM). Force, velocity, power, and rate of force development (RFD) values were measured during fast velocity exercises with the greatest values produced across loads selected for further analysis. Anthropometric data, including lengths, widths, and girths were collected using a 3-dimensional body scanner. Potential regression variables were first identified using correlation analyses. Suitable variables were then regressed using a best subsets approach. Three factor models generally provided the most appropriate balance between explained variance and model complexity. Adjusted R values of 0.86, 0.82, and 0.67 were obtained for sprint, jump, and change of direction performance, respectively. Anthropometric measurements did not feature in any of the top models because of their strong association with body mass. For each performance measure, variance was best explained by relative maximum strength. Improvements in models were then obtained by including velocity and power values for jumping and sprinting performance, and by including RFD values for change of direction performance. PMID:24345969
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…
Can luteal regression be reversed?
Telleria, Carlos M
2006-01-01
The corpus luteum is an endocrine gland whose limited lifespan is hormonally programmed. This debate article summarizes findings of our research group that challenge the principle that the end of function of the corpus luteum or luteal regression, once triggered, cannot be reversed. Overturning luteal regression by pharmacological manipulations may be of critical significance in designing strategies to improve fertility efficacy. PMID:17074090
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…
Wild bootstrap for quantile regression.
Feng, Xingdong; He, Xuming; Hu, Jianhua
2011-12-01
The existing theory of the wild bootstrap has focused on linear estimators. In this note, we broaden its validity by providing a class of weight distributions that is asymptotically valid for quantile regression estimators. As most weight distributions in the literature lead to biased variance estimates for nonlinear estimators of linear regression, we propose a modification of the wild bootstrap that admits a broader class of weight distributions for quantile regression. A simulation study on median regression is carried out to compare various bootstrap methods. With a simple finite-sample correction, the wild bootstrap is shown to account for general forms of heteroscedasticity in a regression model with fixed design points. PMID:23049133
[Regression grading in gastrointestinal tumors].
Tischoff, I; Tannapfel, A
2012-02-01
Preoperative neoadjuvant chemoradiation therapy is a well-established and essential part of the interdisciplinary treatment of gastrointestinal tumors. Neoadjuvant treatment leads to regressive changes in tumors. To evaluate the histological tumor response different scoring systems describing regressive changes are used and known as tumor regression grading. Tumor regression grading is usually based on the presence of residual vital tumor cells in proportion to the total tumor size. Currently, no nationally or internationally accepted grading systems exist. In general, common guidelines should be used in the pathohistological diagnostics of tumors after neoadjuvant therapy. In particularly, the standard tumor grading will be replaced by tumor regression grading. Furthermore, tumors after neoadjuvant treatment are marked with the prefix "y" in the TNM classification. PMID:22293790
Fungible weights in logistic regression.
Jones, Jeff A; Waller, Niels G
2016-06-01
In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record
You, Hua; Gu, Hai; Ning, Weiqing; Zhou, Hua; Dong, Hengjin
2016-01-01
Background The New Rural Cooperative Medical Scheme (NCMS) includes a maternal care benefits package that is associated with increasing maternal health services. The local compensation policies have been frequently adjusted in recent years. This study examined the association between the NCMS maternal-services policy adjustment and expense reimbursement in Yuyao, China. Methods Two household surveys were conducted in Yuyao in 2008 and 2011 (before and after the NCMS policy adjustment, respectively). Local women (N = 154) who had delivery history in the past three years were recruited. A questionnaire was used to collect information about delivery history, maternal health services utilization (prenatal care, postnatal care, and the grade of delivery institutions), NCMS participation, and reimbursement status. Logistic regression analyses were used to predict the association between policy adjustment and maternal health utilization and the association between policy adjustment and out-of-pocket proportion. Next, t-tests and covariance analyses adjusting for household income were used to compare the out-of-pocket proportion between 2008 and 2011. Results Results revealed that compensation policy adjustment was associated with an increase in postnatal visits (adjusted OR = 3.32, p = 0.009) and the use of second level or above institutions for delivery (adjusted OR = 2.32, p = 0.03) among participants. In 2008, only 9.1% of pregnant women received reimbursement from the NCMS; however, this rate increased to 36.8% in 2011. After policy adjustment, there were no significant changes in the proportion of out-of-pocket expenses shared in delivery fee (F = 0.24, p = 0.63) and in household income (F = 0.46, p = 0.50). Conclusions Financial compensation increase improved maternal health services utilization; however, this effect was limited. Although the reimbursement rate was raised, the out-of-pocket proportion was not significant changed; therefore, the compensation design
Scher, Christine D; Ellwanger, Joel
2009-10-01
This study builds upon current understanding of risk and protective factors for post-disaster adjustment by examining relationships between disaster-related cognitions, three empirically supported risk factors for poorer adjustment (i.e., greater disaster impact, female gender, and racial/ethnic minority status), and three common post-disaster outcomes (i.e., depression, anxiety, and somatic complaints). Participants were 200 students exposed to wildfire disaster. Simultaneous hierarchical regression analyses revealed that, during the acute stress period: (1) disaster-related cognitions in interaction with fire impact and minority status, as well as gender, were related to anxiety symptoms, (2) cognitions were related to depression symptoms, and (3) cognitions in interaction with minority status, as well as fire impact, were related to somatic symptoms. No examined variables predicted symptom change.
Silverthorn, N A; Gekoski, W L
1995-03-01
Results of regression analyses on data from 96 first-year undergraduates indicated that social desirability (Jackson and Marlowe-Crowne Social Desirability Scales), particularly scores on the Jackson scale, is related strongly to scores on measures of adjustment (Student Adaptation to College Questionnaire), self-efficacy (Hale-Fibel Generalized Expectation for Success Scale), and independence (Psychological Separation Inventory) from mother, but not from father. In addition, both the Jackson and Marlowe-Crowne scales were correlated highly. Independence from parents and self-efficacy each continued to show a relationship with adjustment to university after social desirability effects were removed. Failure to remove the effect(s) of social desirability from the present measures is likely to lead to inflated estimates of their relation to each other or to other measures.
Length bias correction in gene ontology enrichment analysis using logistic regression.
Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H
2012-01-01
When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible. PMID:23056249
ERIC Educational Resources Information Center
Van Galen, Jane, Ed.; And Others
1992-01-01
This theme issue of the serial "Educational Foundations" contains four articles devoted to the topic of "Sociopolitical Analyses." In "An Interview with Peter L. McLaren," Mary Leach presented the views of Peter L. McLaren on topics of local and national discourses, values, and the politics of difference. Landon E. Beyer's "Educational Studies and…
Stürmer, Til; Schneeweiss, Sebastian; Avorn, Jerry; Glynn, Robert J
2006-01-01
Often important confounders are not available in studies. Sensitivity analyses based on the relation of single, but not multiple, unmeasured confounders with an exposure of interest in a separate validation study have been proposed. The authors controlled for measured confounding in the main cohort using propensity scores (PS) and addressed unmeasured confounding by estimating two additional PS in a validation study. The ‘error-prone’ PS exclusively used information available in the main cohort. The ‘gold-standard’ PS additionally included covariates available only in the validation study. Based on these two PS in the validation study, regression calibration was applied to adjust regression coefficients. This propensity score calibration (PSC) adjusts for unmeasured confounding in cohort studies with validation data under certain, usually untestable, assumptions. PSC was used to assess nonsteroidal antiinflammatory drugs (NSAID) and 1-year mortality in a large cohort of elderly. ‘Traditional’ adjustment resulted in a relative risk (RR) in NSAID users of 0.80 (95% confidence interval: 0.77–0.83) compared to an unadjusted RR of 0.68 (0.66–0.71). Application of PSC resulted in a more plausible RR of 1.06 (1.00–1.12). Until validity and limitations of PSC have been assessed in different settings, the method should be seen as a sensitivity analysis. PMID:15987725
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).
Role loss and emotional adjustment in chronic pain.
Harris, Samantha; Morley, Stephen; Barton, Stephen B
2003-09-01
Chronic pain interrupts behaviour, interferes with functioning, and may affect a person's identity: their sense of self. We tested whether loss of role and personal attributes and current and past self-concept differentiation, predicted adjustment as indexed by measures of depression. Chronic pain patients (n=80) completed measures of pain (MPQ), disability (PDI), depression and anxiety (BDI, HADS). Measures of role and attribute loss and self-concept differentiation were derived from a Role-Attribute Test in which participants identified four social roles in four domains (friendship, occupation, leisure, family) and nominated two personal attributes in each role prior to pain onset and current. Participants reported mean losses of 3.38 roles, and 6.97 attributes. Greater losses were observed in friendship, occupation and leisure domains compared with the family domain. Multiple regression analyses revealed that after controlling for demographic and clinical differences, role and attribute loss predicted depression scores. There was no evidence that depression was associated with past self-concept differentiation. The results are discussed with reference to the methodology used and the relevance of self-identity to understand adjustment to chronic pain. PMID:14499455
Regional regression of flood characteristics employing historical information
Tasker, Gary D.; Stedinger, J.R.
1987-01-01
Streamflow gauging networks provide hydrologic information for use in estimating the parameters of regional regression models. The regional regression models can be used to estimate flood statistics, such as the 100 yr peak, at ungauged sites as functions of drainage basin characteristics. A recent innovation in regional regression is the use of a generalized least squares (GLS) estimator that accounts for unequal station record lengths and sample cross correlation among the flows. However, this technique does not account for historical flood information. A method is proposed here to adjust this generalized least squares estimator to account for possible information about historical floods available at some stations in a region. The historical information is assumed to be in the form of observations of all peaks above a threshold during a long period outside the systematic record period. A Monte Carlo simulation experiment was performed to compare the GLS estimator adjusted for historical floods with the unadjusted GLS estimator and the ordinary least squares estimator. Results indicate that using the GLS estimator adjusted for historical information significantly improves the regression model. ?? 1987.
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.
Regression methods for spatial data
NASA Technical Reports Server (NTRS)
Yakowitz, S. J.; Szidarovszky, F.
1982-01-01
The kriging approach, a parametric regression method used by hydrologists and mining engineers, among others also provides an error estimate the integral of the regression function. The kriging method is explored and some of its statistical characteristics are described. The Watson method and theory are extended so that the kriging features are displayed. Theoretical and computational comparisons of the kriging and Watson approaches are offered.
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.
Basis Selection for Wavelet Regression
NASA Technical Reports Server (NTRS)
Wheeler, Kevin R.; Lau, Sonie (Technical Monitor)
1998-01-01
A wavelet basis selection procedure is presented for wavelet regression. Both the basis and the threshold are selected using cross-validation. The method includes the capability of incorporating prior knowledge on the smoothness (or shape of the basis functions) into the basis selection procedure. The results of the method are demonstrated on sampled functions widely used in the wavelet regression literature. The results of the method are contrasted with other published methods.
Regression Discontinuity Designs in Epidemiology
Moscoe, Ellen; Mutevedzi, Portia; Newell, Marie-Louise; Bärnighausen, Till
2014-01-01
When patients receive an intervention based on whether they score below or above some threshold value on a continuously measured random variable, the intervention will be randomly assigned for patients close to the threshold. The regression discontinuity design exploits this fact to estimate causal treatment effects. In spite of its recent proliferation in economics, the regression discontinuity design has not been widely adopted in epidemiology. We describe regression discontinuity, its implementation, and the assumptions required for causal inference. We show that regression discontinuity is generalizable to the survival and nonlinear models that are mainstays of epidemiologic analysis. We then present an application of regression discontinuity to the much-debated epidemiologic question of when to start HIV patients on antiretroviral therapy. Using data from a large South African cohort (2007–2011), we estimate the causal effect of early versus deferred treatment eligibility on mortality. Patients whose first CD4 count was just below the 200 cells/μL CD4 count threshold had a 35% lower hazard of death (hazard ratio = 0.65 [95% confidence interval = 0.45–0.94]) than patients presenting with CD4 counts just above the threshold. We close by discussing the strengths and limitations of regression discontinuity designs for epidemiology. PMID:25061922
McKenzie, K.R.
1959-07-01
An electrode support which permits accurate alignment and adjustment of the electrode in a plurality of planes and about a plurality of axes in a calutron is described. The support will align the slits in the electrode with the slits of an ionizing chamber so as to provide for the egress of ions. The support comprises an insulator, a leveling plate carried by the insulator and having diametrically opposed attaching screws screwed to the plate and the insulator and diametrically opposed adjusting screws for bearing against the insulator, and an electrode associated with the plate for adjustment therewith.
Attachment style and adjustment to divorce.
Yárnoz-Yaben, Sagrario
2010-05-01
Divorce is becoming increasingly widespread in Europe. In this study, I present an analysis of the role played by attachment style (secure, dismissing, preoccupied and fearful, plus the dimensions of anxiety and avoidance) in the adaptation to divorce. Participants comprised divorced parents (N = 40) from a medium-sized city in the Basque Country. The results reveal a lower proportion of people with secure attachment in the sample group of divorcees. Attachment style and dependence (emotional and instrumental) are closely related. I have also found associations between measures that showed a poor adjustment to divorce and the preoccupied and fearful attachment styles. Adjustment is related to a dismissing attachment style and to the avoidance dimension. Multiple regression analysis confirmed that secure attachment and the avoidance dimension predict adjustment to divorce and positive affectivity while preoccupied attachment and the anxiety dimension predicted negative affectivity. Implications for research and interventions with divorcees are discussed.
Background stratified Poisson regression analysis of cohort data
Langholz, Bryan
2012-01-01
Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as ‘nuisance’ variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this ‘conditional’ regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. PMID:22193911
Background stratified Poisson regression analysis of cohort data.
Richardson, David B; Langholz, Bryan
2012-03-01
Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models. PMID:22193911
Incremental learning for ν-Support Vector Regression.
Gu, Bin; Sheng, Victor S; Wang, Zhijie; Ho, Derek; Osman, Said; Li, Shuo
2015-07-01
The ν-Support Vector Regression (ν-SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ν on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to ν-Support Vector Classification (ν-SVC) (Schölkopf et al., 2000), ν-SVR introduces an additional linear term into its objective function. Thus, directly applying the accurate on-line ν-SVC algorithm (AONSVM) to ν-SVR will not generate an effective initial solution. It is the main challenge to design an incremental ν-SVR learning algorithm. To overcome this challenge, we propose a special procedure called initial adjustments in this paper. This procedure adjusts the weights of ν-SVC based on the Karush-Kuhn-Tucker (KKT) conditions to prepare an initial solution for the incremental learning. Combining the initial adjustments with the two steps of AONSVM produces an exact and effective incremental ν-SVR learning algorithm (INSVR). Theoretical analysis has proven the existence of the three key inverse matrices, which are the cornerstones of the three steps of INSVR (including the initial adjustments), respectively. The experiments on benchmark datasets demonstrate that INSVR can avoid the infeasible updating paths as far as possible, and successfully converges to the optimal solution. The results also show that INSVR is faster than batch ν-SVR algorithms with both cold and warm starts.
Remotely Adjustable Hydraulic Pump
NASA Technical Reports Server (NTRS)
Kouns, H. H.; Gardner, L. D.
1987-01-01
Outlet pressure adjusted to match varying loads. Electrohydraulic servo has positioned sleeve in leftmost position, adjusting outlet pressure to maximum value. Sleeve in equilibrium position, with control land covering control port. For lowest pressure setting, sleeve shifted toward right by increased pressure on sleeve shoulder from servovalve. Pump used in aircraft and robots, where hydraulic actuators repeatedly turned on and off, changing pump load frequently and over wide range.
Weighted triangulation adjustment
Anderson, Walter L.
1969-01-01
The variation of coordinates method is employed to perform a weighted least squares adjustment of horizontal survey networks. Geodetic coordinates are required for each fixed and adjustable station. A preliminary inverse geodetic position computation is made for each observed line. Weights associated with each observed equation for direction, azimuth, and distance are applied in the formation of the normal equations in-the least squares adjustment. The number of normal equations that may be solved is twice the number of new stations and less than 150. When the normal equations are solved, shifts are produced at adjustable stations. Previously computed correction factors are applied to the shifts and a most probable geodetic position is found for each adjustable station. Pinal azimuths and distances are computed. These may be written onto magnetic tape for subsequent computation of state plane or grid coordinates. Input consists of punch cards containing project identification, program options, and position and observation information. Results listed include preliminary and final positions, residuals, observation equations, solution of the normal equations showing magnitudes of shifts, and a plot of each adjusted and fixed station. During processing, data sets containing irrecoverable errors are rejected and the type of error is listed. The computer resumes processing of additional data sets.. Other conditions cause warning-errors to be issued, and processing continues with the current data set.
Stress, social support, and college adjustment among Latino students.
Jarama Alvan, S L; Belgrave, F Z; Zea, M C
1996-01-01
This study examined the role of social support and stress on adjustment to college among Latino students. Measures of social support, stress, and adjustment to college were obtained from 77 Latino college students. Social support was positively associated with adjustment and negatively associated with stress. A negative relationship was found between stress and adjustment in bivariate analyses indicating that exposure to stress interferes with adequate adjustment. However, stress did not significantly contribute to adjustment when included with social support in multivariate analyses. The functional nature of support was also examined in this study. Emotional support was associated with better overall and academic adjustment and less stress than instrumental support. there was a significant negative relationship between support from friend/other and stress. Finally, support from friend/other was negatively correlated with stress from exposure to racism. Implications of the study in terms of future research and college programs for Latino students are discussed.
Interpretation of Standardized Regression Coefficients in Multiple Regression.
ERIC Educational Resources Information Center
Thayer, Jerome D.
The extent to which standardized regression coefficients (beta values) can be used to determine the importance of a variable in an equation was explored. The beta value and the part correlation coefficient--also called the semi-partial correlation coefficient and reported in squared form as the incremental "r squared"--were compared for variables…
Regressive Evolution in Astyanax Cavefish
Jeffery, William R.
2013-01-01
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. PMID:19640230
Laplace regression with censored data.
Bottai, Matteo; Zhang, Jiajia
2010-08-01
We consider a regression model where the error term is assumed to follow a type of asymmetric Laplace distribution. We explore its use in the estimation of conditional quantiles of a continuous outcome variable given a set of covariates in the presence of random censoring. Censoring may depend on covariates. Estimation of the regression coefficients is carried out by maximizing a non-differentiable likelihood function. In the scenarios considered in a simulation study, the Laplace estimator showed correct coverage and shorter computation time than the alternative methods considered, some of which occasionally failed to converge. We illustrate the use of Laplace regression with an application to survival time in patients with small cell lung cancer.
Interquantile Shrinkage in Regression Models
Jiang, Liewen; Wang, Huixia Judy; Bondell, Howard D.
2012-01-01
Conventional analysis using quantile regression typically focuses on fitting the regression model at different quantiles separately. However, in situations where the quantile coefficients share some common feature, joint modeling of multiple quantiles to accommodate the commonality often leads to more efficient estimation. One example of common features is that a predictor may have a constant effect over one region of quantile levels but varying effects in other regions. To automatically perform estimation and detection of the interquantile commonality, we develop two penalization methods. When the quantile slope coefficients indeed do not change across quantile levels, the proposed methods will shrink the slopes towards constant and thus improve the estimation efficiency. We establish the oracle properties of the two proposed penalization methods. Through numerical investigations, we demonstrate that the proposed methods lead to estimations with competitive or higher efficiency than the standard quantile regression estimation in finite samples. Supplemental materials for the article are available online. PMID:24363546
[Is regression of atherosclerosis possible?].
Thomas, D; Richard, J L; Emmerich, J; Bruckert, E; Delahaye, F
1992-10-01
Experimental studies have shown the regression of atherosclerosis in animals given a cholesterol-rich diet and then given a normal diet or hypolipidemic therapy. Despite favourable results of clinical trials of primary prevention modifying the lipid profile, the concept of atherosclerosis regression in man remains very controversial. The methodological approach is difficult: this is based on angiographic data and requires strict standardisation of angiographic views and reliable quantitative techniques of analysis which are available with image processing. Several methodologically acceptable clinical coronary studies have shown not only stabilisation but also regression of atherosclerotic lesions with reductions of about 25% in total cholesterol levels and of about 40% in LDL cholesterol levels. These reductions were obtained either by drugs as in CLAS (Cholesterol Lowering Atherosclerosis Study), FATS (Familial Atherosclerosis Treatment Study) and SCOR (Specialized Center of Research Intervention Trial), by profound modifications in dietary habits as in the Lifestyle Heart Trial, or by surgery (ileo-caecal bypass) as in POSCH (Program On the Surgical Control of the Hyperlipidemias). On the other hand, trials with non-lipid lowering drugs such as the calcium antagonists (INTACT, MHIS) have not shown significant regression of existing atherosclerotic lesions but only a decrease on the number of new lesions. The clinical benefits of these regression studies are difficult to demonstrate given the limited period of observation, relatively small population numbers and the fact that in some cases the subjects were asymptomatic. The decrease in the number of cardiovascular events therefore seems relatively modest and concerns essentially subjects who were symptomatic initially. The clinical repercussion of studies of prevention involving a single lipid factor is probably partially due to the reduction in progression and anatomical regression of the atherosclerotic plaque
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…
Baurain, Céline; Nader-Grosbois, Nathalie; Dionne, Carmen
2013-09-01
This study examined the extent to which socio-emotional regulation displayed in three dyadic interactive play contexts (neutral, competitive or cooperative) by 45 children with intellectual disability compared with 45 typically developing children (matched on developmental age, ranging from 3 to 6 years) is linked with the teachers' perceptions of their social adjustment. A Coding Grid of Socio-Emotional Regulation by Sequences (Baurain & Nader-Grosbois, 2011b, 2011c) focusing on Emotional Expression, Social Behavior and Behavior toward Social Rules in children was applied. The Social Adjustment for Children Scale (EASE, Hugues, Soares-Boucaud, Hochman, & Frith, 1997) and the Assessment, Evaluation and Intervention Program System (AEPS, Bricker, 2002) were completed by teachers. Regression analyses emphasized, in children with intellectual disability only, a positive significant link between their Behavior toward Social Rules in interactive contexts and the teachers' perceptions of their social adjustment. Children with intellectual disabilities who listen to and follow instructions, who are patient in waiting for their turn, and who moderate their externalized behavior are perceived by their teachers as socially adapted in their daily social relationships. The between-groups dissimilarity in the relational patterns between abilities in socio-emotional regulation and social adjustment supports the "structural difference hypothesis" with regard to the group with intellectual disability, compared with the typically developing group. Hierarchical cluster cases analyses identified distinct subgroups showing variable structural patterns between the three specific categories of abilities in socio-emotional regulation and their levels of social adjustment perceived by teachers. In both groups, several abilities in socio-emotional regulation and teachers' perceptions of social adjustment vary depending on children's developmental age. Chronological age in children with
Polak, Katarzyna Anna; Puttler, Leon I; Ilgen, Mark Andrew
2012-06-01
Sixty adolescents from alcoholic families living in two large cities in Poland were examined in 2008 and 2009. Richness, stability, and certainty of their self-concepts, as well as levels of school adjustment, anxiety, and depression, were evaluated using a set of questionnaires. In a series of bivariate analyses, the strongest associations found were between richness of the self-concept and the social withdrawal syndrome, and between stability of the self-concept and depression. Both relationships remained significant, using multiple regression models, after controlling for possible confounding factors. Possible explanations and implications for the findings, as well as the study's limitations, are noted and discussed.
Correlation Weights in Multiple Regression
ERIC Educational Resources Information Center
Waller, Niels G.; Jones, Jeff A.
2010-01-01
A general theory on the use of correlation weights in linear prediction has yet to be proposed. In this paper we take initial steps in developing such a theory by describing the conditions under which correlation weights perform well in population regression models. Using OLS weights as a comparison, we define cases in which the two weighting…
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…
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…
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…
Ridge Regression for Interactive Models.
ERIC Educational Resources Information Center
Tate, Richard L.
1988-01-01
An exploratory study of the value of ridge regression for interactive models is reported. Assuming that the linear terms in a simple interactive model are centered to eliminate non-essential multicollinearity, a variety of common models, representing both ordinal and disordinal interactions, are shown to have "orientations" that are favorable to…
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…
Simple, Internally Adjustable Valve
NASA Technical Reports Server (NTRS)
Burley, Richard K.
1990-01-01
Valve containing simple in-line, adjustable, flow-control orifice made from ordinary plumbing fitting and two allen setscrews. Construction of valve requires only simple drilling, tapping, and grinding. Orifice installed in existing fitting, avoiding changes in rest of plumbing.
NASA Technical Reports Server (NTRS)
1986-01-01
Corning Glass Works' Serengeti Driver sunglasses are unique in that their lenses self-adjust and filter light while suppressing glare. They eliminate more than 99% of the ultraviolet rays in sunlight. The frames are based on the NASA Anthropometric Source Book.
ERIC Educational Resources Information Center
Abramson, Jane A.
Personal interviews with 100 former farm operators living in Saskatoon, Saskatchewan, were conducted in an attempt to understand the nature of the adjustment process caused by migration from rural to urban surroundings. Requirements for inclusion in the study were that respondents had owned or operated a farm for at least 3 years, had left their…
Hunter, Steven L.
2002-01-01
An inclinometer utilizing synchronous demodulation for high resolution and electronic offset adjustment provides a wide dynamic range without any moving components. A device encompassing a tiltmeter and accompanying electronic circuitry provides quasi-leveled tilt sensors that detect highly resolved tilt change without signal saturation.
3D Regression Heat Map Analysis of Population Study Data.
Klemm, Paul; Lawonn, Kai; Glaßer, Sylvia; Niemann, Uli; Hegenscheid, Katrin; Völzke, Henry; Preim, Bernhard
2016-01-01
Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease. PMID:26529689
3D Regression Heat Map Analysis of Population Study Data.
Klemm, Paul; Lawonn, Kai; Glaßer, Sylvia; Niemann, Uli; Hegenscheid, Katrin; Völzke, Henry; Preim, Bernhard
2016-01-01
Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease.
Quantile Regression With Measurement Error
Wei, Ying; Carroll, Raymond J.
2010-01-01
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. PMID:20305802
Precision and Recall for Regression
NASA Astrophysics Data System (ADS)
Torgo, Luis; Ribeiro, Rita
Cost sensitive prediction is a key task in many real world applications. Most existing research in this area deals with classification problems. This paper addresses a related regression problem: the prediction of rare extreme values of a continuous variable. These values are often regarded as outliers and removed from posterior analysis. However, for many applications (e.g. in finance, meteorology, biology, etc.) these are the key values that we want to accurately predict. Any learning method obtains models by optimizing some preference criteria. In this paper we propose new evaluation criteria that are more adequate for these applications. We describe a generalization for regression of the concepts of precision and recall often used in classification. Using these new evaluation metrics we are able to focus the evaluation of predictive models on the cases that really matter for these applications. Our experiments indicate the advantages of the use of these new measures when comparing predictive models in the context of our target applications.
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.
Tavernier, Royette; Munroe, Melanie; Willoughby, Teena
2015-01-01
Past research has consistently found that evening-types typically report poorer academic adjustment and higher levels of substance use compared to morning-types. An important development within the morningness-eveningness and psychosocial adjustment literature has been the hypothesis that social jetlag (i.e. the asynchrony between an individual's "biological" and "social" clocks) is one factor that may explain why evening-types are at a greater risk for negative psychosocial adjustment. Yet, only a handful of studies have assessed social jetlag. Furthermore, the few studies that have assessed social jetlag have done so only with concurrent data, and thus have not been able to determine the direction of effects among morningness-eveningness, social jetlag and psychosocial adjustment. To address this important gap in the literature, the present 3-year longitudinal study employed the use of a cross-lagged auto-regressive model to specifically examine the predictive role of perceived morningness-eveningness and social jetlag on two important indices of psychosocial adjustment among university students: academic adjustment and substance use. We also assessed whether there would be an indirect effect between perceived morningness-eveningness and psychosocial adjustment through social jetlag. Participants were 942 (71.5% female; M = 19 years, SD = 0.90) undergraduates at a mid-sized university in Southern Ontario, Canada, who completed a survey at three assessments, each one year apart, beginning in first-year university. Measures were demographics (age, gender and parental education), sleep problems, perceived morningness-eveningness, social jetlag, academic adjustment and substance use. As hypothesized, results of path analyses indicated that a greater perceived eveningness preference significantly predicted higher social jetlag, poorer academic adjustment and higher substance use over time. In contrast, we found no support for social jetlag as a predictor of
Tavernier, Royette; Munroe, Melanie; Willoughby, Teena
2015-01-01
Past research has consistently found that evening-types typically report poorer academic adjustment and higher levels of substance use compared to morning-types. An important development within the morningness-eveningness and psychosocial adjustment literature has been the hypothesis that social jetlag (i.e. the asynchrony between an individual's "biological" and "social" clocks) is one factor that may explain why evening-types are at a greater risk for negative psychosocial adjustment. Yet, only a handful of studies have assessed social jetlag. Furthermore, the few studies that have assessed social jetlag have done so only with concurrent data, and thus have not been able to determine the direction of effects among morningness-eveningness, social jetlag and psychosocial adjustment. To address this important gap in the literature, the present 3-year longitudinal study employed the use of a cross-lagged auto-regressive model to specifically examine the predictive role of perceived morningness-eveningness and social jetlag on two important indices of psychosocial adjustment among university students: academic adjustment and substance use. We also assessed whether there would be an indirect effect between perceived morningness-eveningness and psychosocial adjustment through social jetlag. Participants were 942 (71.5% female; M = 19 years, SD = 0.90) undergraduates at a mid-sized university in Southern Ontario, Canada, who completed a survey at three assessments, each one year apart, beginning in first-year university. Measures were demographics (age, gender and parental education), sleep problems, perceived morningness-eveningness, social jetlag, academic adjustment and substance use. As hypothesized, results of path analyses indicated that a greater perceived eveningness preference significantly predicted higher social jetlag, poorer academic adjustment and higher substance use over time. In contrast, we found no support for social jetlag as a predictor of
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…
Hierarchical Logistic Regression: Accounting for Multilevel Data in DIF Detection
ERIC Educational Resources Information Center
French, Brian F.; Finch, W. Holmes
2010-01-01
The purpose of this study was to examine the performance of differential item functioning (DIF) assessment in the presence of a multilevel structure that often underlies data from large-scale testing programs. Analyses were conducted using logistic regression (LR), a popular, flexible, and effective tool for DIF detection. Data were simulated…
Cutburth, Ronald W.; Silva, Leonard L.
1988-01-01
An improved mounting stage of the type used for the detection of laser beams is disclosed. A stage center block is mounted on each of two opposite sides by a pair of spaced ball bearing tracks which provide stability as well as simplicity. The use of the spaced ball bearing pairs in conjunction with an adjustment screw which also provides support eliminates extraneous stabilization components and permits maximization of the area of the center block laser transmission hole.
Ducker, W.L.
1982-09-14
A system of rotatably and pivotally mounted radially extended bent supports for radially extending windmill rotor vanes in combination with axially movable radially extended control struts connected to the vanes with semi-automatic and automatic torque and other sensing and servo units provide automatic adjustment of the windmill vanes relative to their axes of rotation to produce mechanical output at constant torque or at constant speed or electrical quantities dependent thereon.
Ducker, W.L.
1980-01-15
A system of rotatably and pivotally mounted radially extended bent supports for radially extending windmill rotor vanes in combination with axially movable radially extended control struts connected to the vanes with semi-automatic and automatic torque and other sensing and servo units provide automatic adjustment of the windmill vanes relative to their axes of rotation to produce mechanical output at constant torque or at constant speed or electrical quantities dependent thereon.
Ducker, W.L.
1982-09-07
A system of rotatably and pivotally mounted radially extended bent supports for radially extending windmill rotor vanes in combination with axially movable radially extended control struts connected to the vanes with semi-automatic and automatic torque and other sensing and servo units provide automatic adjustment of the windmill vanes relative to their axes of rotation to produce mechanical output at constant torque or at constant speed or electrical quantities dependent thereon.
NASA Technical Reports Server (NTRS)
Malin, Jane T.; Schrenkenghost, Debra K.
2001-01-01
The Adjustable Autonomy Testbed (AAT) is a simulation-based testbed located in the Intelligent Systems Laboratory in the Automation, Robotics and Simulation Division at NASA Johnson Space Center. The purpose of the testbed is to support evaluation and validation of prototypes of adjustable autonomous agent software for control and fault management for complex systems. The AA T project has developed prototype adjustable autonomous agent software and human interfaces for cooperative fault management. This software builds on current autonomous agent technology by altering the architecture, components and interfaces for effective teamwork between autonomous systems and human experts. Autonomous agents include a planner, flexible executive, low level control and deductive model-based fault isolation. Adjustable autonomy is intended to increase the flexibility and effectiveness of fault management with an autonomous system. The test domain for this work is control of advanced life support systems for habitats for planetary exploration. The CONFIG hybrid discrete event simulation environment provides flexible and dynamically reconfigurable models of the behavior of components and fluids in the life support systems. Both discrete event and continuous (discrete time) simulation are supported, and flows and pressures are computed globally. This provides fast dynamic simulations of interacting hardware systems in closed loops that can be reconfigured during operations scenarios, producing complex cascading effects of operations and failures. Current object-oriented model libraries support modeling of fluid systems, and models have been developed of physico-chemical and biological subsystems for processing advanced life support gases. In FY01, water recovery system models will be developed.
10 CFR 436.22 - Adjusted internal rate of return.
Code of Federal Regulations, 2011 CFR
2011-01-01
... Methodology and Procedures for Life Cycle Cost Analyses § 436.22 Adjusted internal rate of return. The adjusted internal rate of return is the overall rate of return on an energy or water conservation measure... yearly net savings in energy or water and non-fuel or non-water operation and maintenance...
10 CFR 436.22 - Adjusted internal rate of return.
Code of Federal Regulations, 2013 CFR
2013-01-01
... Methodology and Procedures for Life Cycle Cost Analyses § 436.22 Adjusted internal rate of return. The adjusted internal rate of return is the overall rate of return on an energy or water conservation measure... yearly net savings in energy or water and non-fuel or non-water operation and maintenance...
10 CFR 436.22 - Adjusted internal rate of return.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 10 Energy 3 2010-01-01 2010-01-01 false Adjusted internal rate of return. 436.22 Section 436.22 Energy DEPARTMENT OF ENERGY ENERGY CONSERVATION FEDERAL ENERGY MANAGEMENT AND PLANNING PROGRAMS Methodology and Procedures for Life Cycle Cost Analyses § 436.22 Adjusted internal rate of return....
Quality Reporting of Multivariable Regression Models in Observational Studies
Real, Jordi; Forné, Carles; Roso-Llorach, Albert; Martínez-Sánchez, Jose M.
2016-01-01
Abstract Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly used MRMs (logistic, linear, and Cox regression) that were applied in analytical observational studies published between 2003 and 2014 by journals indexed in MEDLINE. Review of a representative sample of articles indexed in MEDLINE (n = 428) with observational design and use of MRMs (logistic, linear, and Cox regression). We assessed the quality of reporting about: model assumptions and goodness-of-fit, interactions, sensitivity analysis, crude and adjusted effect estimate, and specification of more than 1 adjusted model. The tests of underlying assumptions or goodness-of-fit of the MRMs used were described in 26.2% (95% CI: 22.0–30.3) of the articles and 18.5% (95% CI: 14.8–22.1) reported the interaction analysis. Reporting of all items assessed was higher in articles published in journals with a higher impact factor. A low percentage of articles indexed in MEDLINE that used multivariable techniques provided information demonstrating rigorous application of the model selected as an adjustment method. Given the importance of these methods to the final results and conclusions of observational studies, greater rigor is required in reporting the use of MRMs in the scientific literature. PMID:27196467
Regression analysis of cytopathological data
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.
The Impact of Financial Sophistication on Adjustable Rate Mortgage Ownership
ERIC Educational Resources Information Center
Smith, Hyrum; Finke, Michael S.; Huston, Sandra J.
2011-01-01
The influence of a financial sophistication scale on adjustable-rate mortgage (ARM) borrowing is explored. Descriptive statistics and regression analysis using recent data from the Survey of Consumer Finances reveal that ARM borrowing is driven by both the least and most financially sophisticated households but for different reasons. Less…
Effects of Relational Authenticity on Adjustment to College
ERIC Educational Resources Information Center
Lenz, A. Stephen; Holman, Rachel L.; Lancaster, Chloe; Gotay, Stephanie G.
2016-01-01
The authors examined the association between relational health and student adjustment to college. Data were collected from 138 undergraduate students completing their 1st semester at a large university in the mid-southern United States. Regression analysis indicated that higher levels of relational authenticity were a predictor of success during…
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 ...
Risk-adjusted outcome models for public mental health outpatient programs.
Hendryx, M S; Dyck, D G; Srebnik, D
1999-01-01
OBJECTIVE: To develop and test risk-adjustment outcome models in publicly funded mental health outpatient settings. We developed prospective risk models that used demographic and diagnostic variables; client-reported functioning, satisfaction, and quality of life; and case manager clinical ratings to predict subsequent client functional status, health-related quality of life, and satisfaction with services. DATA SOURCES/STUDY SETTING: Data collected from 289 adult clients at five- and ten-month intervals, from six community mental health agencies in Washington state located primarily in suburban and rural areas. Data sources included client self-report, case manager ratings, and management information system data. STUDY DESIGN: Model specifications were tested using prospective linear regression analyses. Models were validated in a separate sample and comparative agency performance examined. PRINCIPAL FINDINGS: Presence of severe diagnoses, substance abuse, client age, and baseline functional status and quality of life were predictive of mental health outcomes. Unadjusted versus risk-adjusted scores resulted in differently ranked agency performance. CONCLUSIONS: Risk-adjusted functional status and patient satisfaction outcome models can be developed for public mental health outpatient programs. Research is needed to improve the predictive accuracy of the outcome models developed in this study, and to develop techniques for use in applied settings. The finding that risk adjustment changes comparative agency performance has important consequences for quality monitoring and improvement. Issues in public mental health risk adjustment are discussed, including static versus dynamic risk models, utilization versus outcome models, choice and timing of measures, and access and quality improvement incentives. PMID:10201857
Multiatlas Segmentation as Nonparametric Regression
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
Variable Selection in ROC Regression
2013-01-01
Regression models are introduced into the receiver operating characteristic (ROC) analysis to accommodate effects of covariates, such as genes. If many covariates are available, the variable selection issue arises. The traditional induced methodology separately models outcomes of diseased and nondiseased groups; thus, separate application of variable selections to two models will bring barriers in interpretation, due to differences in selected models. Furthermore, in the ROC regression, the accuracy of area under the curve (AUC) should be the focus instead of aiming at the consistency of model selection or the good prediction performance. In this paper, we obtain one single objective function with the group SCAD to select grouped variables, which adapts to popular criteria of model selection, and propose a two-stage framework to apply the focused information criterion (FIC). Some asymptotic properties of the proposed methods are derived. Simulation studies show that the grouped variable selection is superior to separate model selections. Furthermore, the FIC improves the accuracy of the estimated AUC compared with other criteria. PMID:24312135
Hunter, Paul R
2009-12-01
Household water treatment (HWT) is being widely promoted as an appropriate intervention for reducing the burden of waterborne disease in poor communities in developing countries. A recent study has raised concerns about the effectiveness of HWT, in part because of concerns over the lack of blinding and in part because of considerable heterogeneity in the reported effectiveness of randomized controlled trials. This study set out to attempt to investigate the causes of this heterogeneity and so identify factors associated with good health gains. Studies identified in an earlier systematic review and meta-analysis were supplemented with more recently published randomized controlled trials. A total of 28 separate studies of randomized controlled trials of HWT with 39 intervention arms were included in the analysis. Heterogeneity was studied using the "metareg" command in Stata. Initial analyses with single candidate predictors were undertaken and all variables significant at the P < 0.2 level were included in a final regression model. Further analyses were done to estimate the effect of the interventions over time by MonteCarlo modeling using @Risk and the parameter estimates from the final regression model. The overall effect size of all unblinded studies was relative risk = 0.56 (95% confidence intervals 0.51-0.63), but after adjusting for bias due to lack of blinding the effect size was much lower (RR = 0.85, 95% CI = 0.76-0.97). Four main variables were significant predictors of effectiveness of intervention in a multipredictor meta regression model: Log duration of study follow-up (regression coefficient of log effect size = 0.186, standard error (SE) = 0.072), whether or not the study was blinded (coefficient 0.251, SE 0.066) and being conducted in an emergency setting (coefficient -0.351, SE 0.076) were all significant predictors of effect size in the final model. Compared to the ceramic filter all other interventions were much less effective (Biosand 0.247, 0
Marston, Louise; Peacock, Janet L; Yu, Keming; Brocklehurst, Peter; Calvert, Sandra A; Greenough, Anne; Marlow, Neil
2009-07-01
Studies of prematurely born infants contain a relatively large percentage of multiple births, so the resulting data have a hierarchical structure with small clusters of size 1, 2 or 3. Ignoring the clustering may lead to incorrect inferences. The aim of this study was to compare statistical methods which can be used to analyse such data: generalised estimating equations, multilevel models, multiple linear regression and logistic regression. Four datasets which differed in total size and in percentage of multiple births (n = 254, multiple 18%; n = 176, multiple 9%; n = 10 098, multiple 3%; n = 1585, multiple 8%) were analysed. With the continuous outcome, two-level models produced similar results in the larger dataset, while generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) produced divergent estimates using the smaller dataset. For the dichotomous outcome, most methods, except generalised least squares multilevel modelling (ML GH 'xtlogit' in Stata) gave similar odds ratios and 95% confidence intervals within datasets. For the continuous outcome, our results suggest using multilevel modelling. We conclude that generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) should be used with caution when the dataset is small. Where the outcome is dichotomous and there is a relatively large percentage of non-independent data, it is recommended that these are accounted for in analyses using logistic regression with adjusted standard errors or multilevel modelling. If, however, the dataset has a small percentage of clusters greater than size 1 (e.g. a population dataset of children where there are few multiples) there appears to be less need to adjust for clustering.
Kokuhu, Takatoshi; Fukushima, Keizo; Ushigome, Hidetaka; Yoshimura, Norio; Sugioka, Nobuyuki
2013-01-01
The optimal use and monitoring of cyclosporine A (CyA) have remained unclear and the current strategy of CyA treatment requires frequent dose adjustment following an empirical initial dosage adjusted for total body weight (TBW). The primary aim of this study was to evaluate age and anthropometric parameters as predictors for dose adjustment of CyA; and the secondary aim was to compare the usefulness of the concentration at predose (C0) and 2-hour postdose (C2) monitoring. An open-label, non-randomized, retrospective study was performed in 81 renal transplant patients in Japan during 2001-2010. The relationships between the area under the blood concentration-time curve (AUC0-9) of CyA and its C0 or C2 level were assessed with a linear regression analysis model. In addition to age, 7 anthropometric parameters were tested as predictors for AUC0-9 of CyA: TBW, height (HT), body mass index (BMI), body surface area (BSA), ideal body weight (IBW), lean body weight (LBW), and fat free mass (FFM). Correlations between AUC0-9 of CyA and these parameters were also analyzed with a linear regression model. The rank order of the correlation coefficient was C0 > C2 (C0; r=0.6273, C2; r=0.5562). The linear regression analyses between AUC0-9 of CyA and candidate parameters indicated their potential usefulness from the following rank order: IBW > FFM > HT > BSA > LBW > TBW > BMI > Age. In conclusion, after oral administration, C2 monitoring has a large variation and could be at high risk for overdosing. Therefore, after oral dosing of CyA, it was not considered to be a useful approach for single monitoring, but should rather be used with C0 monitoring. The regression analyses between AUC0-9 of CyA and anthropometric parameters indicated that IBW was potentially the superior predictor for dose adjustment of CyA in an empiric strategy using TBW (IBW; r=0.5181, TBW; r=0.3192); however, this finding seems to lack the pharmacokinetic rationale and thus warrants further basic and clinical
Spatial regression analysis on 32 years of total column ozone data
NASA Astrophysics Data System (ADS)
Knibbe, J. S.; van der A, R. J.; de Laat, A. T. J.
2014-08-01
Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data (2009-2010). The two-dimensionality in this data set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on nonseasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO) and stratospheric alternative halogens which are parameterized by the effective equivalent stratospheric chlorine (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of a similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at mid- and high latitudes, the solar cycle affects ozone positively mostly in the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high northern latitudes, the effect of QBO is positive and negative in the tropics and mid- to high latitudes, respectively, and ENSO affects ozone negatively
Anchoring and adjustment during social inferences.
Tamir, Diana I; Mitchell, Jason P
2013-02-01
Simulation theories of social cognition suggest that people use their own mental states to understand those of others-particularly similar others. However, perceivers cannot rely solely on self-knowledge to understand another person; they must also correct for differences between the self and others. Here we investigated serial adjustment as a mechanism for correction from self-knowledge anchors during social inferences. In 3 studies, participants judged the attitudes of a similar or dissimilar person and reported their own attitudes. For each item, we calculated the discrepancy between responses for the self and other. The adjustment process unfolds serially, so to the extent that individuals indeed anchor on self-knowledge and then adjust away, trials with a large amount of self-other discrepancy should be associated with longer response times, whereas small self-other discrepancy should correspond to shorter response times. Analyses consistently revealed this positive linear relationship between reaction time and self-other discrepancy, evidence of anchoring-and-adjustment, but only during judgments of similar targets. These results suggest that perceivers mentalize about similar others using the cognitive process of anchoring-and-adjustment. PMID:22506753
Subsea adjustable choke valves
Cyvas, M.K. )
1989-08-01
With emphasis on deepwater wells and marginal offshore fields growing, the search for reliable subsea production systems has become a high priority. A reliable subsea adjustable choke is essential to the realization of such a system, and recent advances are producing the degree of reliability required. Technological developments have been primarily in (1) trim material (including polycrystalline diamond), (2) trim configuration, (3) computer programs for trim sizing, (4) component materials, and (5) diver/remote-operated-vehicle (ROV) interfaces. These five facets are overviewed and progress to date is reported. A 15- to 20-year service life for adjustable subsea chokes is now a reality. Another factor vital to efficient use of these technological developments is to involve the choke manufacturer and ROV/diver personnel in initial system conceptualization. In this manner, maximum benefit can be derived from the latest technology. Major areas of development still required and under way are listed, and the paper closes with a tabulation of successful subsea choke installations in recent years.
Geometric adjustment of pools to changes in slope and discharge: a flume experiment
NASA Astrophysics Data System (ADS)
Thompson, Douglas M.
2002-08-01
To characterize the factors controlling pool shape, 30 different forced pools were created utilizing a 50% triangular constriction in a 0.5-m wide, 6-m long recirculating flume. Pools were scoured from an initial plane bed of sand with a d50 of 0.25 mm. Pool depth and length were measured and used as dependent variables in least-squares, multiple-regression analyses. Discharge, channel-bed gradient and energy slope were the independent variables. Additional linear-regression analyses were conducted with either pool depth or length and stream power. Results indicate that both pool depth and length are primarily a function of discharge. Channel-bed and energy slopes are also significantly related to pool length but are not significantly related to pool depth. Stream power is significantly related to both pool depth and length, but R2 values for pool depth versus discharge indicate stronger relations than those between pool depth and stream power. Observations on the type of geometric adjustment indicate that pools may minimize their rate of energy expenditure primarily through elongation. In contrast, pool depth appears to be more sensitive to the characteristics of the constrictions that create the forced pools. The results suggest that many field studies may suffer from cross-correlation problems. In particular, channel erodibility may exert a more dominant influence on pool geometry than hydraulic controls in many constriction-influenced channels.
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).
Adolescent Mothers' Adjustment to Parenting.
ERIC Educational Resources Information Center
Samuels, Valerie Jarvis; And Others
1994-01-01
Examined adolescent mothers' adjustment to parenting, self-esteem, social support, and perceptions of baby. Subjects (n=52) responded to questionnaires at two time periods approximately six months apart. Mothers with higher self-esteem at Time 1 had better adjustment at Time 2. Adjustment was predicted by Time 2 variables; contact with baby's…
Langberg, Joshua M; Dvorsky, Melissa R; Kipperman, Kristen L; Molitor, Stephen J; Eddy, Laura D
2015-06-01
The primary aim of this study was to evaluate whether alcohol consumption longitudinally predicts the adjustment, overall functioning, and grade point average (GPA) of college students with ADHD and to determine whether self-report of executive functioning (EF) mediates these relationships. Sixty-two college students comprehensively diagnosed with ADHD completed ratings at the beginning and end of the school year. Regression analyses revealed that alcohol consumption rated at the beginning of the year significantly predicted self-report of adjustment and overall impairment at the end of the year, above and beyond ADHD symptoms and baseline levels of adjustment/impairment but did not predict GPA. Exploratory multiple mediator analyses suggest that alcohol use impacts impairment primarily through EF deficits in self-motivation. EF deficits in the motivation to refrain from pursuing immediately rewarding behaviors in order to work toward long-term goals appear to be particularly important in understanding why college students with ADHD who consume alcohol have a higher likelihood of experiencing significant negative outcomes. The implications of these findings for the prevention of the negative functional outcomes often experienced by college students with ADHD are discussed. (PsycINFO Database Record
Allen, Brian
2008-08-01
Recent research has documented the long-term mental health consequences of childhood psychological maltreatment; however, this research is limited in that it typically fails to recognize the qualitative differences of the various behaviors labeled as psychological maltreatment. This study examines the predictive ability of caregiver terrorizing, degradation, ignoring, and isolating during childhood on the self-reported occurrence of anxiety, depression, somatic complaints, and features of borderline personality disorder (BPD) in a sample of 256 university students between the ages of 18 and 22. Witnessing violence and childhood physical abuse are included in the analyses. Simultaneous regression analyses reveal that different forms of maltreatment emerge as predictors of the variables of emotional adjustment. Terrorizing predicted anxiety and somatic concerns, ignoring predicted scores of depression and features of BPD, and degradation predicted BPD features only. Findings suggest psychological maltreatment is a multifaceted construct requiring further research to investigate the long-term impact of various subtypes. PMID:18556593
Semiparametric regression during 2003–2007*
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
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.…
Building Regression Models: The Importance of Graphics.
ERIC Educational Resources Information Center
Dunn, Richard
1989-01-01
Points out reasons for using graphical methods to teach simple and multiple regression analysis. Argues that a graphically oriented approach has considerable pedagogic advantages in the exposition of simple and multiple regression. Shows that graphical methods may play a central role in the process of building regression models. (Author/LS)
Regression Analysis by Example. 5th Edition
ERIC Educational Resources Information Center
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…
Standards for Standardized Logistic Regression Coefficients
ERIC Educational Resources Information Center
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
A Bayesian approach to linear regression in astronomy
NASA Astrophysics Data System (ADS)
Sereno, Mauro
2016-01-01
Linear regression is common in astronomical analyses. I discuss a Bayesian hierarchical modelling of data with heteroscedastic and possibly correlated measurement errors and intrinsic scatter. The method fully accounts for time evolution. The slope, the normalization, and the intrinsic scatter of the relation can evolve with the redshift. The intrinsic distribution of the independent variable is approximated using a mixture of Gaussian distributions whose means and standard deviations depend on time. The method can address scatter in the measured independent variable (a kind of Eddington bias), selection effects in the response variable (Malmquist bias), and departure from linearity in form of a knee. I tested the method with toy models and simulations and quantified the effect of biases and inefficient modelling. The R-package LIRA (LInear Regression in Astronomy) is made available to perform the regression.
Estimating equivalence with quantile regression
Cade, B.S.
2011-01-01
Equivalence testing and corresponding confidence interval estimates are used to provide more enlightened statistical statements about parameter estimates by relating them to intervals of effect sizes deemed to be of scientific or practical importance rather than just to an effect size of zero. Equivalence tests and confidence interval estimates are based on a null hypothesis that a parameter estimate is either outside (inequivalence hypothesis) or inside (equivalence hypothesis) an equivalence region, depending on the question of interest and assignment of risk. The former approach, often referred to as bioequivalence testing, is often used in regulatory settings because it reverses the burden of proof compared to a standard test of significance, following a precautionary principle for environmental protection. Unfortunately, many applications of equivalence testing focus on establishing average equivalence by estimating differences in means of distributions that do not have homogeneous variances. I discuss how to compare equivalence across quantiles of distributions using confidence intervals on quantile regression estimates that detect differences in heterogeneous distributions missed by focusing on means. I used one-tailed confidence intervals based on inequivalence hypotheses in a two-group treatment-control design for estimating bioequivalence of arsenic concentrations in soils at an old ammunition testing site and bioequivalence of vegetation biomass at a reclaimed mining site. Two-tailed confidence intervals based both on inequivalence and equivalence hypotheses were used to examine quantile equivalence for negligible trends over time for a continuous exponential model of amphibian abundance. ?? 2011 by the Ecological Society of America.
Insulin resistance: regression and clustering.
Yoon, Sangho; Assimes, Themistocles L; Quertermous, Thomas; Hsiao, Chin-Fu; Chuang, Lee-Ming; Hwu, Chii-Min; Rajaratnam, Bala; Olshen, Richard A
2014-01-01
In this paper we try to define insulin resistance (IR) precisely for a group of Chinese women. Our definition deliberately does not depend upon body mass index (BMI) or age, although in other studies, with particular random effects models quite different from models used here, BMI accounts for a large part of the variability in IR. We accomplish our goal through application of Gauss mixture vector quantization (GMVQ), a technique for clustering that was developed for application to lossy data compression. Defining data come from measurements that play major roles in medical practice. A precise statement of what the data are is in Section 1. Their family structures are described in detail. They concern levels of lipids and the results of an oral glucose tolerance test (OGTT). We apply GMVQ to residuals obtained from regressions of outcomes of an OGTT and lipids on functions of age and BMI that are inferred from the data. A bootstrap procedure developed for our family data supplemented by insights from other approaches leads us to believe that two clusters are appropriate for defining IR precisely. One cluster consists of women who are IR, and the other of women who seem not to be. Genes and other features are used to predict cluster membership. We argue that prediction with "main effects" is not satisfactory, but prediction that includes interactions may be. PMID:24887437
Psychosocial Predictors of Adjustment among First Year College of Education Students
ERIC Educational Resources Information Center
Salami, Samuel O.
2011-01-01
The purpose of this study was to examine the contribution of psychological and social factors to the prediction of adjustment to college. A total of 250 first year students from colleges of education in Kwara State, Nigeria, completed measures of self-esteem, emotional intelligence, stress, social support and adjustment. Regression analyses…
ERIC Educational Resources Information Center
Hickman, Gregory P.; Bartholomae, Suzanne; McKenry, Patrick C.
2000-01-01
Examines the relationship between parenting styles and academic achievement and adjustment of traditional college freshmen (N=101). Multiple regression models indicate that authoritative parenting style was positively related to student's academic adjustment. Self-esteem was significantly predictive of social, personal-emotional, goal…
ERIC Educational Resources Information Center
Raymond, Mark R.; Harik, Polina; Clauser, Brian E.
2011-01-01
Prior research indicates that the overall reliability of performance ratings can be improved by using ordinary least squares (OLS) regression to adjust for rater effects. The present investigation extends previous work by evaluating the impact of OLS adjustment on standard errors of measurement ("SEM") at specific score levels. In addition, a…
Fully Regressive Melanoma: A Case Without Metastasis.
Ehrsam, Eric; Kallini, Joseph R; Lebas, Damien; Khachemoune, Amor; Modiano, Philippe; Cotten, Hervé
2016-08-01
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
Delay Adjusted Incidence Infographic
This Infographic shows the National Cancer Institute SEER Incidence Trends. The graphs show the Average Annual Percent Change (AAPC) 2002-2011. For Men, Thyroid: 5.3*,Liver & IBD: 3.6*, Melanoma: 2.3*, Kidney: 2.0*, Myeloma: 1.9*, Pancreas: 1.2*, Leukemia: 0.9*, Oral Cavity: 0.5, Non-Hodgkin Lymphoma: 0.3*, Esophagus: -0.1, Brain & ONS: -0.2*, Bladder: -0.6*, All Sites: -1.1*, Stomach: -1.7*, Larynx: -1.9*, Prostate: -2.1*, Lung & Bronchus: -2.4*, and Colon & Rectum: -3/0*. For Women, Thyroid: 5.8*, Liver & IBD: 2.9*, Myeloma: 1.8*, Kidney: 1.6*, Melanoma: 1.5, Corpus & Uterus: 1.3*, Pancreas: 1.1*, Leukemia: 0.6*, Brain & ONS: 0, Non-Hodgkin Lymphoma: -0.1, All Sites: -0.1, Breast: -0.3, Stomach: -0.7*, Oral Cavity: -0.7*, Bladder: -0.9*, Ovary: -0.9*, Lung & Bronchus: -1.0*, Cervix: -2.4*, and Colon & Rectum: -2.7*. * AAPC is significantly different from zero (p<.05). Rates were adjusted for reporting delay in the registry. www.cancer.gov Source: Special section of the Annual Report to the Nation on the Status of Cancer, 1975-2011.
Developmental regression in autism spectrum disorder
Al Backer, Nouf Backer
2015-01-01
The occurrence of developmental regression in autism spectrum disorder (ASD) is one of the most puzzling phenomena of this disorder. A little is known about the nature and mechanism of developmental regression in ASD. About one-third of young children with ASD lose some skills during the preschool period, usually speech, but sometimes also nonverbal communication, social or play skills are also affected. There is a lot of evidence suggesting that most children who demonstrate regression also had previous, subtle, developmental differences. It is difficult to predict the prognosis of autistic children with developmental regression. It seems that the earlier development of social, language, and attachment behaviors followed by regression does not predict the later recovery of skills or better developmental outcomes. The underlying mechanisms that lead to regression in autism are unknown. The role of subclinical epilepsy in the developmental regression of children with autism remains unclear. PMID:27493417
Risk factors for autistic regression: results of an ambispective cohort study.
Zhang, Ying; Xu, Qiong; Liu, Jing; Li, She-chang; Xu, Xiu
2012-08-01
A subgroup of children diagnosed with autism experience developmental regression featured by a loss of previously acquired abilities. The pathogeny of autistic regression is unknown, although many risk factors likely exist. To better characterize autistic regression and investigate the association between autistic regression and potential influencing factors in Chinese autistic children, we conducted an ambispective study with a cohort of 170 autistic subjects. Analyses by multiple logistic regression showed significant correlations between autistic regression and febrile seizures (OR = 3.53, 95% CI = 1.17-10.65, P = .025), as well as with a family history of neuropsychiatric disorders (OR = 3.62, 95% CI = 1.35-9.71, P = .011). This study suggests that febrile seizures and family history of neuropsychiatric disorders are correlated with autistic regression.
Analysis of Differential Item Functioning (DIF) Using Hierarchical Logistic Regression Models.
ERIC Educational Resources Information Center
Swanson, David B.; Clauser, Brian E.; Case, Susan M.; Nungester, Ronald J.; Featherman, Carol
2002-01-01
Outlines an approach to differential item functioning (DIF) analysis using hierarchical linear regression that makes it possible to combine results of logistic regression analyses across items to identify consistent sources of DIF, to quantify the proportion of explained variation in DIF coefficients, and to compare the predictive accuracy of…
ERIC Educational Resources Information Center
Tong, Fuhui
2006-01-01
Background: An extensive body of researches has favored the use of regression over other parametric analyses that are based on OVA. In case of noteworthy regression results, researchers tend to explore magnitude of beta weights for the respective predictors. Purpose: The purpose of this paper is to examine both beta weights and structure…
A Tutorial on Calculating and Interpreting Regression Coefficients in Health Behavior Research
ERIC Educational Resources Information Center
Stellefson, Michael L.; Hanik, Bruce W.; Chaney, Beth H.; Chaney, J. Don
2008-01-01
Regression analyses are frequently employed by health educators who conduct empirical research examining a variety of health behaviors. Within regression, there are a variety of coefficients produced, which are not always easily understood and/or articulated by health education researchers. It is important to not only understand what these…
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.
Quantile regression provides a fuller analysis of speed data.
Hewson, Paul
2008-03-01
Considerable interest already exists in terms of assessing percentiles of speed distributions, for example monitoring the 85th percentile speed is a common feature of the investigation of many road safety interventions. However, unlike the mean, where t-tests and ANOVA can be used to provide evidence of a statistically significant change, inference on these percentiles is much less common. This paper examines the potential role of quantile regression for modelling the 85th percentile, or any other quantile. Given that crash risk may increase disproportionately with increasing relative speed, it may be argued these quantiles are of more interest than the conditional mean. In common with the more usual linear regression, quantile regression admits a simple test as to whether the 85th percentile speed has changed following an intervention in an analogous way to using the t-test to determine if the mean speed has changed by considering the significance of parameters fitted to a design matrix. Having briefly outlined the technique and briefly examined an application with a widely published dataset concerning speed measurements taken around the introduction of signs in Cambridgeshire, this paper will demonstrate the potential for quantile regression modelling by examining recent data from Northamptonshire collected in conjunction with a "community speed watch" programme. Freely available software is used to fit these models and it is hoped that the potential benefits of using quantile regression methods when examining and analysing speed data are demonstrated.
Association between regression and self injury among children with autism.
Lance, Eboni I; York, Janet M; Lee, Li-Ching; Zimmerman, Andrew W
2014-02-01
Self injurious behaviors (SIBs) are challenging clinical problems in individuals with autism spectrum disorders (ASDs). This study is one of the first and largest to utilize inpatient data to examine the associations between autism, developmental regression, and SIBs. Medical records of 125 neurobehavioral hospitalized patients with diagnoses of ASDs and SIBs between 4 and 17 years of age were reviewed. Data were collected from medical records on the type and frequency of SIBs and a history of language, social, or behavioral regression during development. The children with a history of any type of developmental regression (social, behavioral, or language) were more likely to have a diagnosis of autistic disorder than other ASD diagnoses. There were no significant differences in the occurrence of self injurious or other problem behaviors (such as aggression or disruption) between children with and without regression. Regression may influence the diagnostic considerations in ASDs but does not seem to influence the clinical phenotype with regard to behavioral issues. Additional data analyses explored the frequencies and subtypes of SIBs and other medical diagnoses in ASDs, with intellectual disability and disruptive behavior disorder found most commonly.
Erdmann, Christine A.; Steiner, Kate C.; Apte, Michael G.
2002-02-01
In previously published analyses of the 41-building 1994-1996 USEPA Building Assessment Survey and Evaluation (BASE) dataset, higher workday time-averaged indoor minus outdoor CO{sub 2} concentrations (dCO{sub 2}) were associated with increased prevalence of certain mucous membrane and lower respiratory sick building syndrome (SBS) symptoms, even at peak dCO{sub 2} concentrations below 1,000 ppm. For this paper, similar analyses were performed using the larger 100-building 1994-1998 BASE dataset. Multivariate logistic regression analyses quantified the associations between dCO{sub 2} and the SBS symptoms, adjusting for age, sex, smoking status, presence of carpet in workspace, thermal exposure, relative humidity, and a marker for entrained automobile exhaust. Adjusted dCO{sub 2} prevalence odds ratios for sore throat and wheeze were 1.17 and 1.20 per 100-ppm increase in dCO{sub 2} (p <0.05), respectively. These new analyses generally support our prior findings. Regional differences in climate, building design, and operation may account for some of the differences observed in analyses of the two datasets.
Bakhtiyari, Mahmood; Mehmandar, Mohammad Reza; Mirbagheri, Babak; Hariri, Gholam Reza; Delpisheh, Ali; Soori, Hamid
2014-01-01
Risk factors of human-related traffic crashes are the most important and preventable challenges for community health due to their noteworthy burden in developing countries in particular. The present study aims to investigate the role of human risk factors of road traffic crashes in Iran. Through a cross-sectional study using the COM 114 data collection forms, the police records of almost 600,000 crashes occurred in 2010 are investigated. The binary logistic regression and proportional odds regression models are used. The odds ratio for each risk factor is calculated. These models are adjusted for known confounding factors including age, sex and driving time. The traffic crash reports of 537,688 men (90.8%) and 54,480 women (9.2%) are analysed. The mean age is 34.1 ± 14 years. Not maintaining eyes on the road (53.7%) and losing control of the vehicle (21.4%) are the main causes of drivers' deaths in traffic crashes within cities. Not maintaining eyes on the road is also the most frequent human risk factor for road traffic crashes out of cities. Sudden lane excursion (OR = 9.9, 95% CI: 8.2-11.9) and seat belt non-compliance (OR = 8.7, CI: 6.7-10.1), exceeding authorised speed (OR = 17.9, CI: 12.7-25.1) and exceeding safe speed (OR = 9.7, CI: 7.2-13.2) are the most significant human risk factors for traffic crashes in Iran. The high mortality rate of 39 people for every 100,000 population emphasises on the importance of traffic crashes in Iran. Considering the important role of human risk factors in traffic crashes, struggling efforts are required to control dangerous driving behaviours such as exceeding speed, illegal overtaking and not maintaining eyes on the road.
Life Events, Sibling Warmth, and Youths' Adjustment.
Waite, Evelyn B; Shanahan, Lilly; Calkins, Susan D; Keane, Susan P; O'Brien, Marion
2011-10-01
Sibling warmth has been identified as a protective factor from life events, but stressor-support match-mismatch and social domains perspectives suggest that sibling warmth may not efficiently protect youths from all types of life events. We tested whether sibling warmth moderated the association between each of family-wide, youths' personal, and siblings' personal life events and both depressive symptoms and risk-taking behaviors. Participants were 187 youths aged 9-18 (M = 11.80 years old, SD = 2.05). Multiple regression models revealed that sibling warmth was a protective factor from depressive symptoms for family-wide events, but not for youths' personal and siblings' personal life events. Findings highlight the importance of contextualizing protective functions of sibling warmth by taking into account the domains of stressors and adjustment. PMID:22241934
LRGS: Linear Regression by Gibbs Sampling
NASA Astrophysics Data System (ADS)
Mantz, Adam B.
2016-02-01
LRGS (Linear Regression by Gibbs Sampling) implements a Gibbs sampler to solve the problem of multivariate linear regression with uncertainties in all measured quantities and intrinsic scatter. LRGS extends an algorithm by Kelly (2007) that used Gibbs sampling for performing linear regression in fairly general cases in two ways: generalizing the procedure for multiple response variables, and modeling the prior distribution of covariates using a Dirichlet process.
Quantile regression applied to spectral distance decay
Rocchini, D.; Cade, B.S.
2008-01-01
Remotely sensed imagery has long been recognized as a powerful support for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance allows us to quantitatively estimate the amount of turnover in species composition with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological data sets are characterized by a high number of zeroes that add noise to the regression model. Quantile regressions can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this letter, we used ordinary least squares (OLS) and quantile regressions to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.01), considering both OLS and quantile regressions. Nonetheless, the OLS regression estimate of the mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when the spectral distance approaches zero, was very low compared with the intercepts of the upper quantiles, which detected high species similarity when habitats are more similar. In this letter, we demonstrated the power of using quantile regressions applied to spectral distance decay to reveal species diversity patterns otherwise lost or underestimated by OLS regression. ?? 2008 IEEE.
Regression Calibration with Heteroscedastic Error Variance
Spiegelman, Donna; Logan, Roger; Grove, Douglas
2011-01-01
The problem of covariate measurement error with heteroscedastic measurement error variance is considered. Standard regression calibration assumes that the measurement error has a homoscedastic measurement error variance. An estimator is proposed to correct regression coefficients for covariate measurement error with heteroscedastic variance. Point and interval estimates are derived. Validation data containing the gold standard must be available. This estimator is a closed-form correction of the uncorrected primary regression coefficients, which may be of logistic or Cox proportional hazards model form, and is closely related to the version of regression calibration developed by Rosner et al. (1990). The primary regression model can include multiple covariates measured without error. The use of these estimators is illustrated in two data sets, one taken from occupational epidemiology (the ACE study) and one taken from nutritional epidemiology (the Nurses’ Health Study). In both cases, although there was evidence of moderate heteroscedasticity, there was little difference in estimation or inference using this new procedure compared to standard regression calibration. It is shown theoretically that unless the relative risk is large or measurement error severe, standard regression calibration approximations will typically be adequate, even with moderate heteroscedasticity in the measurement error model variance. In a detailed simulation study, standard regression calibration performed either as well as or better than the new estimator. When the disease is rare and the errors normally distributed, or when measurement error is moderate, standard regression calibration remains the method of choice. PMID:22848187
Process modeling with the regression network.
van der Walt, T; Barnard, E; van Deventer, J
1995-01-01
A new connectionist network topology called the regression network is proposed. The structural and underlying mathematical features of the regression network are investigated. Emphasis is placed on the intricacies of the optimization process for the regression network and some measures to alleviate these difficulties of optimization are proposed and investigated. The ability of the regression network algorithm to perform either nonparametric or parametric optimization, as well as a combination of both, is also highlighted. It is further shown how the regression network can be used to model systems which are poorly understood on the basis of sparse data. A semi-empirical regression network model is developed for a metallurgical processing operation (a hydrocyclone classifier) by building mechanistic knowledge into the connectionist structure of the regression network model. Poorly understood aspects of the process are provided for by use of nonparametric regions within the structure of the semi-empirical connectionist model. The performance of the regression network model is compared to the corresponding generalization performance results obtained by some other nonparametric regression techniques.
Hybrid fuzzy regression with trapezoidal fuzzy data
NASA Astrophysics Data System (ADS)
Razzaghnia, T.; Danesh, S.; Maleki, A.
2011-12-01
In this regard, this research deals with a method for hybrid fuzzy least-squares regression. The extension of symmetric triangular fuzzy coefficients to asymmetric trapezoidal fuzzy coefficients is considered as an effective measure for removing unnecessary fuzziness of the linear fuzzy model. First, trapezoidal fuzzy variable is applied to derive a bivariate regression model. In the following, normal equations are formulated to solve the four parts of hybrid regression coefficients. Also the model is extended to multiple regression analysis. Eventually, method is compared with Y-H.O. chang's model.
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R(2)) indicates the importance of independent variables in the outcome.
Geodesic least squares regression on information manifolds
Verdoolaege, Geert
2014-12-05
We present a novel regression method targeted at situations with significant uncertainty on both the dependent and independent variables or with non-Gaussian distribution models. Unlike the classic regression model, the conditional distribution of the response variable suggested by the data need not be the same as the modeled distribution. Instead they are matched by minimizing the Rao geodesic distance between them. This yields a more flexible regression method that is less constrained by the assumptions imposed through the regression model. As an example, we demonstrate the improved resistance of our method against some flawed model assumptions and we apply this to scaling laws in magnetic confinement fusion.
Mood Adjustment via Mass Communication.
ERIC Educational Resources Information Center
Knobloch, Silvia
2003-01-01
Proposes and experimentally tests mood adjustment approach, complementing mood management theory. Discusses how results regarding self-exposure across time show that patterns of popular music listening among a group of undergraduate students differ with initial mood and anticipation, lending support to mood adjustment hypotheses. Describes how…
Spousal Adjustment to Myocardial Infarction.
ERIC Educational Resources Information Center
Ziglar, Elisa J.
This paper reviews the literature on the stresses and coping strategies of spouses of patients with myocardial infarction (MI). It attempts to identify specific problem areas of adjustment for the spouse and to explore the effects of spousal adjustment on patient recovery. Chapter one provides an overview of the importance in examining the…
Data correction for seven activity trackers based on regression models.
Andalibi, Vafa; Honko, Harri; Christophe, Francois; Viik, Jari
2015-08-01
Using an activity tracker for measuring activity-related parameters, e.g. steps and energy expenditure (EE), can be very helpful in assisting a person's fitness improvement. Unlike the measuring of number of steps, an accurate EE estimation requires additional personal information as well as accurate velocity of movement, which is hard to achieve due to inaccuracy of sensors. In this paper, we have evaluated regression-based models to improve the precision for both steps and EE estimation. For this purpose, data of seven activity trackers and two reference devices was collected from 20 young adult volunteers wearing all devices at once in three different tests, namely 60-minute office work, 6-hour overall activity and 60-minute walking. Reference data is used to create regression models for each device and relative percentage errors of adjusted values are then statistically compared to that of original values. The effectiveness of regression models are determined based on the result of a statistical test. During a walking period, EE measurement was improved in all devices. The step measurement was also improved in five of them. The results show that improvement of EE estimation is possible only with low-cost implementation of fitting model over the collected data e.g. in the app or in corresponding service back-end. PMID:26736578
ERIC Educational Resources Information Center
Bulcock, J. W.
The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…
Parental Divorce and Children's Adjustment.
Lansford, Jennifer E
2009-03-01
This article reviews the research literature on links between parental divorce and children's short-term and long-term adjustment. First, I consider evidence regarding how divorce relates to children's externalizing behaviors, internalizing problems, academic achievement, and social relationships. Second, I examine timing of the divorce, demographic characteristics, children's adjustment prior to the divorce, and stigmatization as moderators of the links between divorce and children's adjustment. Third, I examine income, interparental conflict, parenting, and parents well-being as mediators of relations between divorce and children's adjustment. Fourth, I note the caveats and limitations of the research literature. Finally, I consider notable policies related to grounds for divorce, child support, and child custody in light of how they might affect children s adjustment to their parents divorce.
Multiple regression and principal components analysis of puberty and growth in cattle.
Baker, J F; Stewart, T S; Long, C R; Cartwright, T C
1988-09-01
Multiple regression and principal components analyses were employed to examine relationships among pubertal and growth characters. Records used were from 424 bulls and 475 heifers produced by a diallel mating of Angus, Brahman, Hereford, Holstein and Jersey breeds. Characters studied were age, weight and height at puberty and measurements of weight and hip height from 9 to 21 mo of age; pelvic measurements of heifers also were included. Measurements of weight and height near 1 yr of age were related most highly to pubertal age, weight adn height. Larger size near 1 yr of age was associated with younger, larger animals at puberty. Growth rate was associated with pubertal characters before, but not after, adjustment for effects of breed-type. Principal components of the variation of pubertal and growth characters among animals were strongly related to both weight and height. The majority of the variation among breed-types was due to height. Characteristic vectors of principal components describing the variation of bulls and heifers were strikingly similar. The variance-covariance structure of pubertal characters was essentially the same for both sexes even though the mean values of the characters differed. PMID:3170369
Imai, Chisato; Hashizume, Masahiro
2015-01-01
Background: Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted. Findings: Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms. Conclusion: The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases. PMID:25859149
Adjustment versus no adjustment when using adjustable sutures in strabismus surgery
Liebermann, Laura; Hatt, Sarah R.; Leske, David A.; Holmes, Jonathan M.
2013-01-01
Purpose To compare long-term postoperative outcomes when performing an adjustment to achieve a desired immediate postoperative alignment versus simply tying off at the desired immediate postoperative alignment when using adjustable sutures for strabismus surgery. Methods We retrospectively identified 89 consecutive patients who underwent a reoperation for horizontal strabismus using adjustable sutures and also had a 6-week and 1-year outcome examination. In each case, the intent of the surgeon was to tie off and only to adjust if the patient was not within the intended immediate postoperative range. Postoperative success was predefined based on angle of misalignment and diplopia at distance and near. Results Of the 89 patients, 53 (60%) were adjusted and 36 (40%) were tied off. Success rates were similar between patients who were simply tied off immediately after surgery and those who were adjusted. At 6 weeks, the success rate was 64% for the nonadjusted group versus 81% for the adjusted group (P = 0.09; difference of 17%; 95% CI, −2% to 36%). At 1 year, the success rate was 67% for the nonadjusted group versus 77% for the adjusted group (P = 0.3; difference of 11%; 95% CI, −8% to 30%). Conclusions Performing an adjustment to obtain a desired immediate postoperative alignment did not yield inferior long-term outcomes to those obtained by tying off to obtain that initial alignment. If patients were who were outside the desired immediate postoperative range had not been not adjusted, it is possible that their long-term outcomes would have been worse, therefore, overall, an adjustable approach may be superior to a nonadjustable approach. PMID:23415035
Risk-adjusted monitoring of survival times.
Sego, Landon H; Reynolds, Marion R; Woodall, William H
2009-04-30
We consider the monitoring of surgical outcomes, where each patient has a different risk of post-operative mortality due to risk factors that exist prior to the surgery. We propose a risk-adjusted (RA) survival time CUSUM chart (RAST CUSUM) for monitoring a continuous, time-to-event variable that may be right-censored. Risk adjustment is accomplished using accelerated failure time regression models. We compare the average run length performance of the RAST CUSUM chart with the RA Bernoulli CUSUM chart using data from cardiac surgeries to motivate the details of the comparison. The comparisons show that the RAST CUSUM chart is more efficient at detecting a sudden increase in the odds of mortality than the RA Bernoulli CUSUM chart, especially when the fraction of censored observations is relatively low or when a small increase in the odds of mortality occurs. We also discuss the impact of the amount of training data used to estimate chart parameters as well as the implementation of the RAST CUSUM chart during prospective monitoring.
Fitts' Law in early postural adjustments.
Bertucco, M; Cesari, P; Latash, M L
2013-02-12
We tested a hypothesis that the classical relation between movement time and index of difficulty (ID) in quick pointing action (Fitts' Law) reflects processes at the level of motor planning. Healthy subjects stood on a force platform and performed quick and accurate hand movements into targets of different size located at two distances. The movements were associated with early postural adjustments that are assumed to reflect motor planning processes. The short distance did not require trunk rotation, while the long distance did. As a result, movements over the long distance were associated with substantial Coriolis forces. Movement kinematics and contact forces and moments recorded by the platform were studied. Movement time scaled with ID for both movements. However, the data could not be fitted with a single regression: Movements over the long distance had a larger intercept corresponding to movement times about 140 ms longer than movements over the shorter distance. The magnitude of postural adjustments prior to movement initiation scaled with ID for both short and long distances. Our results provide strong support for the hypothesis that Fitts' Law emerges at the level of motor planning, not at the level of corrections of ongoing movements. They show that, during natural movements, changes in movement distance may lead to changes in the relation between movement time and ID, for example when the contribution of different body segments to the movement varies and when the action of Coriolis force may require an additional correction of the movement trajectory. PMID:23211560
Fitts’ Law in Early Postural Adjustments
Bertucco, M.; Cesari, P.; Latash, M.L
2012-01-01
We tested a hypothesis that the classical relation between movement time and index of difficulty (ID) in quick pointing action (Fitts’ Law) reflects processes at the level of motor planning. Healthy subjects stood on a force platform and performed quick and accurate hand movements into targets of different size located at two distances. The movements were associated with early postural adjustments that are assumed to reflect motor planning processes. The short distance did not require trunk rotation, while the long distance did. As a result, movements over the long distance were associated with substantiual Coriolis forces. Movement kinematics and contact forces and moments recorded by the platform were studied. Movement time scaled with ID for both movements. However, the data could not be fitted with a single regression: Movements over the long distance had a larger intercept corresponding to movement times about 140 ms longer than movements over the shorter distance. The magnitude of postural adjustments prior to movement initiation scaled with ID for both short and long distances. Our results provide strong support for the hypothesis that Fitts’ Law emerges at the level of motor planning, not at the level of corrections of ongoing movements. They show that, during natural movements, changes in movement distance may lead to changes in the relation between movement time and ID, for example when the contribution of different body segments to the movement varies and when the action of Coriolis force may require an additional correction of the movement trajectory. PMID:23211560
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…
Deriving the Regression Equation without Using Calculus
ERIC Educational Resources Information Center
Gordon, Sheldon P.; Gordon, Florence S.
2004-01-01
Probably the one "new" mathematical topic that is most responsible for modernizing courses in college algebra and precalculus over the last few years is the idea of fitting a function to a set of data in the sense of a least squares fit. Whether it be simple linear regression or nonlinear regression, this topic opens the door to applying the…
A Practical Guide to Regression Discontinuity
ERIC Educational Resources Information Center
Jacob, Robin; Zhu, Pei; Somers, Marie-Andrée; Bloom, Howard
2012-01-01
Regression discontinuity (RD) analysis is a rigorous nonexperimental approach that can be used to estimate program impacts in situations in which candidates are selected for treatment based on whether their value for a numeric rating exceeds a designated threshold or cut-point. Over the last two decades, the regression discontinuity approach has…
Dealing with Outliers: Robust, Resistant Regression
ERIC Educational Resources Information Center
Glasser, Leslie
2007-01-01
Least-squares linear regression is the best of statistics and it is the worst of statistics. The reasons for this paradoxical claim, arising from possible inapplicability of the method and the excessive influence of "outliers", are discussed and substitute regression methods based on median selection, which is both robust and resistant, are…
Cross-Validation, Shrinkage, and Multiple Regression.
ERIC Educational Resources Information Center
Hynes, Kevin
One aspect of multiple regression--the shrinkage of the multiple correlation coefficient on cross-validation is reviewed. The paper consists of four sections. In section one, the distinction between a fixed and a random multiple regression model is made explicit. In section two, the cross-validation paradigm and an explanation for the occurrence…
Incremental Net Effects in Multiple Regression
ERIC Educational Resources Information Center
Lipovetsky, Stan; Conklin, Michael
2005-01-01
A regular problem in regression analysis is estimating the comparative importance of the predictors in the model. This work considers the 'net effects', or shares of the predictors in the coefficient of the multiple determination, which is a widely used characteristic of the quality of a regression model. Estimation of the net effects can be a…
Illustration of Regression towards the Means
ERIC Educational Resources Information Center
Govindaraju, K.; Haslett, S. J.
2008-01-01
This article presents a procedure for generating a sequence of data sets which will yield exactly the same fitted simple linear regression equation y = a + bx. Unless rescaled, the generated data sets will have progressively smaller variability for the two variables, and the associated response and covariate will "regress" towards their…
Regression Analysis and the Sociological Imagination
ERIC Educational Resources Information Center
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
Three-Dimensional Modeling in Linear Regression.
ERIC Educational Resources Information Center
Herman, James D.
Linear regression examines the relationship between one or more independent (predictor) variables and a dependent variable. By using a particular formula, regression determines the weights needed to minimize the error term for a given set of predictors. With one predictor variable, the relationship between the predictor and the dependent variable…
Leukemia regression by vascular disruption and antiangiogenic therapy
Madlambayan, Gerard J.; Meacham, Amy M.; Hosaka, Koji; Mir, Saad; Jorgensen, Marda; Scott, Edward W.; Siemann, Dietmar W.
2010-01-01
Acute myelogenous leukemias (AMLs) and endothelial cells depend on each other for survival and proliferation. Monotherapy antivascular strategies such as targeting vascular endothelial growth factor (VEGF) has limited efficacy in treating AML. Thus, in search of a multitarget antivascular treatment strategy for AML, we tested a novel vascular disrupting agent, OXi4503, alone and in combination with the anti-VEGF antibody, bevacizumab. Using xenotransplant animal models, OXi4503 treatment of human AML chloromas led to vascular disruption in leukemia cores that displayed increased leukemia cell apoptosis. However, viable rims of leukemia cells remained and were richly vascular with increased VEGF-A expression. To target this peripheral reactive angiogenesis, bevacizumab was combined with OXi4503 and abrogated viable vascular rims, thereby leading to enhanced leukemia regression. In a systemic model of primary human AML, OXi4503 regressed leukemia engraftment alone and in combination with bevacizumab. Differences in blood vessel density alone could not account for the observed regression, suggesting that OXi4503 also exhibited direct cytotoxic effects on leukemia cells. In vitro analyses confirmed this targeted effect, which was mediated by the production of reactive oxygen species and resulted in apoptosis. Together, these data show that OXi4503 alone is capable of regressing AML by a multitargeted mechanism and that the addition of bevacizumab mitigates reactive angiogenesis. PMID:20472832
Nodule Regression in Adults With Nodular Gastritis
Kim, Ji Wan; Lee, Sun-Young; Kim, Jeong Hwan; Sung, In-Kyung; Park, Hyung Seok; Shim, Chan-Sup; Han, Hye Seung
2015-01-01
Background Nodular gastritis (NG) is associated with the presence of Helicobacter pylori infection, but there are controversies on nodule regression in adults. The aim of this study was to analyze the factors that are related to the nodule regression in adults diagnosed as NG. Methods Adult population who were diagnosed as NG with H. pylori infection during esophagogastroduodenoscopy (EGD) at our center were included. Changes in the size and location of the nodules, status of H. pylori infection, upper gastrointestinal (UGI) symptom, EGD and pathology findings were analyzed between the initial and follow-up tests. Results Of the 117 NG patients, 66.7% (12/18) of the eradicated NG patients showed nodule regression after H. pylori eradication, whereas 9.9% (9/99) of the non-eradicated NG patients showed spontaneous nodule regression without H. pylori eradication (P < 0.001). Nodule regression was more frequent in NG patients with antral nodule location (P = 0.010), small-sized nodules (P = 0.029), H. pylori eradication (P < 0.001), UGI symptom (P = 0.007), and a long-term follow-up period (P = 0.030). On the logistic regression analysis, nodule regression was inversely correlated with the persistent H. pylori infection on the follow-up test (odds ratio (OR): 0.020, 95% confidence interval (CI): 0.003 - 0.137, P < 0.001) and short-term follow-up period < 30.5 months (OR: 0.140, 95% CI: 0.028 - 0.700, P = 0.017). Conclusions In adults with NG, H. pylori eradication is the most significant factor associated with nodule regression. Long-term follow-up period is also correlated with nodule regression, but is less significant than H. pylori eradication. Our findings suggest that H. pylori eradication should be considered to promote nodule regression in NG patients with H. pylori infection.
NASA Astrophysics Data System (ADS)
Zhang, Ying; Bi, Peng; Hiller, Janet
2008-01-01
This is the first study to identify appropriate regression models for the association between climate variation and salmonellosis transmission. A comparison between different regression models was conducted using surveillance data in Adelaide, South Australia. By using notified salmonellosis cases and climatic variables from the Adelaide metropolitan area over the period 1990-2003, four regression methods were examined: standard Poisson regression, autoregressive adjusted Poisson regression, multiple linear regression, and a seasonal autoregressive integrated moving average (SARIMA) model. Notified salmonellosis cases in 2004 were used to test the forecasting ability of the four models. Parameter estimation, goodness-of-fit and forecasting ability of the four regression models were compared. Temperatures occurring 2 weeks prior to cases were positively associated with cases of salmonellosis. Rainfall was also inversely related to the number of cases. The comparison of the goodness-of-fit and forecasting ability suggest that the SARIMA model is better than the other three regression models. Temperature and rainfall may be used as climatic predictors of salmonellosis cases in regions with climatic characteristics similar to those of Adelaide. The SARIMA model could, thus, be adopted to quantify the relationship between climate variations and salmonellosis transmission.
Integrating Risk Adjustment and Enrollee Premiums in Health Plan Payment
McGuire, Thomas G.; Glazer, Jacob; Newhouse, Joseph P.; Normand, Sharon-Lise; Shi, Julie; Sinaiko, Anna D.; Zuvekas, Samuel
2013-01-01
In two important health policy contexts – private plans in Medicare and the new state-run “Exchanges” created as part of the Affordable Care Act (ACA) – plan payments come from two sources: risk-adjusted payments from a Regulator and premiums charged to individual enrollees. This paper derives principles for integrating risk-adjusted payments and premium policy in individual health insurance markets based on fitting total plan payments to health plan costs per person as closely as possible. A least squares regression including both health status and variables used in premiums reveals the weights a Regulator should put on risk adjusters when markets determine premiums. We apply the methods to an Exchange-eligible population drawn from the Medical Expenditure Panel Survey (MEPS). PMID:24308878
2014-01-01
Background Risk adjustment is crucial for comparison of outcome in medical care. Knowledge of the external factors that impact measured outcome but that cannot be influenced by the physician is a prerequisite for this adjustment. To date, a universal and reproducible method for identification of the relevant external factors has not been published. The selection of external factors in current quality assurance programmes is mainly based on expert opinion. We propose and demonstrate a methodology for identification of external factors requiring risk adjustment of outcome indicators and we apply it to a cataract surgery register. Methods Defined test criteria to determine the relevance for risk adjustment are “clinical relevance” and “statistical significance”. Clinical relevance of the association is presumed when observed success rates of the indicator in the presence and absence of the external factor exceed a pre-specified range of 10%. Statistical significance of the association between the external factor and outcome indicators is assessed by univariate stratification and multivariate logistic regression adjustment. The cataract surgery register was set up as part of a German multi-centre register trial for out-patient cataract surgery in three high-volume surgical sites. A total of 14,924 patient follow-ups have been documented since 2005. Eight external factors potentially relevant for risk adjustment were related to the outcome indicators “refractive accuracy” and “visual rehabilitation” 2–5 weeks after surgery. Results The clinical relevance criterion confirmed 2 (“refractive accuracy”) and 5 (“visual rehabilitation”) external factors. The significance criterion was verified in two ways. Univariate and multivariate analyses revealed almost identical external factors: 4 were related to “refractive accuracy” and 7 (6) to “visual rehabilitation”. Two (“refractive accuracy”) and 5 (“visual rehabilitation”) factors
ERIC Educational Resources Information Center
Shafiq, M. Najeeb
2013-01-01
Using quantile regression analyses, this study examines gender gaps in mathematics, science, and reading in Azerbaijan, Indonesia, Jordan, the Kyrgyz Republic, Qatar, Tunisia, and Turkey among 15-year-old students. The analyses show that girls in Azerbaijan achieve as well as boys in mathematics and science and overachieve in reading. In Jordan,…
Jenkins, T G; Leymaster, K A; MacNeil, M D
1995-12-01
Regression equations to predict kilograms of fat-free soft tissue (the sum of water and protein from chemical analyses) were developed from data collected on 526 steers and heifers. Straightbred animals representing Angus, Braunvieh, Charolais, Gelbvieh, Hereford, Limousin, Pinzgauer, Red Poll, and Simmental breeds of cattle contributed to the data set. Cattle ranged in slaughter weight and age from approximately 350 to 575 kg and from 13 to 23 mo, respectively. Diets (100% ground alfalfa, 67% ground alfalfa and 33% ground corn or 33% ground alfalfa and 67% ground corn) were cross-classified with breed and sex. Estimative traits included in the equation were warm carcass weight, fat depth at the 12th rib, and body impedance. Carcass soft-tissue samples were taken for determination of chemical constituents. The prediction equation accounted for 94% of the variation in fat-free soft tissue of the carcass. Adjusting for breed-sex-diet contemporary groups increased the R2 value by 2% units. The prediction model was evaluated using data collected on 65 steers sired by Charolais or by Hereford bulls at the Ft Keogh Livestock and Range Research Laboratory (Miles City, MT). Postweaning feeding strategies and slaughter ages varied among these animals. Carcass weight, back fat depth, and resistive impedance measures were recorded. Carcass soft-tissue samples were taken for determination of chemical constituents. Values of estimator variables recorded at Ft. Keogh were used in the regression equation to predict fat-free soft tissue for each animal. The values for kilogram of fat-free soft tissue determined from chemical analysis were regressed on predicted fat-free soft tissue. the results indicate that fat-free soft tissue of carcasses can be accurately predicted using estimative traits that do not diminish carcass value. PMID:8655437
Jenkins, T G; Leymaster, K A; MacNeil, M D
1995-12-01
Regression equations to predict kilograms of fat-free soft tissue (the sum of water and protein from chemical analyses) were developed from data collected on 526 steers and heifers. Straightbred animals representing Angus, Braunvieh, Charolais, Gelbvieh, Hereford, Limousin, Pinzgauer, Red Poll, and Simmental breeds of cattle contributed to the data set. Cattle ranged in slaughter weight and age from approximately 350 to 575 kg and from 13 to 23 mo, respectively. Diets (100% ground alfalfa, 67% ground alfalfa and 33% ground corn or 33% ground alfalfa and 67% ground corn) were cross-classified with breed and sex. Estimative traits included in the equation were warm carcass weight, fat depth at the 12th rib, and body impedance. Carcass soft-tissue samples were taken for determination of chemical constituents. The prediction equation accounted for 94% of the variation in fat-free soft tissue of the carcass. Adjusting for breed-sex-diet contemporary groups increased the R2 value by 2% units. The prediction model was evaluated using data collected on 65 steers sired by Charolais or by Hereford bulls at the Ft Keogh Livestock and Range Research Laboratory (Miles City, MT). Postweaning feeding strategies and slaughter ages varied among these animals. Carcass weight, back fat depth, and resistive impedance measures were recorded. Carcass soft-tissue samples were taken for determination of chemical constituents. Values of estimator variables recorded at Ft. Keogh were used in the regression equation to predict fat-free soft tissue for each animal. The values for kilogram of fat-free soft tissue determined from chemical analysis were regressed on predicted fat-free soft tissue. the results indicate that fat-free soft tissue of carcasses can be accurately predicted using estimative traits that do not diminish carcass value.
Adjustable Induction-Heating Coil
NASA Technical Reports Server (NTRS)
Ellis, Rod; Bartolotta, Paul
1990-01-01
Improved design for induction-heating work coil facilitates optimization of heating in different metal specimens. Three segments adjusted independently to obtain desired distribution of temperature. Reduces time needed to achieve required temperature profiles.
Time-adjusted variable resistor
NASA Technical Reports Server (NTRS)
Heyser, R. C.
1972-01-01
Timing mechanism was developed effecting extremely precisioned highly resistant fixed resistor. Switches shunt all or portion of resistor; effective resistance is varied over time interval by adjusting switch closure rate.
Federal Register 2010, 2011, 2012, 2013, 2014
2013-10-22
... noticing a recent Postal Service filing seeking postal rate adjustments based on exigent circumstances...,'' is ``premised on the recent recession as an exigent event.'' Id. at 1, 2. In Order No. 1059,...
Enticott, Joanne C; Cheng, I-Hao; Russell, Grant; Szwarc, Josef; Braitberg, George; Peek, Anne; Meadows, Graham
2015-01-01
This study investigated if people born in refugee source countries are disproportionately represented among those receiving a diagnosis of mental illness within emergency departments (EDs). The setting was the Cities of Greater Dandenong and Casey, the resettlement region for one-twelfth of Australia's refugees. An epidemiological, secondary data analysis compared mental illness diagnoses received in EDs by refugee and non-refugee populations. Data was the Victorian Emergency Minimum Dataset in the 2008-09 financial year. Univariate and multivariate logistic regression created predictive models for mental illness using five variables: age, sex, refugee background, interpreter use and preferred language. Collinearity, model fit and model stability were examined. Multivariate analysis showed age and sex to be the only significant risk factors for mental illness diagnosis in EDs. 'Refugee status', 'interpreter use' and 'preferred language' were not associatedwith a mental health diagnosis following risk adjustment forthe effects ofage and sex. The disappearance ofthe univariate association after adjustment for age and sex is a salutary lesson for Medicare Locals and other health planners regarding the importance of adjusting analyses of health service data for demographic characteristics.
Enticott, Joanne C; Cheng, I-Hao; Russell, Grant; Szwarc, Josef; Braitberg, George; Peek, Anne; Meadows, Graham
2015-01-01
This study investigated if people born in refugee source countries are disproportionately represented among those receiving a diagnosis of mental illness within emergency departments (EDs). The setting was the Cities of Greater Dandenong and Casey, the resettlement region for one-twelfth of Australia's refugees. An epidemiological, secondary data analysis compared mental illness diagnoses received in EDs by refugee and non-refugee populations. Data was the Victorian Emergency Minimum Dataset in the 2008-09 financial year. Univariate and multivariate logistic regression created predictive models for mental illness using five variables: age, sex, refugee background, interpreter use and preferred language. Collinearity, model fit and model stability were examined. Multivariate analysis showed age and sex to be the only significant risk factors for mental illness diagnosis in EDs. 'Refugee status', 'interpreter use' and 'preferred language' were not associatedwith a mental health diagnosis following risk adjustment forthe effects ofage and sex. The disappearance ofthe univariate association after adjustment for age and sex is a salutary lesson for Medicare Locals and other health planners regarding the importance of adjusting analyses of health service data for demographic characteristics. PMID:24922047
Regression modeling of ground-water flow
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)
Jiang, Honghua; Kulkarni, Pandurang M; Mallinckrodt, Craig H; Shurzinske, Linda; Molenberghs, Geert; Lipkovich, Ilya
2015-01-01
The benefits of adjusting for baseline covariates are not as straightforward with repeated binary responses as with continuous response variables. Therefore, in this study, we compared different methods for analyzing repeated binary data through simulations when the outcome at the study endpoint is of interest. Methods compared included chi-square, Fisher's exact test, covariate adjusted/unadjusted logistic regression (Adj.logit/Unadj.logit), covariate adjusted/unadjusted generalized estimating equations (Adj.GEE/Unadj.GEE), covariate adjusted/unadjusted generalized linear mixed model (Adj.GLMM/Unadj.GLMM). All these methods preserved the type I error close to the nominal level. Covariate adjusted methods improved power compared with the unadjusted methods because of the increased treatment effect estimates, especially when the correlation between the baseline and outcome was strong, even though there was an apparent increase in standard errors. Results of the Chi-squared test were identical to those for the unadjusted logistic regression. Fisher's exact test was the most conservative test regarding the type I error rate and also with the lowest power. Without missing data, there was no gain in using a repeated measures approach over a simple logistic regression at the final time point. Analysis of results from five phase III diabetes trials of the same compound was consistent with the simulation findings. Therefore, covariate adjusted analysis is recommended for repeated binary data when the study endpoint is of interest. PMID:25866149
Case-mix adjustment for evaluation of mortality in cystic fibrosis.
O'Connor, Gerald T; Quinton, Hebe B; Kahn, Richard; Robichaud, Priscilla; Maddock, Joanne; Lever, Thomas; Detzer, Mark; Brooks, John G
2002-02-01
Comparison of patient mortality rates in cystic fibrosis (CF) obtained from different institutions requires the use of case-mix adjustment methods to account for baseline differences in patient and disease characteristics. There is no current professional consensus on the use of case-mix adjustment methods for use in comparing mortality rates in CF. Characteristics used for this case-mix adjustment should include those that are different across institutions and are associated with patient survival. They should not include characteristics of disease severity that may be a consequence of effectiveness of treatment. The goal of these analyses was to identify a set of these characteristics of patients or disease that would be useful for case-mix adjustment of CF mortality rates. Data from the Cystic Fibrosis Foundation Patient Registry and from the United States Census of the Population (1990) were used in these analyses. Kaplan-Meier techniques, the log-rank test, and Cox proportional hazards regression were used to estimate survivorship, calculate hazard ratios (HR), 95% confidence intervals (CI(95%)), and to conduct tests of statistical significance. The data set included all 30,469 CF patients seen at CF Care Centers from 1982-1998. There were 5,906 deaths during 508,721 person-years of follow-up. In multivariate analyses, female gender (HR 1.30, CI(95%) (1.16, 1,47), P < 0.001), nonwhite race (HR 1.48, CI(95%) (1.07, 2.04), P = 0.018), Hispanic ethnicity (HR 1.85, CI(95%) (1.42, 2.43), P < 0.001), and symptomatic presentation (respiratory, gastrointestinal, respiratory and gastrointestinal, meconium ileus, and other symptomatic presentations; HRs 1.38-1.83; P values, 0.028 to < 0.001) were associated with higher risk of death. The homozygous Delta F508 genotype (HR 1.36, CI(95%) (1.19, 1.55), P < 0.001) and neither mutation being Delta F508 (HR 1.40, CI(95%) (1.15, 1.71), P = 0.001) were also associated with higher risk of death. Patients diagnosed after 36 months
Technology Transfer Automated Retrieval System (TEKTRAN)
In precision agriculture regression has been used widely to quality the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually makes the regression model suboptimal. In this study, a regression-kriging method was attemp...
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.
Regression of altitude-produced cardiac hypertrophy.
NASA Technical Reports Server (NTRS)
Sizemore, D. A.; Mcintyre, T. W.; Van Liere, E. J.; Wilson , M. F.
1973-01-01
The rate of regression of cardiac hypertrophy with time has been determined in adult male albino rats. The hypertrophy was induced by intermittent exposure to simulated high altitude. The percentage hypertrophy was much greater (46%) in the right ventricle than in the left (16%). The regression could be adequately fitted to a single exponential function with a half-time of 6.73 plus or minus 0.71 days (90% CI). There was no significant difference in the rates of regression for the two ventricles.
Adjustment of directly measured adipose tissue volume in infants
Gale, C; Santhakumaran, S; Wells, J C K; Modi, N
2014-01-01
Background: Direct measurement of adipose tissue (AT) using magnetic resonance imaging is increasingly used to characterise infant body composition. Optimal techniques for adjusting direct measures of infant AT remain to be determined. Objectives: To explore the relationships between body size and direct measures of total and regional AT, the relationship between AT depots representing the metabolic load of adiposity and to determine optimal methods of adjusting adiposity in early life. Design: Analysis of regional AT volume (ATV) measured using magnetic resonance imaging in longitudinal and cross-sectional studies. Subjects: Healthy term infants; 244 in the first month (1–31 days), 72 in early infancy (42–91 days). Methods: The statistical validity of commonly used indices adjusting adiposity for body size was examined. Valid indices, defined as mathematical independence of the index from its denominator, to adjust ATV for body size and metabolic load of adiposity were determined using log-log regression analysis. Results: Indices commonly used to adjust ATV are significantly correlated with body size. Most regional AT depots are optimally adjusted using the index ATV/(height)3 in the first month and ATV/(height)2 in early infancy. Using these indices, height accounts for<2% of the variation in the index for almost all AT depots. Internal abdominal (IA) ATV was optimally adjusted for subcutaneous abdominal (SCA) ATV by calculating IA/SCA0.6. Conclusions: Statistically optimal indices for adjusting directly measured ATV for body size are ATV/height3 in the neonatal period and ATV/height2 in early infancy. The ratio IA/SCA ATV remains significantly correlated with SCA in both the neonatal period and early infancy; the index IA/SCA0.6 is statistically optimal at both of these ages. PMID:24662695
ERIC Educational Resources Information Center
Gilstrap, Donald L.
2013-01-01
In addition to qualitative methods presented in chaos and complexity theories in educational research, this article addresses quantitative methods that may show potential for future research studies. Although much in the social and behavioral sciences literature has focused on computer simulations, this article explores current chaos and…
ERIC Educational Resources Information Center
Gramlich, Stephen Peter
2010-01-01
Open door admissions at community colleges bring returning adults, first timers, low achievers, disabled persons, and immigrants. Passing and retention rates for remedial and non-developmental math courses can be comparatively inadequate (LAVC, 2005; CCPRDC, 2000; SBCC, 2004; Seybert & Soltz, 1992; Waycaster, 2002). Mathematics achievement…
Xiao, Yongling; Abrahamowicz, Michal
2010-03-30
We propose two bootstrap-based methods to correct the standard errors (SEs) from Cox's model for within-cluster correlation of right-censored event times. The cluster-bootstrap method resamples, with replacement, only the clusters, whereas the two-step bootstrap method resamples (i) the clusters, and (ii) individuals within each selected cluster, with replacement. In simulations, we evaluate both methods and compare them with the existing robust variance estimator and the shared gamma frailty model, which are available in statistical software packages. We simulate clustered event time data, with latent cluster-level random effects, which are ignored in the conventional Cox's model. For cluster-level covariates, both proposed bootstrap methods yield accurate SEs, and type I error rates, and acceptable coverage rates, regardless of the true random effects distribution, and avoid serious variance under-estimation by conventional Cox-based standard errors. However, the two-step bootstrap method over-estimates the variance for individual-level covariates. We also apply the proposed bootstrap methods to obtain confidence bands around flexible estimates of time-dependent effects in a real-life analysis of cluster event times.
REGRESSION ESTIMATES FOR TOPOLOGICAL-HYDROGRAPH INPUT.
Karlinger, Michael R.; Guertin, D. Phillip; Troutman, Brent M.
1988-01-01
Physiographic, hydrologic, and rainfall data from 18 small drainage basins in semiarid, central Wyoming were used to calibrate topological, unit-hydrograph models for celerity, the average rate of travel of a flood wave through the basin. The data set consisted of basin characteristics and hydrologic data for the 18 basins and rainfall data for 68 storms. Calibrated values of celerity and peak discharges subsequently were regressed as a function of the basin characteristics and excess rainfall volume. Predicted values obtained in this way can be used as input for estimating hydrographs in ungaged basins. The regression models included ordinary least-squares and seemingly unrelated regression. This latter regression model jointly estimated the celerity and peak discharge.
TWSVR: Regression via Twin Support Vector Machine.
Khemchandani, Reshma; Goyal, Keshav; Chandra, Suresh
2016-02-01
Taking motivation from Twin Support Vector Machine (TWSVM) formulation, Peng (2010) attempted to propose Twin Support Vector Regression (TSVR) where the regressor is obtained via solving a pair of quadratic programming problems (QPPs). In this paper we argue that TSVR formulation is not in the true spirit of TWSVM. Further, taking motivation from Bi and Bennett (2003), we propose an alternative approach to find a formulation for Twin Support Vector Regression (TWSVR) which is in the true spirit of TWSVM. We show that our proposed TWSVR can be derived from TWSVM for an appropriately constructed classification problem. To check the efficacy of our proposed TWSVR we compare its performance with TSVR and classical Support Vector Regression(SVR) on various regression datasets.
TWSVR: Regression via Twin Support Vector Machine.
Khemchandani, Reshma; Goyal, Keshav; Chandra, Suresh
2016-02-01
Taking motivation from Twin Support Vector Machine (TWSVM) formulation, Peng (2010) attempted to propose Twin Support Vector Regression (TSVR) where the regressor is obtained via solving a pair of quadratic programming problems (QPPs). In this paper we argue that TSVR formulation is not in the true spirit of TWSVM. Further, taking motivation from Bi and Bennett (2003), we propose an alternative approach to find a formulation for Twin Support Vector Regression (TWSVR) which is in the true spirit of TWSVM. We show that our proposed TWSVR can be derived from TWSVM for an appropriately constructed classification problem. To check the efficacy of our proposed TWSVR we compare its performance with TSVR and classical Support Vector Regression(SVR) on various regression datasets. PMID:26624223
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)
Kleinman, Ken; Gillman, Matthew W.
2014-01-01
We implemented 6 confounding adjustment methods: 1) covariate-adjusted regression, 2) propensity score (PS) regression, 3) PS stratification, 4) PS matching with two calipers, 5) inverse-probability-weighting, and 6) doubly-robust estimation to examine the associations between the BMI z-score at 3 years and two separate dichotomous exposure measures: exclusive breastfeeding versus formula only (N = 437) and cesarean section versus vaginal delivery (N = 1236). Data were drawn from a prospective pre-birth cohort study, Project Viva. The goal is to demonstrate the necessity and usefulness, and approaches for multiple confounding adjustment methods to analyze observational data. Unadjusted (univariate) and covariate-adjusted linear regression associations of breastfeeding with BMI z-score were −0.33 (95% CI −0.53, −0.13) and −0.24 (−0.46, −0.02), respectively. The other approaches resulted in smaller N (204 to 276) because of poor overlap of covariates, but CIs were of similar width except for inverse-probability-weighting (75% wider) and PS matching with a wider caliper (76% wider). Point estimates ranged widely, however, from −0.01 to −0.38. For cesarean section, because of better covariate overlap, the covariate-adjusted regression estimate (0.20) was remarkably robust to all adjustment methods, and the widths of the 95% CIs differed less than in the breastfeeding example. Choice of covariate adjustment method can matter. Lack of overlap in covariate structure between exposed and unexposed participants in observational studies can lead to erroneous covariate-adjusted estimates and confidence intervals. We recommend inspecting covariate overlap and using multiple confounding adjustment methods. Similar results bring reassurance. Contradictory results suggest issues with either the data or the analytic method. PMID:25171142
7 CFR 251.7 - Formula adjustments.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 7 Agriculture 4 2010-01-01 2010-01-01 false Formula adjustments. 251.7 Section 251.7 Agriculture... GENERAL REGULATIONS AND POLICIES-FOOD DISTRIBUTION THE EMERGENCY FOOD ASSISTANCE PROGRAM § 251.7 Formula adjustments. Formula adjustments. (a) Commodity adjustments. The Department will make annual adjustments...
12 CFR 1209.80 - Inflation adjustments.
Code of Federal Regulations, 2014 CFR
2014-01-01
... 12 Banks and Banking 10 2014-01-01 2014-01-01 false Inflation adjustments. 1209.80 Section 1209.80... PROCEDURE Civil Money Penalty Inflation Adjustments § 1209.80 Inflation adjustments. The maximum amount of... thereafter adjusted in accordance with the Inflation Adjustment Act, on a recurring four-year cycle, is...
12 CFR 1209.80 - Inflation adjustments.
Code of Federal Regulations, 2012 CFR
2012-01-01
... 12 Banks and Banking 9 2012-01-01 2012-01-01 false Inflation adjustments. 1209.80 Section 1209.80... PROCEDURE Civil Money Penalty Inflation Adjustments § 1209.80 Inflation adjustments. The maximum amount of... thereafter adjusted in accordance with the Inflation Adjustment Act, on a recurring four-year cycle, is...
12 CFR 1209.80 - Inflation adjustments.
Code of Federal Regulations, 2013 CFR
2013-01-01
... 12 Banks and Banking 9 2013-01-01 2013-01-01 false Inflation adjustments. 1209.80 Section 1209.80... PROCEDURE Civil Money Penalty Inflation Adjustments § 1209.80 Inflation adjustments. The maximum amount of... thereafter adjusted in accordance with the Inflation Adjustment Act, on a recurring four-year cycle, is...
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 enhancement cannot…
There is No Quantum Regression Theorem
Ford, G.W.; OConnell, R.F.
1996-07-01
The Onsager regression hypothesis states that the regression of fluctuations is governed by macroscopic equations describing the approach to equilibrium. It is here asserted that this hypothesis fails in the quantum case. This is shown first by explicit calculation for the example of quantum Brownian motion of an oscillator and then in general from the fluctuation-dissipation theorem. It is asserted that the correct generalization of the Onsager hypothesis is the fluctuation-dissipation theorem. {copyright} {ital 1996 The American Physical Society.}
Wanninkhof, R.
2003-05-21
As part of the global synthesis effort sponsored by the Global Carbon Cycle project of the National Oceanic and Atmospheric Administration (NOAA) and U.S. Department of Energy, a comprehensive comparison was performed of inorganic carbon parameters measured on oceanographic surveys carried out under auspices of the Joint Global Ocean Flux Study and related programs. Many of the cruises were performed as part of the World Hydrographic Program of the World Ocean Circulation Experiment and the NOAA Ocean-Atmosphere Carbon Exchange Study. Total dissolved inorganic carbon (DIC), total alkalinity (TAlk), fugacity of CO{sub 2}, and pH data from twenty-three cruises were checked to determine whether there were systematic offsets of these parameters between cruises. The focus was on the DIC and TAlk state variables. Data quality and offsets of DIC and TAlk were determined by using several different techniques. One approach was based on crossover analyses, where the deep-water concentrations of DIC and TAlk were compared for stations on different cruises that were within 100 km of each other. Regional comparisons were also made by using a multiple-parameter linear regression technique in which DIC or TAlk was regressed against hydrographic and nutrient parameters. When offsets of greater than 4 {micro}mol/kg were observed for DIC and/or 6 {micro}mol/kg were observed for TAlk, the data taken on the cruise were closely scrutinized to determine whether the offsets were systematic. Based on these analyses, the DIC data and TAlk data of three cruises were deemed of insufficient quality to be included in the comprehensive basinwide data set. For several of the cruises, small adjustments in TAlk were recommended for consistency with other cruises in the region. After these adjustments were incorporated, the inorganic carbon data from all cruises along with hydrographic, chlorofluorocarbon, and nutrient data were combined as a research quality product for the scientific community.
Do Afterlife Beliefs Affect Psychological Adjustment to Late-Life Spousal Loss?
2014-01-01
Objectives. We explore whether beliefs about the existence and nature of an afterlife affect 5 psychological symptoms (anxiety, anger, depression, intrusive thoughts, and yearning) among recently bereaved older spouses. Method. We conduct multivariate regression analyses using data from the Changing Lives of Older Couples (CLOC), a prospective study of spousal loss. The CLOC obtained data from bereaved persons prior to loss and both 6 and 18 months postloss. All analyses are adjusted for health, sociodemographic characteristics, and preloss marital quality. Results. Bleak or uncertain views about the afterlife are associated with multiple aspects of distress postloss. Uncertainty about the existence of an afterlife is associated with elevated intrusive thoughts, a symptom similar to posttraumatic distress. Widowed persons who do not expect to be reunited with loved ones in the afterlife report significantly more depressive symptoms, anger, and intrusive thoughts at both 6 and 18 months postloss. Discussion. Beliefs in an afterlife may be maladaptive for coping with late-life spousal loss, particularly if one is uncertain about its existence or holds a pessimistic view of what the afterlife entails. Our findings are broadly consistent with recent work suggesting that “continuing bonds” with the decedent may not be adaptive for older bereaved spouses. PMID:23811692
Lester, Rosemary A; Story, Brad H
2015-08-01
The purpose of this study was to determine if adjustments to the voice source [i.e., fundamental frequency (F0), degree of vocal fold adduction] or vocal tract filter (i.e., vocal tract shape for vowels) reduce the perception of simulated laryngeal vocal tremor and to determine if listener perception could be explained by characteristics of the acoustical modulations. This research was carried out using a computational model of speech production that allowed for precise control and manipulation of the glottal and vocal tract configurations. Forty-two healthy adults participated in a perceptual study involving pair-comparisons of the magnitude of "shakiness" with simulated samples of laryngeal vocal tremor. Results revealed that listeners perceived a higher magnitude of voice modulation when simulated samples had a higher mean F0, greater degree of vocal fold adduction, and vocal tract shape for /i/ vs /ɑ/. However, the effect of F0 was significant only when glottal noise was not present in the acoustic signal. Acoustical analyses were performed with the simulated samples to determine the features that affected listeners' judgments. Based on regression analyses, listeners' judgments were predicted to some extent by modulation information present in both low and high frequency bands. PMID:26328711
Lester, Rosemary A.; Story, Brad H.
2015-01-01
The purpose of this study was to determine if adjustments to the voice source [i.e., fundamental frequency (F0), degree of vocal fold adduction] or vocal tract filter (i.e., vocal tract shape for vowels) reduce the perception of simulated laryngeal vocal tremor and to determine if listener perception could be explained by characteristics of the acoustical modulations. This research was carried out using a computational model of speech production that allowed for precise control and manipulation of the glottal and vocal tract configurations. Forty-two healthy adults participated in a perceptual study involving pair-comparisons of the magnitude of “shakiness” with simulated samples of laryngeal vocal tremor. Results revealed that listeners perceived a higher magnitude of voice modulation when simulated samples had a higher mean F0, greater degree of vocal fold adduction, and vocal tract shape for /i/ vs /ɑ/. However, the effect of F0 was significant only when glottal noise was not present in the acoustic signal. Acoustical analyses were performed with the simulated samples to determine the features that affected listeners' judgments. Based on regression analyses, listeners' judgments were predicted to some extent by modulation information present in both low and high frequency bands. PMID:26328711
Synthesizing regression results: a factored likelihood method.
Wu, Meng-Jia; Becker, Betsy Jane
2013-06-01
Regression methods are widely used by researchers in many fields, yet methods for synthesizing regression results are scarce. This study proposes using a factored likelihood method, originally developed to handle missing data, to appropriately synthesize regression models involving different predictors. This method uses the correlations reported in the regression studies to calculate synthesized standardized slopes. It uses available correlations to estimate missing ones through a series of regressions, allowing us to synthesize correlations among variables as if each included study contained all the same variables. Great accuracy and stability of this method under fixed-effects models were found through Monte Carlo simulation. An example was provided to demonstrate the steps for calculating the synthesized slopes through sweep operators. By rearranging the predictors in the included regression models or omitting a relatively small number of correlations from those models, we can easily apply the factored likelihood method to many situations involving synthesis of linear models. Limitations and other possible methods for synthesizing more complicated models are discussed. Copyright © 2012 John Wiley & Sons, Ltd. PMID:26053653
Post-processing through linear regression
NASA Astrophysics Data System (ADS)
van Schaeybroeck, B.; Vannitsem, S.
2011-03-01
Various post-processing techniques are compared for both deterministic and ensemble forecasts, all based on linear regression between forecast data and observations. In order to evaluate the quality of the regression methods, three criteria are proposed, related to the effective correction of forecast error, the optimal variability of the corrected forecast and multicollinearity. The regression schemes under consideration include the ordinary least-square (OLS) method, a new time-dependent Tikhonov regularization (TDTR) method, the total least-square method, a new geometric-mean regression (GM), a recently introduced error-in-variables (EVMOS) method and, finally, a "best member" OLS method. The advantages and drawbacks of each method are clarified. These techniques are applied in the context of the 63 Lorenz system, whose model version is affected by both initial condition and model errors. For short forecast lead times, the number and choice of predictors plays an important role. Contrarily to the other techniques, GM degrades when the number of predictors increases. At intermediate lead times, linear regression is unable to provide corrections to the forecast and can sometimes degrade the performance (GM and the best member OLS with noise). At long lead times the regression schemes (EVMOS, TDTR) which yield the correct variability and the largest correlation between ensemble error and spread, should be preferred.
Dynamic Adjustment of Stimuli in Real Time Functional Magnetic Resonance Imaging
Feng, I. Jung; Jack, Anthony I.; Tatsuoka, Curtis
2015-01-01
The conventional fMRI image analysis approach to associating stimuli to brain activation is performed by carrying out a massive number of parallel univariate regression analyses. fMRI blood-oxygen-level dependent (BOLD) signal, the basis of these analyses, is known for its low signal-noise-ratio and high spatial and temporal signal correlation. In order to ensure accurate localization of brain activity, stimulus administration in an fMRI session is often lengthy and repetitive. Real-time fMRI BOLD signal analysis is carried out as the signal is observed. This method allows for dynamic, real-time adjustment of stimuli through sequential experimental designs. We have developed a voxel-wise sequential probability ratio test (SPRT) approach for dynamically determining localization, as well as decision rules for stopping stimulus administration. SPRT methods and general linear model (GLM) approaches are combined to identify brain regions that are activated by specific elements of stimuli. Stimulus administration is dynamically stopped when sufficient statistical evidence is collected to determine activation status across regions of interest, following predetermined statistical error thresholds. Simulation experiments and an example based on real fMRI data show that scan volumes can be substantially reduced when compared with pre-determined, fixed designs while achieving similar or better accuracy in detecting activated voxels. Moreover, the proposed approach is also able to accurately detect differentially activated areas, and other comparisons between task-related GLM parameters that can be formulated in a hypothesis-testing framework. Finally, we give a demonstration of SPRT being employed in conjunction with a halving algorithm to dynamically adjust stimuli. PMID:25785856
Interpret with caution: multicollinearity in multiple regression of cognitive data.
Morrison, Catriona M
2003-08-01
Shibihara and Kondo in 2002 reported a reanalysis of the 1997 Kanji picture-naming data of Yamazaki, Ellis, Morrison, and Lambon-Ralph in which independent variables were highly correlated. Their addition of the variable visual familiarity altered the previously reported pattern of results, indicating that visual familiarity, but not age of acquisition, was important in predicting Kanji naming speed. The present paper argues that caution should be taken when drawing conclusions from multiple regression analyses in which the independent variables are so highly correlated, as such multicollinearity can lead to unreliable output.
Progression-regression effects in tracking repeated patterns
NASA Technical Reports Server (NTRS)
Jagacinski, R. J.; Hah, S.
1986-01-01
Subjects used a position control system to perform compensatory tracking of a repeated input pattern. The input pattern was 20 seconds in duration and was either an arctangent function or the sum of two sine waves. Tracking error decreased with practice and increased with the addition of a concurrent memory task. The shape of the ensemble-average tracking error resembled the shape of the input velocity signal throughout these changes in performance. Regression analyses were used to parameterize these effects and compare these results with the predictions of several conceptualizations of perceptual-motor learning.
50 CFR 622.281 - Adjustment of management measures.
Code of Federal Regulations, 2014 CFR
2014-10-01
... ATLANTIC Dolphin and Wahoo Fishery Off the Atlantic States § 622.281 Adjustment of management measures. In accordance with the framework procedures of the FMP for the Dolphin and Wahoo Fishery off the Atlantic States... Atlantic dolphin and wahoo. (a) Atlantic dolphin and wahoo. Biomass levels, age-structured analyses,...
50 CFR 622.281 - Adjustment of management measures.
Code of Federal Regulations, 2013 CFR
2013-10-01
... ATLANTIC Dolphin and Wahoo Fishery Off the Atlantic States § 622.281 Adjustment of management measures. In accordance with the framework procedures of the FMP for the Dolphin and Wahoo Fishery off the Atlantic States... Atlantic dolphin and wahoo. (a) Atlantic dolphin and wahoo. Biomass levels, age-structured analyses,...
50 CFR 622.210 - Adjustment of management measures.
Code of Federal Regulations, 2014 CFR
2014-10-01
... ATLANTIC Shrimp Fishery of the South Atlantic Region § 622.210 Adjustment of management measures. In accordance with the framework procedures of the FMP for the Shrimp Fishery of the South Atlantic Region, the... shrimp. (a) Biomass levels, age-structured analyses, BRD certification criteria, BRD specifications,...
50 CFR 622.210 - Adjustment of management measures.
Code of Federal Regulations, 2013 CFR
2013-10-01
... ATLANTIC Shrimp Fishery of the South Atlantic Region § 622.210 Adjustment of management measures. In accordance with the framework procedures of the FMP for the Shrimp Fishery of the South Atlantic Region, the... shrimp. (a) Biomass levels, age-structured analyses, BRD certification criteria, BRD specifications,...
Chung, Sang M; Lee, David J; Hand, Austin; Young, Philip; Vaidyanathan, Jayabharathi; Sahajwalla, Chandrahas
2015-12-01
The study evaluated whether the renal function decline rate per year with age in adults varies based on two primary statistical analyses: cross-section (CS), using one observation per subject, and longitudinal (LT), using multiple observations per subject over time. A total of 16628 records (3946 subjects; age range 30-92 years) of creatinine clearance and relevant demographic data were used. On average, four samples per subject were collected for up to 2364 days (mean: 793 days). A simple linear regression and random coefficient models were selected for CS and LT analyses, respectively. The renal function decline rates per year were 1.33 and 0.95 ml/min/year for CS and LT analyses, respectively, and were slower when the repeated individual measurements were considered. The study confirms that rates are different based on statistical analyses, and that a statistically robust longitudinal model with a proper sampling design provides reliable individual as well as population estimates of the renal function decline rates per year with age in adults. In conclusion, our findings indicated that one should be cautious in interpreting the renal function decline rate with aging information because its estimation was highly dependent on the statistical analyses. From our analyses, a population longitudinal analysis (e.g. random coefficient model) is recommended if individualization is critical, such as a dose adjustment based on renal function during a chronic therapy.
Poisson Regression Analysis of Illness and Injury Surveillance Data
Frome E.L., Watkins J.P., Ellis E.D.
2012-12-12
The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences due to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra
Time course for tail regression during metamorphosis of the ascidian Ciona intestinalis.
Matsunobu, Shohei; Sasakura, Yasunori
2015-09-01
In most ascidians, the tadpole-like swimming larvae dramatically change their body-plans during metamorphosis and develop into sessile adults. The mechanisms of ascidian metamorphosis have been researched and debated for many years. Until now information on the detailed time course of the initiation and completion of each metamorphic event has not been described. One dramatic and important event in ascidian metamorphosis is tail regression, in which ascidian larvae lose their tails to adjust themselves to sessile life. In the present study, we measured the time associated with tail regression in the ascidian Ciona intestinalis. Larvae are thought to acquire competency for each metamorphic event in certain developmental periods. We show that the timing with which the competence for tail regression is acquired is determined by the time since hatching, and this timing is not affected by the timing of post-hatching events such as adhesion. Because larvae need to adhere to substrates with their papillae to induce tail regression, we measured the duration for which larvae need to remain adhered in order to initiate tail regression and the time needed for the tail to regress. Larvae acquire the ability to adhere to substrates before they acquire tail regression competence. We found that when larvae adhered before they acquired tail regression competence, they were able to remember the experience of adhesion until they acquired the ability to undergo tail regression. The time course of the events associated with tail regression provides a valuable reference, upon which the cellular and molecular mechanisms of ascidian metamorphosis can be elucidated.
Reconstruction of missing daily streamflow data using dynamic regression models
NASA Astrophysics Data System (ADS)
Tencaliec, Patricia; Favre, Anne-Catherine; Prieur, Clémentine; Mathevet, Thibault
2015-12-01
River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.
MCCB warm adjustment testing concept
NASA Astrophysics Data System (ADS)
Erdei, Z.; Horgos, M.; Grib, A.; Preradović, D. M.; Rodic, V.
2016-08-01
This paper presents an experimental investigation in to operating of thermal protection device behavior from an MCCB (Molded Case Circuit Breaker). One of the main functions of the circuit breaker is to assure protection for the circuits where mounted in for possible overloads of the circuit. The tripping mechanism for the overload protection is based on a bimetal movement during a specific time frame. This movement needs to be controlled and as a solution to control this movement we choose the warm adjustment concept. This concept is meant to improve process capability control and final output. The warm adjustment device design will create a unique adjustment of the bimetal position for each individual breaker, determined when the testing current will flow thru a phase which needs to trip in a certain amount of time. This time is predetermined due to scientific calculation for all standard types of amperages and complies with the IEC 60497 standard requirements.
ERIC Educational Resources Information Center
Friedlander, Laura J.; Reid, Graham J.; Shupak, Naomi; Cribbie, Robert
2007-01-01
The current study examined the joint effects of stress, social support, and self-esteem on adjustment to university. First-year undergraduate students (N = 115) were assessed during the first semester and again 10 weeks later, during the second semester of the academic year. Multiple regressions predicting adjustment to university from perceived…
Within-herd heritability estimated with daughter-parent regression for yield and somatic cell score.
Dechow, C D; Norman, H D
2007-01-01
Estimates of heritability within herd (h(WH)(2) ) that were generated with daughter-dam regression, daughter-sire regression, and REML were compared, and effects of adjusting lactation records for within-herd heritability on genetic evaluations were evaluated. Holstein records for milk, fat, and protein yields and somatic cell score (SCS) from the USDA national database represented herds in the US Northeast, Southeast, Midwest, and West. Four data subsets (457 to 499 herds) were randomly selected, and a large-herd subset included the 15 largest herds from the West and 10 largest herds from other regions. Subset heritabilities for yield and SCS were estimated assuming a regression model that included fixed covariates for effects of dam yield or SCS, sire predicted transmitting ability (PTA) for yield or SCS, herd-year-season of calving, and age within parity. Dam records and sire PTA were nested within herd as random covariates to generate within-herd heritability estimates that were regressed toward mean h(WH)(2) for the random subset. Heritabilities were estimated with REML using sire models (REML(SIRE)), sire-maternal grandsire models (REML(MGS)), and animal models (REML(ANIM)) for each herd individually in the large-herd subset. Phenotypic variance for each herd was estimated from herd residual variance after adjusting for effects of year-season and age within parity. Deviations from herd-year-season mean were standardized to constant genetic variance across herds, and records were weighted according to estimated error variance to accommodate h(WH)(2) when estimating breeding values. Mean h(WH)(2) tended to be higher with daughter-dam regression (0.35 for milk yield) than with daughter-sire regression (0.24 for milk yield). Heritability estimates varied widely across herds (0.04 to 0.67 for milk yield estimated with daughter-dam regression), and h(WH)(2) deviated from subset means more for large herds than for small herds. Correlation with REML(ANIM) h(WH)(2
Convective adjustment in baroclinic atmospheres
NASA Technical Reports Server (NTRS)
Emanuel, Kerry A.
1986-01-01
Local convection in planetary atmospheres is generally considered to result from the action of gravity on small regions of anomalous density. That in rotating baroclinic fluids the total potential energy for small scale convection contains a centrifugal as well as a gravitational contribution is shown. Convective adjustment in such an atmosphere results in the establishment of near adiabatic lapse rates of temperature along suitably defined surfaces of constant angular momentum, rather than in the vertical. This leads in general to sub-adiabatic vertical lapse rates. That such an adjustment actually occurs in the earth's atmosphere is shown by example and the magnitude of the effect for several other planetary atmospheres is estimated.
Risk Adjustment for Medicare Total Knee Arthroplasty Bundled Payments.
Clement, R Carter; Derman, Peter B; Kheir, Michael M; Soo, Adrianne E; Flynn, David N; Levin, L Scott; Fleisher, Lee
2016-09-01
The use of bundled payments is growing because of their potential to align providers and hospitals on the goal of cost reduction. However, such gain sharing could incentivize providers to "cherry-pick" more profitable patients. Risk adjustment can prevent this unintended consequence, yet most bundling programs include minimal adjustment techniques. This study was conducted to determine how bundled payments for total knee arthroplasty (TKA) should be adjusted for risk. The authors collected financial data for all Medicare patients (age≥65 years) undergoing primary unilateral TKA at an academic center over a period of 2 years (n=941). Multivariate regression was performed to assess the effect of patient factors on the costs of acute inpatient care, including unplanned 30-day readmissions. This analysis mirrors a bundling model used in the Medicare Bundled Payments for Care Improvement initiative. Increased age, American Society of Anesthesiologists (ASA) class, and the presence of a Medicare Major Complications/Comorbid Conditions (MCC) modifier (typically representing major complications) were associated with increased costs (regression coefficients, $57 per year; $729 per ASA class beyond I; and $3122 for patients meeting MCC criteria; P=.003, P=.001, and P<.001, respectively). Differences in costs were not associated with body mass index, sex, or race. If the results are generalizable, Medicare bundled payments for TKA encompassing acute inpatient care should be adjusted upward by the stated amounts for older patients, those with elevated ASA class, and patients meeting MCC criteria. This is likely an underestimate for many bundling models, including the Comprehensive Care for Joint Replacement program, incorporating varying degrees of postacute care. Failure to adjust for factors that affect costs may create adverse incentives, creating barriers to care for certain patient populations. [Orthopedics. 2016; 39(5):e911-e916.]. PMID:27359282
Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression
Chen, Yanguang
2016-01-01
In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson’s statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran’s index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China’s regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test. PMID:26800271
Dietary mucilage promotes regression of atheromatous lesions in hypercholesterolemic rabbits.
Boban, Puthenpura T; Nambisan, Bala; Sudhakaran, Perumana R
2009-05-01
The antihypercholesterolemic and antiatherogenic effect of the mucilage galactomannan isolated from fenugreek seeds was studied in experimental rabbits maintained on a high cholesterol diet for 3 months. Changes in the levels of cholesterol and triglycerides in serum and tissues and aortic fatty lesions were analysed in animals receiving mucilage (40 mg/kg body weight) daily and compared with the control. A significant decrease in serum total cholesterol, LDL cholesterol and triglycerides and cholesterol and triglycerides in liver and aorta and a decrease in Sudan IV staining of aorta indicated antihypercholesterolemic and antiatherogenic effects of the mucilage. Regression studies showed that administration of mucilage for 3 months caused a significant decrease in serum total and LDL cholesterol and aortic cholesterol. Mucilage accelerated the regression of atheromatous lesions in the aorta as evidenced by significantly low sudanophilic staining. Recovery from inflammation in hypercholesterolemic animals receiving mucilage was evidenced by a faster decrease in C-reactive protein (CRP) in serum to basal levels. The lipid lowering and antiatherogenic effects of mucilage from fenugreek which is used as a food flavoring spice highlights the importance of dietary intervention in the regression of atherosclerosis. PMID:19107734
Overlapping Group Logistic Regression with Applications to Genetic Pathway Selection
Zeng, Yaohui; Breheny, Patrick
2016-01-01
Discovering important genes that account for the phenotype of interest has long been a challenge in genome-wide expression analysis. Analyses such as gene set enrichment analysis (GSEA) that incorporate pathway information have become widespread in hypothesis testing, but pathway-based approaches have been largely absent from regression methods due to the challenges of dealing with overlapping pathways and the resulting lack of available software. The R package grpreg is widely used to fit group lasso and other group-penalized regression models; in this study, we develop an extension, grpregOverlap, to allow for overlapping group structure using a latent variable approach. We compare this approach to the ordinary lasso and to GSEA using both simulated and real data. We find that incorporation of prior pathway information can substantially improve the accuracy of gene expression classifiers, and we shed light on several ways in which hypothesis-testing approaches such as GSEA differ from regression approaches with respect to the analysis of pathway data. PMID:27679461
Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression.
Chen, Yanguang
2016-01-01
In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson's statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran's index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China's regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test.
Calibration of stormwater quality regression models: a random process?
Dembélé, A; Bertrand-Krajewski, J-L; Barillon, B
2010-01-01
Regression models are among the most frequently used models to estimate pollutants event mean concentrations (EMC) in wet weather discharges in urban catchments. Two main questions dealing with the calibration of EMC regression models are investigated: i) the sensitivity of models to the size and the content of data sets used for their calibration, ii) the change of modelling results when models are re-calibrated when data sets grow and change with time when new experimental data are collected. Based on an experimental data set of 64 rain events monitored in a densely urbanised catchment, four TSS EMC regression models (two log-linear and two linear models) with two or three explanatory variables have been derived and analysed. Model calibration with the iterative re-weighted least squares method is less sensitive and leads to more robust results than the ordinary least squares method. Three calibration options have been investigated: two options accounting for the chronological order of the observations, one option using random samples of events from the whole available data set. Results obtained with the best performing non linear model clearly indicate that the model is highly sensitive to the size and the content of the data set used for its calibration.
Overlapping Group Logistic Regression with Applications to Genetic Pathway Selection.
Zeng, Yaohui; Breheny, Patrick
2016-01-01
Discovering important genes that account for the phenotype of interest has long been a challenge in genome-wide expression analysis. Analyses such as gene set enrichment analysis (GSEA) that incorporate pathway information have become widespread in hypothesis testing, but pathway-based approaches have been largely absent from regression methods due to the challenges of dealing with overlapping pathways and the resulting lack of available software. The R package grpreg is widely used to fit group lasso and other group-penalized regression models; in this study, we develop an extension, grpregOverlap, to allow for overlapping group structure using a latent variable approach. We compare this approach to the ordinary lasso and to GSEA using both simulated and real data. We find that incorporation of prior pathway information can substantially improve the accuracy of gene expression classifiers, and we shed light on several ways in which hypothesis-testing approaches such as GSEA differ from regression approaches with respect to the analysis of pathway data. PMID:27679461
Overlapping Group Logistic Regression with Applications to Genetic Pathway Selection
Zeng, Yaohui; Breheny, Patrick
2016-01-01
Discovering important genes that account for the phenotype of interest has long been a challenge in genome-wide expression analysis. Analyses such as gene set enrichment analysis (GSEA) that incorporate pathway information have become widespread in hypothesis testing, but pathway-based approaches have been largely absent from regression methods due to the challenges of dealing with overlapping pathways and the resulting lack of available software. The R package grpreg is widely used to fit group lasso and other group-penalized regression models; in this study, we develop an extension, grpregOverlap, to allow for overlapping group structure using a latent variable approach. We compare this approach to the ordinary lasso and to GSEA using both simulated and real data. We find that incorporation of prior pathway information can substantially improve the accuracy of gene expression classifiers, and we shed light on several ways in which hypothesis-testing approaches such as GSEA differ from regression approaches with respect to the analysis of pathway data.
Geodesic least squares regression for scaling studies in magnetic confinement fusion
Verdoolaege, Geert
2015-01-13
In regression analyses for deriving scaling laws that occur in various scientific disciplines, usually standard regression methods have been applied, of which ordinary least squares (OLS) is the most popular. However, concerns have been raised with respect to several assumptions underlying OLS in its application to scaling laws. We here discuss a new regression method that is robust in the presence of significant uncertainty on both the data and the regression model. The method, which we call geodesic least squares regression (GLS), is based on minimization of the Rao geodesic distance on a probabilistic manifold. We demonstrate the superiority of the method using synthetic data and we present an application to the scaling law for the power threshold for the transition to the high confinement regime in magnetic confinement fusion devices.
Dorer, David J; Knickerbocker, Ronald K; Baccarani, Michele; Cortes, Jorge E; Hochhaus, Andreas; Talpaz, Moshe; Haluska, Frank G
2016-09-01
Ponatinib is approved for adults with refractory chronic myeloid leukemia or Philadelphia chromosome-positive acute lymphoblastic leukemia, including those with the T315I BCR-ABL1 mutation. We pooled data from 3 clinical trials (N=671) to determine the impact of ponatinib dose intensity on the following adverse events: arterial occlusive events (cardiovascular, cerebrovascular, and peripheral vascular events), venous thromboembolic events, cardiac failure, thrombocytopenia, neutropenia, hypertension, pancreatitis, increased lipase, increased alanine aminotransferase, increased aspartate aminotransferase, rash, arthralgia, and hypertriglyceridemia. Multivariate analyses allowed adjustment for covariates potentially related to changes in dosing or an event. Logistic regression analysis identified significant associations between dose intensity and most events after adjusting for covariates. Pancreatitis, rash, and cardiac failure had the strongest associations with dose intensity (odds ratios >2). Time-to-event analyses showed significant associations between dose intensity and risk of arterial occlusive events and each subcategory. Further, these analyses suggested that a lag exists between a change in dose and the resulting change in event risk. No significant association between dose intensity and risk of venous thromboembolic events was evident. Collectively, these findings suggest a potential causal relationship between ponatinib dose and certain adverse events and support prospective investigations of approaches to lower average ponatinib dose intensity. PMID:27505637
Dorer, David J; Knickerbocker, Ronald K; Baccarani, Michele; Cortes, Jorge E; Hochhaus, Andreas; Talpaz, Moshe; Haluska, Frank G
2016-09-01
Ponatinib is approved for adults with refractory chronic myeloid leukemia or Philadelphia chromosome-positive acute lymphoblastic leukemia, including those with the T315I BCR-ABL1 mutation. We pooled data from 3 clinical trials (N=671) to determine the impact of ponatinib dose intensity on the following adverse events: arterial occlusive events (cardiovascular, cerebrovascular, and peripheral vascular events), venous thromboembolic events, cardiac failure, thrombocytopenia, neutropenia, hypertension, pancreatitis, increased lipase, increased alanine aminotransferase, increased aspartate aminotransferase, rash, arthralgia, and hypertriglyceridemia. Multivariate analyses allowed adjustment for covariates potentially related to changes in dosing or an event. Logistic regression analysis identified significant associations between dose intensity and most events after adjusting for covariates. Pancreatitis, rash, and cardiac failure had the strongest associations with dose intensity (odds ratios >2). Time-to-event analyses showed significant associations between dose intensity and risk of arterial occlusive events and each subcategory. Further, these analyses suggested that a lag exists between a change in dose and the resulting change in event risk. No significant association between dose intensity and risk of venous thromboembolic events was evident. Collectively, these findings suggest a potential causal relationship between ponatinib dose and certain adverse events and support prospective investigations of approaches to lower average ponatinib dose intensity.
Can Quiet Standing Posture Predict Compensatory Postural Adjustment?
Moya, Gabriel Bueno Lahóz; Siqueira, Cássio Marinho; Caffaro, Renê Rogieri; Fu, Carolina; Tanaka, Clarice
2009-01-01
OBJECTIVE The aim of this study was to analyze whether quiet standing posture is related to compensatory postural adjustment. INTRODUCTION The latest data in clinical practice suggests that static posture may play a significant role in musculoskeletal function, even in dynamic activities. However, no evidence exists regarding whether static posture during quiet standing is related to postural adjustment. METHODS Twenty healthy participants standing on a movable surface underwent unexpected, standardized backward and forward postural perturbations while kinematic data were acquired; ankle, knee, pelvis and trunk positions were then calculated. An initial and a final video frame representing quiet standing posture and the end of the postural perturbation were selected in such a way that postural adjustments had occurred between these frames. The positions of the body segments were calculated in these initial and final frames, together with the displacement of body segments during postural adjustments between the initial and final frames. The relationship between the positions of body segments in the initial and final frames and their displacements over this time period was analyzed using multiple regressions with a significance level of p ≤ 0.05. RESULTS We failed to identify a relationship between the position of the body segments in the initial and final frames and the associated displacement of the body segments. DISCUSSION The motion pattern during compensatory postural adjustment is not related to quiet standing posture or to the final posture of compensatory postural adjustment. This fact should be considered when treating balance disturbances and musculoskeletal abnormalities. CONCLUSION Static posture cannot predict how body segments will behave during compensatory postural adjustment. PMID:19690665
An empirical evaluation of spatial regression models
NASA Astrophysics Data System (ADS)
Gao, Xiaolu; Asami, Yasushi; Chung, Chang-Jo F.
2006-10-01
Conventional statistical methods are often ineffective to evaluate spatial regression models. One reason is that spatial regression models usually have more parameters or smaller sample sizes than a simple model, so their degree of freedom is reduced. Thus, it is often unlikely to evaluate them based on traditional tests. Another reason, which is theoretically associated with statistical methods, is that statistical criteria are crucially dependent on such assumptions as normality, independence, and homogeneity. This may create problems because the assumptions are open for testing. In view of these problems, this paper proposes an alternative empirical evaluation method. To illustrate the idea, a few hedonic regression models for a house and land price data set are evaluated, including a simple, ordinary linear regression model and three spatial models. Their performance as to how well the price of the house and land can be predicted is examined. With a cross-validation technique, the prices at each sample point are predicted with a model estimated with the samples excluding the one being concerned. Then, empirical criteria are established whereby the predicted prices are compared with the real, observed prices. The proposed method provides an objective guidance for the selection of a suitable model specification for a data set. Moreover, the method is seen as an alternative way to test the significance of the spatial relationships being concerned in spatial regression models.
Response-adaptive regression for longitudinal data.
Wu, Shuang; Müller, Hans-Georg
2011-09-01
We propose a response-adaptive model for functional linear regression, which is adapted to sparsely sampled longitudinal responses. Our method aims at predicting response trajectories and models the regression relationship by directly conditioning the sparse and irregular observations of the response on the predictor, which can be of scalar, vector, or functional type. This obliterates the need to model the response trajectories, a task that is challenging for sparse longitudinal data and was previously required for functional regression implementations for longitudinal data. The proposed approach turns out to be superior compared to previous functional regression approaches in terms of prediction error. It encompasses a variety of regression settings that are relevant for the functional modeling of longitudinal data in the life sciences. The improved prediction of response trajectories with the proposed response-adaptive approach is illustrated for a longitudinal study of Kiwi weight growth and by an analysis of the dynamic relationship between viral load and CD4 cell counts observed in AIDS clinical trials. PMID:21133880
Mental chronometry with simple linear regression.
Chen, J Y
1997-10-01
Typically, mental chronometry is performed by means of introducing an independent variable postulated to affect selectively some stage of a presumed multistage process. However, the effect could be a global one that spreads proportionally over all stages of the process. Currently, there is no method to test this possibility although simple linear regression might serve the purpose. In the present study, the regression approach was tested with tasks (memory scanning and mental rotation) that involved a selective effect and with a task (word superiority effect) that involved a global effect, by the dominant theories. The results indicate (1) the manipulation of the size of a memory set or of angular disparity affects the intercept of the regression function that relates the times for memory scanning with different set sizes or for mental rotation with different angular disparities and (2) the manipulation of context affects the slope of the regression function that relates the times for detecting a target character under word and nonword conditions. These ratify the regression approach as a useful method for doing mental chronometry. PMID:9347535
MULTILINEAR TENSOR REGRESSION FOR LONGITUDINAL RELATIONAL DATA
Hoff, Peter D.
2016-01-01
A fundamental aspect of relational data, such as from a social network, is the possibility of dependence among the relations. In particular, the relations between members of one pair of nodes may have an effect on the relations between members of another pair. This article develops a type of regression model to estimate such effects in the context of longitudinal and multivariate relational data, or other data that can be represented in the form of a tensor. The model is based on a general multilinear tensor regression model, a special case of which is a tensor autoregression model in which the tensor of relations at one time point are parsimoniously regressed on relations from previous time points. This is done via a separable, or Kronecker-structured, regression parameter along with a separable covariance model. In the context of an analysis of longitudinal multivariate relational data, it is shown how the multilinear tensor regression model can represent patterns that often appear in relational and network data, such as reciprocity and transitivity. PMID:27458495
Assessment of Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk
Czarnota, Jenna; Gennings, Chris; Wheeler, David C
2015-01-01
In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalized regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these problems by estimating a body burden index, which identifies important chemicals in a mixture of correlated environmental chemicals. Our focus was on assessing through simulation studies the accuracy of WQS regression in detecting subsets of chemicals associated with health outcomes (binary and continuous) in site-specific analyses and in non-site-specific analyses. We also evaluated the performance of the penalized regression methods of lasso, adaptive lasso, and elastic net in correctly classifying chemicals as bad actors or unrelated to the outcome. We based the simulation study on data from the National Cancer Institute Surveillance Epidemiology and End Results Program (NCI-SEER) case–control study of non-Hodgkin lymphoma (NHL) to achieve realistic exposure situations. Our results showed that WQS regression had good sensitivity and specificity across a variety of conditions considered in this study. The shrinkage methods had a tendency to incorrectly identify a large number of components, especially in the case of strong association with the outcome. PMID:26005323
Analyzing industrial energy use through ordinary least squares regression models
NASA Astrophysics Data System (ADS)
Golden, Allyson Katherine
Extensive research has been performed using regression analysis and calibrated simulations to create baseline energy consumption models for residential buildings and commercial institutions. However, few attempts have been made to discuss the applicability of these methodologies to establish baseline energy consumption models for industrial manufacturing facilities. In the few studies of industrial facilities, the presented linear change-point and degree-day regression analyses illustrate ideal cases. It follows that there is a need in the established literature to discuss the methodologies and to determine their applicability for establishing baseline energy consumption models of industrial manufacturing facilities. The thesis determines the effectiveness of simple inverse linear statistical regression models when establishing baseline energy consumption models for industrial manufacturing facilities. Ordinary least squares change-point and degree-day regression methods are used to create baseline energy consumption models for nine different case studies of industrial manufacturing facilities located in the southeastern United States. The influence of ambient dry-bulb temperature and production on total facility energy consumption is observed. The energy consumption behavior of industrial manufacturing facilities is only sometimes sufficiently explained by temperature, production, or a combination of the two variables. This thesis also provides methods for generating baseline energy models that are straightforward and accessible to anyone in the industrial manufacturing community. The methods outlined in this thesis may be easily replicated by anyone that possesses basic spreadsheet software and general knowledge of the relationship between energy consumption and weather, production, or other influential variables. With the help of simple inverse linear regression models, industrial manufacturing facilities may better understand their energy consumption and
Epidemiology of CKD Regression in Patients under Nephrology Care
Borrelli, Silvio; Leonardis, Daniela; Minutolo, Roberto; Chiodini, Paolo; De Nicola, Luca; Esposito, Ciro; Mallamaci, Francesca; Zoccali, Carmine; Conte, Giuseppe
2015-01-01
Chronic Kidney Disease (CKD) regression is considered as an infrequent renal outcome, limited to early stages, and associated with higher mortality. However, prevalence, prognosis and the clinical correlates of CKD regression remain undefined in the setting of nephrology care. This is a multicenter prospective study in 1418 patients with established CKD (eGFR: 60–15 ml/min/1.73m²) under nephrology care in 47 outpatient clinics in Italy from a least one year. We defined CKD regressors as a ΔGFR ≥0 ml/min/1.73 m2/year. ΔGFR was estimated as the absolute difference between eGFR measured at baseline and at follow up visit after 18–24 months, respectively. Outcomes were End Stage Renal Disease (ESRD) and overall-causes Mortality.391 patients (27.6%) were identified as regressors as they showed an eGFR increase between the baseline visit in the renal clinic and the follow up visit. In multivariate regression analyses the regressor status was not associated with CKD stage. Low proteinuria was the main factor associated with CKD regression, accounting per se for 48% of the likelihood of this outcome. Lower systolic blood pressure, higher BMI and absence of autosomal polycystic disease (PKD) were additional predictors of CKD regression. In regressors, ESRD risk was 72% lower (HR: 0.28; 95% CI 0.14–0.57; p<0.0001) while mortality risk did not differ from that in non-regressors (HR: 1.16; 95% CI 0.73–1.83; p = 0.540). Spline models showed that the reduction of ESRD risk associated with positive ΔGFR was attenuated in advanced CKD stage. CKD regression occurs in about one-fourth patients receiving renal care in nephrology units and correlates with low proteinuria, BP and the absence of PKD. This condition portends better renal prognosis, mostly in earlier CKD stages, with no excess risk for mortality. PMID:26462071
NASA Technical Reports Server (NTRS)
Gallimore, F. H.
1986-01-01
Adjustable angular drill block accurately transfers hole patterns from mating surfaces not normal to each other. Block applicable to transfer of nonperpendicular holes in mating contoured assemblies in aircraft industry. Also useful in general manufacturing to transfer mating installation holes to irregular and angular surfaces.
Economic Pressures and Family Adjustment.
ERIC Educational Resources Information Center
Haccoun, Dorothy Markiewicz; Ledingham, Jane E.
The relationships between economic stress on the family and child and parental adjustment were examined for a sample of 199 girls and boys in grades one, four, and seven. These associations were examined separately for families in which both parents were present and in which mothers only were at home. Economic stress was associated with boys'…
Uncertainty quantification in DIC with Kriging regression
NASA Astrophysics Data System (ADS)
Wang, Dezhi; DiazDelaO, F. A.; Wang, Weizhuo; Lin, Xiaoshan; Patterson, Eann A.; Mottershead, John E.
2016-03-01
A Kriging regression model is developed as a post-processing technique for the treatment of measurement uncertainty in classical subset-based Digital Image Correlation (DIC). Regression is achieved by regularising the sample-point correlation matrix using a local, subset-based, assessment of the measurement error with assumed statistical normality and based on the Sum of Squared Differences (SSD) criterion. This leads to a Kriging-regression model in the form of a Gaussian process representing uncertainty on the Kriging estimate of the measured displacement field. The method is demonstrated using numerical and experimental examples. Kriging estimates of displacement fields are shown to be in excellent agreement with 'true' values for the numerical cases and in the experimental example uncertainty quantification is carried out using the Gaussian random process that forms part of the Kriging model. The root mean square error (RMSE) on the estimated displacements is produced and standard deviations on local strain estimates are determined.
MLREG, stepwise multiple linear regression program
Carder, J.H.
1981-09-01
This program is written in FORTRAN for an IBM computer and performs multiple linear regressions according to a stepwise procedure. The program transforms and combines old variables into new variables, prints input and transformed data, sums, raw sums or squares, residual sum of squares, means and standard deviations, correlation coefficients, regression results at each step, ANOVA at each step, and predicted response results at each step. This package contains an EXEC used to execute the program,sample input data and output listing, source listing, documentation, and card decks containing the EXEC sample input, and FORTRAN source.
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.
Spontaneous regression of a conjunctival naevus.
Haldar, Shreya; Leyland, Martin
2016-01-01
Conjunctival naevi are one of the most common lesions affecting the conjunctiva. While benign in the vast majority of cases, the risk of malignant transformation necessitates regular follow-up. They are well known to increase in size; however, we present the first photo-documented case of spontaneous regression of conjunctival naevus. In most cases, surgical excision is performed due to the clinician's concerns over malignancy. However, a substantial proportion of patients request excision. Highlighting the potential for regression of the lesion is important to ensure patients make an informed decision when contemplating such surgery. PMID:27581234
Removing Malmquist bias from linear regressions
NASA Technical Reports Server (NTRS)
Verter, Frances
1993-01-01
Malmquist bias is present in all astronomical surveys where sources are observed above an apparent brightness threshold. Those sources which can be detected at progressively larger distances are progressively more limited to the intrinsically luminous portion of the true distribution. This bias does not distort any of the measurements, but distorts the sample composition. We have developed the first treatment to correct for Malmquist bias in linear regressions of astronomical data. A demonstration of the corrected linear regression that is computed in four steps is presented.
Multicollinearity in cross-sectional regressions
NASA Astrophysics Data System (ADS)
Lauridsen, Jørgen; Mur, Jesùs
2006-10-01
The paper examines robustness of results from cross-sectional regression paying attention to the impact of multicollinearity. It is well known that the reliability of estimators (least-squares or maximum-likelihood) gets worse as the linear relationships between the regressors become more acute. We resolve the discussion in a spatial context, looking closely into the behaviour shown, under several unfavourable conditions, by the most outstanding misspecification tests when collinear variables are added to the regression. A Monte Carlo simulation is performed. The conclusions point to the fact that these statistics react in different ways to the problems posed.
Spontaneous hypnotic age regression: case report.
Spiegel, D; Rosenfeld, A
1984-12-01
Age regression--reliving the past as though it were occurring in the present, with age appropriate vocabulary, mental content, and affect--can occur with instruction in highly hypnotizable individuals, but has rarely been reported to occur spontaneously, especially as a primary symptom. The psychiatric presentation and treatment of a 16-year-old girl with spontaneous age regressions accessible and controllable with hypnosis and psychotherapy are described. Areas of overlap and divergence between this patient's symptoms and those found in patients with hysterical fugue and multiple personality syndrome are also discussed.
Defense mechanisms and psychological adjustment in childhood.
Sandstrom, Marlene J; Cramer, Phebe
2003-08-01
The association between maturity of defense use and psychological functioning was assessed in a group of 95 elementary school children. Defense mechanisms were measured using a valid and reliable storytelling task, and psychological adjustment was assessed through a combination of parent and self-report questionnaires. Correlational analyses indicated that children who relied on the developmentally immature defense of denial reported higher levels of self-rated social anxiety and depression and received higher ratings of parent-reported internalizing and externalizing behavior problems. However, children who made use of the developmentally mature defense of identification exhibited higher scores on perceived competence in social, academic, conduct, athletic, and global domains. Significantly, there was no relationship between children's use of denial and their level of perceived competence or between children's use of identification and their degree of maladjustment.
Logistic regression when binary predictor variables are highly correlated.
Barker, L; Brown, C
Standard logistic regression can produce estimates having large mean square error when predictor variables are multicollinear. Ridge regression and principal components regression can reduce the impact of multicollinearity in ordinary least squares regression. Generalizations of these, applicable in the logistic regression framework, are alternatives to standard logistic regression. It is shown that estimates obtained via ridge and principal components logistic regression can have smaller mean square error than estimates obtained through standard logistic regression. Recommendations for choosing among standard, ridge and principal components logistic regression are developed. Published in 2001 by John Wiley & Sons, Ltd.
Partial least squares (PLS) analysis offers a number of advantages over the more traditionally used regression analyses applied in landscape ecology to study the associations among constituents of surface water and landscapes. Common data problems in ecological studies include: s...
Partial least squares (PLS) analysis offers a number of advantages over the more traditionally used regression analyses applied in landscape ecology, particularly for determining the associations among multiple constituents of surface water and landscape configuration. Common dat...
Code of Federal Regulations, 2014 CFR
2014-04-01
... calendar year because of an error that does not constitute a compensation adjustment as defined in... compensation adjustment as defined in paragraph (b) of this section, the employer shall adjust the error by... compensation, proper adjustments with respect to the contributions shall be made, without interest,...
Code of Federal Regulations, 2013 CFR
2013-04-01
... calendar year because of an error that does not constitute a compensation adjustment as defined in... compensation adjustment as defined in paragraph (b) of this section, the employer shall adjust the error by... compensation, proper adjustments with respect to the contributions shall be made, without interest,...
Adjusting to University: The Hong Kong Experience
ERIC Educational Resources Information Center
Yau, Hon Keung; Sun, Hongyi; Cheng, Alison Lai Fong
2012-01-01
Students' adjustment to the university environment is an important factor in predicting university outcomes and is crucial to their future achievements. University support to students' transition to university life can be divided into three dimensions: academic adjustment, social adjustment and psychological adjustment. However, these…
12 CFR 19.240 - Inflation adjustments.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 12 Banks and Banking 1 2010-01-01 2010-01-01 false Inflation adjustments. 19.240 Section 19.240... PROCEDURE Civil Money Penalty Inflation Adjustments § 19.240 Inflation adjustments. (a) The maximum amount... Civil Penalties Inflation Adjustment Act of 1990 (28 U.S.C. 2461 note) as follows: ER10NO08.001 (b)...
12 CFR 19.240 - Inflation adjustments.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 12 Banks and Banking 1 2011-01-01 2011-01-01 false Inflation adjustments. 19.240 Section 19.240... PROCEDURE Civil Money Penalty Inflation Adjustments § 19.240 Inflation adjustments. (a) The maximum amount... Civil Penalties Inflation Adjustment Act of 1990 (28 U.S.C. 2461 note) as follows: ER10NO08.001 (b)...
12 CFR 19.240 - Inflation adjustments.
Code of Federal Regulations, 2012 CFR
2012-01-01
... 12 Banks and Banking 1 2012-01-01 2012-01-01 false Inflation adjustments. 19.240 Section 19.240... PROCEDURE Civil Money Penalty Inflation Adjustments § 19.240 Inflation adjustments. (a) The maximum amount... Civil Penalties Inflation Adjustment Act of 1990 (28 U.S.C. 2461 note) as follows: ER10NO08.001 (b)...
Bootstrap inference longitudinal semiparametric regression model
NASA Astrophysics Data System (ADS)
Pane, Rahmawati; Otok, Bambang Widjanarko; Zain, Ismaini; Budiantara, I. Nyoman
2016-02-01
Semiparametric regression contains two components, i.e. parametric and nonparametric component. Semiparametric regression model is represented by yt i=μ (x˜'ti,zt i)+εt i where μ (x˜'ti,zt i)=x˜'tiβ ˜+g (zt i) and yti is response variable. It is assumed to have a linear relationship with the predictor variables x˜'ti=(x1 i 1,x2 i 2,…,xT i r) . Random error εti, i = 1, …, n, t = 1, …, T is normally distributed with zero mean and variance σ2 and g(zti) is a nonparametric component. The results of this study showed that the PLS approach on longitudinal semiparametric regression models obtain estimators β˜^t=[X'H(λ)X]-1X'H(λ )y ˜ and g˜^λ(z )=M (λ )y ˜ . The result also show that bootstrap was valid on longitudinal semiparametric regression model with g^λ(b )(z ) as nonparametric component estimator.
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.
Nodular fasciitis with degeneration and regression.
Yanagisawa, Akihiro; Okada, Hideki
2008-07-01
Nodular fasciitis is a benign reactive proliferation that is frequently misdiagnosed as a sarcoma. This article describes a case of nodular fasciitis of 6-month duration located in the cheek, which degenerated and spontaneously regressed after biopsy. The nodule was fixed to the zygoma but was free from the overlying skin. The mass was 3.0 cm in diameter and demonstrated high signal intensity on T2-weighted magnetic resonance imaging. A small part of the lesion was biopsied. Pathological and immunohistochemical examinations identified the nodule as nodular fasciitis with myxoid histology. One month after the biopsy, the mass showed decreased signal intensity on T2-weighted images and measured 2.2 cm in size. The signal on T2-weighted images showed time-dependent decreases, and the mass continued to reduce in size throughout the follow-up period. The lesion presented as hypointense to the surrounding muscles on T2-weighted images and was 0.4 cm in size at 2 years of follow-up. This case demonstrates that nodular fasciitis with myxoid histology can change to that with fibrous appearance gradually with time, thus bringing about spontaneous regression. Degeneration may be involved in the spontaneous regression of nodular fasciitis with myxoid appearance. The mechanism of regression, unclarified at present, should be further studied. PMID:18650753
A New Sample Size Formula for Regression.
ERIC Educational Resources Information Center
Brooks, Gordon P.; Barcikowski, Robert S.
The focus of this research was to determine the efficacy of a new method of selecting sample sizes for multiple linear regression. A Monte Carlo simulation was used to study both empirical predictive power rates and empirical statistical power rates of the new method and seven other methods: those of C. N. Park and A. L. Dudycha (1974); J. Cohen…
Prediction of dynamical systems by symbolic regression.
Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K; Noack, Bernd R
2016-07-01
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast. PMID:27575130
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…
Method for nonlinear exponential regression analysis
NASA Technical Reports Server (NTRS)
Junkin, B. G.
1972-01-01
Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.
Multiple Regression Analysis and Automatic Interaction Detection.
ERIC Educational Resources Information Center
Koplyay, Janos B.
The Automatic Interaction Detector (AID) is discussed as to its usefulness in multiple regression analysis. The algorithm of AID-4 is a reversal of the model building process; it starts with the ultimate restricted model, namely, the whole group as a unit. By a unique splitting process maximizing the between sum of squares for the categories of…
A Spline Regression Model for Latent Variables
ERIC Educational Resources Information Center
Harring, Jeffrey R.
2014-01-01
Spline (or piecewise) regression models have been used in the past to account for patterns in observed data that exhibit distinct phases. The changepoint or knot marking the shift from one phase to the other, in many applications, is an unknown parameter to be estimated. As an extension of this framework, this research considers modeling the…
Regression Segmentation for M³ Spinal Images.
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.
Prediction of dynamical systems by symbolic regression
NASA Astrophysics Data System (ADS)
Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K.; Noack, Bernd R.
2016-07-01
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.
Using Regression Analysis: A Guided Tour.
ERIC Educational Resources Information Center
Shelton, Fred Ames
1987-01-01
Discusses the use and interpretation of multiple regression analysis with computer programs and presents a flow chart of the process. A general explanation of the flow chart is provided, followed by an example showing the development of a linear equation which could be used in estimating manufacturing overhead cost. (Author/LRW)
Genetic Programming Transforms in Linear Regression Situations
NASA Astrophysics Data System (ADS)
Castillo, Flor; Kordon, Arthur; Villa, Carlos
The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.
The M Word: Multicollinearity in Multiple Regression.
ERIC Educational Resources Information Center
Morrow-Howell, Nancy
1994-01-01
Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…
Revisiting Regression in Autism: Heller's "Dementia Infantilis"
ERIC Educational Resources Information Center
Westphal, Alexander; Schelinski, Stefanie; Volkmar, Fred; Pelphrey, Kevin
2013-01-01
Theodor Heller first described a severe regression of adaptive function in normally developing children, something he termed dementia infantilis, over one 100 years ago. Dementia infantilis is most closely related to the modern diagnosis, childhood disintegrative disorder. We translate Heller's paper, Uber Dementia Infantilis, and discuss…
Prediction of dynamical systems by symbolic regression.
Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K; Noack, Bernd R
2016-07-01
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.
Design Coding and Interpretation in Multiple Regression.
ERIC Educational Resources Information Center
Lunneborg, Clifford E.
The multiple regression or general linear model (GLM) is a parameter estimation and hypothesis testing model which encompasses and approaches the more familiar fixed effects analysis of variance (ANOVA). The transition from ANOVA to GLM is accomplished, roughly, by coding treatment level or group membership to produce a set of predictor or…
Predicting Social Trust with Binary Logistic Regression
ERIC Educational Resources Information Center
Adwere-Boamah, Joseph; Hufstedler, Shirley
2015-01-01
This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…
Covariate-adjusted response-adaptive designs for binary response.
Rosenberger, W F; Vidyashankar, A N; Agarwal, D K
2001-11-01
An adaptive allocation design for phase III clinical trials that incorporates covariates is described. The allocation scheme maps the covariate-adjusted odds ratio from a logistic regression model onto [0, 1]. Simulations assume that both staggered entry and time to response are random and follow a known probability distribution that can depend on the treatment assigned, the patient's response, a covariate, or a time trend. Confidence intervals on the covariate-adjusted odds ratio is slightly anticonservative for the adaptive design under the null hypothesis, but power is similar to equal allocation under various alternatives for n = 200. For similar power, the net savings in terms of expected number of treatment failures is modest, but enough to make this design attractive for certain studies where known covariates are expected to be important and stratification is not desired, and treatment failures have a high ethical cost.
Vaeth, Michael; Skovlund, Eva
2004-06-15
For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination.
Examining the Components of Children's Peer Liking as Antecedents of School Adjustment
ERIC Educational Resources Information Center
Betts, Lucy R.; Rotenberg, Ken J.; Trueman, Mark; Stiller, James
2012-01-01
Children's social interactions with their peers influence their psychosocial adjustment; consequently, the relationship between class-wide peer liking, same-gender peer liking, and school adjustment was explored in two age groups. Peer liking was analysed using the social relations model (SRM). In Study 1, 205 children (103 female and 102 male,…
Typology of Emotional and Behavioral Adjustment for Low-Income Children: A Child-Centered Approach
ERIC Educational Resources Information Center
Bulotsky-Shearer, Rebecca J.; Fantuzzo, John W.; McDermott, Paul A.
2010-01-01
An empirical typology of classroom emotional and behavioral adjustment was developed for preschool children living in urban poverty. Multistage hierarchical cluster analyses were applied to identify six distinct and reliable subtypes of classroom adjustment, differentiated by high and low levels of behavioral (aggressive, inattentive,…
ERIC Educational Resources Information Center
Yoleri, Sibel
2015-01-01
The relationships among school adjustment, victimisation, and gender were investigated with 284 Turkish children aged between five and six years. Teacher Rating Scale of School Adjustment, The Preschool Behaviour Questionnaire, and Peer Victimisation Scale were used in this study. Analyses indicated that children's behaviour problems and…
Logistic models--an odd(s) kind of regression.
Jupiter, Daniel C
2013-01-01
The logistic regression model bears some similarity to the multivariable linear regression with which we are familiar. However, the differences are great enough to warrant a discussion of the need for and interpretation of logistic regression.
Embedded Sensors for Measuring Surface Regression
NASA Technical Reports Server (NTRS)
Gramer, Daniel J.; Taagen, Thomas J.; Vermaak, Anton G.
2006-01-01
The development and evaluation of new hybrid and solid rocket motors requires accurate characterization of the propellant surface regression as a function of key operational parameters. These characteristics establish the propellant flow rate and are prime design drivers affecting the propulsion system geometry, size, and overall performance. There is a similar need for the development of advanced ablative materials, and the use of conventional ablatives exposed to new operational environments. The Miniature Surface Regression Sensor (MSRS) was developed to serve these applications. It is designed to be cast or embedded in the material of interest and regresses along with it. During this process, the resistance of the sensor is related to its instantaneous length, allowing the real-time thickness of the host material to be established. The time derivative of this data reveals the instantaneous surface regression rate. The MSRS could also be adapted to perform similar measurements for a variety of other host materials when it is desired to monitor thicknesses and/or regression rate for purposes of safety, operational control, or research. For example, the sensor could be used to monitor the thicknesses of brake linings or racecar tires and indicate when they need to be replaced. At the time of this reporting, over 200 of these sensors have been installed into a variety of host materials. An MSRS can be made in either of two configurations, denoted ladder and continuous (see Figure 1). A ladder MSRS includes two highly electrically conductive legs, across which narrow strips of electrically resistive material are placed at small increments of length. These strips resemble the rungs of a ladder and are electrically equivalent to many tiny resistors connected in parallel. A substrate material provides structural support for the legs and rungs. The instantaneous sensor resistance is read by an external signal conditioner via wires attached to the conductive legs on the
Parametric expressions for the adjusted Hargreaves coefficient in Eastern Spain
NASA Astrophysics Data System (ADS)
Martí, Pau; Zarzo, Manuel; Vanderlinden, Karl; Girona, Joan
2015-10-01
The application of simple empirical equations for estimating reference evapotranspiration (ETo) is the only alternative in many cases to robust approaches with high input requirements, especially at the local scale. In particular, temperature-based approaches present a high potential applicability, among others, because temperature might explain a high amount of ETo variability, and also because it can be measured easily and is one of the most available climatic inputs. One of the most well-known temperature-based approaches, the Hargreaves (HG) equation, requires a preliminary local calibration that is usually performed through an adjustment of the HG coefficient (AHC). Nevertheless, these calibrations are site-specific, and cannot be extrapolated to other locations. So, they become useless in many situations, because they are derived from already available benchmarks based on more robust methods, which will be applied in practice. Therefore, the development of accurate equations for estimating AHC at local scale becomes a relevant task. This paper analyses the performance of calibrated and non-calibrated HG equations at 30 stations in Eastern Spain at daily, weekly, fortnightly and monthly scales. Moreover, multiple linear regression was applied for estimating AHC based on different inputs, and the resulting equations yielded higher performance accuracy than the non-calibrated HG estimates. The approach relying on the ratio mean temperature to temperature range did not provide suitable AHC estimations, and was highly improved by splitting it into two independent predictors. Temperature-based equations were improved by incorporating geographical inputs. Finally, the model relying on temperature and geographic inputs was further improved by incorporating wind speed, even just with simple qualitative information about wind category (e.g. poorly vs. highly windy). The accuracy of the calibrated and non-calibrated HG estimates increased for longer time steps (daily
Progression and regression of the atherosclerotic plaque.
de Feyter, P J; Vos, J; Deckers, J W
1995-08-01
In animals in which atherosclerosis was induced experimentally (by a high cholesterol diet) regression of the atherosclerotic lesion was demonstrated after serum cholesterol was reduced by cholesterol- lowering drugs or a low-fat diet. Whether regression of advanced coronary arterly lesions also takes place in humans after a similar intervention remains conjectural. However, several randomized studies, primarily employing lipid-lowering intervention or comprehensive changes in lifestyle, have demonstrated, using serial angiograms, that it is possible to achieve less progression, arrest or even (small) regression of atherosclerotic lesions. The lipid-lowering trials (NHBLI, CLAS, POSCH, FATS, SCOR and STARS) studied 1240 symptomatic patients, mostly men, with moderately elevated cholesterol levels and moderately severe angiographic-proven coronary artery disease. A variety of lipid-lowering drugs, in addition to a diet, were used over an intervention period ranging from 2 to 3 years. In all but one study (NHBLI), the progression of coronary atherosclerosis was less in the treated group, but regression was induced in only a few patients. The overall relative risk of progression of coronary atherosclerosis was 0 x 62 and 2 x 13, respectively. The induced angiographic differences were small and did not produce any significant haemodynamic benefit. The most important result was tht the disease process could be stabilized in the majority of patients. Three comprehensive lifestyle change trials (the Lifestyle Heart study, STARS and the Heidelberg Study) studied 183 patients, who were subjected to stress management, and/or intensive exercise, in addition to a low fat diet, over a period ranging from 1 to 3 years. All three trials demonstrated less progression, and more regression with overall relative risks of 0 x 40 and 2 x 35 respectively, in the intervention groups. Angiographic trials demonstrated that retardation or arrest of coronary atherosclerosis was possible
Lambert, Sharon F; Roche, Kathleen M; Saleem, Farzana T; Henry, Jessica S
2015-09-01
Parents' racial socialization messages, including messages focused on awareness, preparation, and strategies for managing racial discrimination, are necessary to help African American youth successfully navigate their daily lives. However, mixed findings regarding the utility of preparation for bias messages for African American youth's mental health adjustment raise questions about the conditions under which these protective racial socialization messages are most beneficial to African American youth. The current study examined the degree to which communication and trust as well as anger and alienation in the mother-adolescent relationship moderated associations between 2 types of preparation for bias messages, cultural alertness to discrimination and cultural coping with antagonism, and adolescent mental health. Participants were 106 African American adolescents (57% female; mean age = 15.41) who reported about their receipt of racial socialization messages, mother-adolescent relationship quality, and depressive symptoms. Hierarchical regression analyses indicated that positive associations between cultural alertness to racial discrimination and youth depressive symptoms were weaker for boys in the context of higher mother-adolescent communication and trust; communication and trust were not similarly protective for girls. For boys, the positive associations between cultural coping with antagonism messages and depressive symptoms were stronger in the context of high anger and alienation in the mother-adolescent relationship. Findings suggest that qualities of the mother-adolescent relationship, in which preparation for bias messages are delivered, are important for understanding the mental health adjustment of African American adolescents. PMID:26460701
DuBois, David L; Silverthorn, Naida
2004-06-01
We investigated bias in self-perceptions of competence (relative to parent ratings) for family, school, and peer domains as predictors of adjustment problems among 139 young adolescents over a 1-year period using a prospective design. Regressions examined measures of bias at Time 1 (T1) as predictors of ratings of internalizing and externalizing problems at Time 2 (T2), controlling for T1 adjustment ratings. For the family domain, curvilinear trends were found. Follow-up analyses revealed that for this domain both negative bias (self-perceptions less favorable than parent ratings) and positive bias (self-perceptions more favorable than parent ratings) predicted greater internalizing and externalizing problems as rated by youth, parents, and teachers. For the peer domain, higher scores on the measure of bias predicted greater internalizing and externalizing problems as rated by teachers. These findings are consistent with the view that accuracy in self-perceptions of competence can have important implications across multiple domains of development.
Lambert, Sharon F; Roche, Kathleen M; Saleem, Farzana T; Henry, Jessica S
2015-09-01
Parents' racial socialization messages, including messages focused on awareness, preparation, and strategies for managing racial discrimination, are necessary to help African American youth successfully navigate their daily lives. However, mixed findings regarding the utility of preparation for bias messages for African American youth's mental health adjustment raise questions about the conditions under which these protective racial socialization messages are most beneficial to African American youth. The current study examined the degree to which communication and trust as well as anger and alienation in the mother-adolescent relationship moderated associations between 2 types of preparation for bias messages, cultural alertness to discrimination and cultural coping with antagonism, and adolescent mental health. Participants were 106 African American adolescents (57% female; mean age = 15.41) who reported about their receipt of racial socialization messages, mother-adolescent relationship quality, and depressive symptoms. Hierarchical regression analyses indicated that positive associations between cultural alertness to racial discrimination and youth depressive symptoms were weaker for boys in the context of higher mother-adolescent communication and trust; communication and trust were not similarly protective for girls. For boys, the positive associations between cultural coping with antagonism messages and depressive symptoms were stronger in the context of high anger and alienation in the mother-adolescent relationship. Findings suggest that qualities of the mother-adolescent relationship, in which preparation for bias messages are delivered, are important for understanding the mental health adjustment of African American adolescents.
2012-01-01
Background Caesarean section (CS) rate is a quality of health care indicator frequently used at national and international level. The aim of this study was to assess whether adjustment for Robson’s Ten Group Classification System (TGCS), and clinical and socio-demographic variables of the mother and the fetus is necessary for inter-hospital comparisons of CS rates. Methods The study population includes 64,423 deliveries in Emilia-Romagna between January 1, 2003 and December 31, 2004, classified according to theTGCS. Poisson regression was used to estimate crude and adjusted hospital relative risks of CS compared to a reference category. Analyses were carried out in the overall population and separately according to the Robson groups (groups I, II, III, IV and V–X combined). Adjusted relative risks (RR) of CS were estimated using two risk-adjustment models; the first (M1) including the TGCS group as the only adjustment factor; the second (M2) including in addition demographic and clinical confounders identified using a stepwise selection procedure. Percentage variations between crude and adjusted RRs by hospital were calculated to evaluate the confounding effect of covariates. Results The percentage variations from crude to adjusted RR proved to be similar in M1 and M2 model. However, stratified analyses by Robson’s classification groups showed that residual confounding for clinical and demographic variables was present in groups I (nulliparous, single, cephalic, ≥37 weeks, spontaneous labour) and III (multiparous, excluding previous CS, single, cephalic, ≥37 weeks, spontaneous labour) and IV (multiparous, excluding previous CS, single, cephalic, ≥37 weeks, induced or CS before labour) and to a minor extent in groups II (nulliparous, single, cephalic, ≥37 weeks, induced or CS before labour) and IV (multiparous, excluding previous CS, single, cephalic, ≥37 weeks, induced or CS before labour). Conclusions The TGCS classification is useful for
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.
Adjustable link for kinematic mounting systems
Hale, Layton C.
1997-01-01
An adjustable link for kinematic mounting systems. The adjustable link is a low-cost, passive device that provides backlash-free adjustment along its single constraint direction and flexural freedom in all other directions. The adjustable link comprises two spheres, two sockets in which the spheres are adjustable retain, and a connection link threadly connected at each end to the spheres, to provide a single direction of restraint and to adjust the length or distance between the sockets. Six such adjustable links provide for six degrees of freedom for mounting an instrument on a support. The adjustable link has applications in any machine or instrument requiring precision adjustment in six degrees of freedom, isolation from deformations of the supporting platform, and/or additional structural damping. The damping is accomplished by using a hollow connection link that contains an inner rod and a viscoelastic separation layer between the two.
Adjustable link for kinematic mounting systems
Hale, L.C.
1997-07-01
An adjustable link for kinematic mounting systems is disclosed. The adjustable link is a low-cost, passive device that provides backlash-free adjustment along its single constraint direction and flexural freedom in all other directions. The adjustable link comprises two spheres, two sockets in which the spheres are adjustable retain, and a connection link threadly connected at each end to the spheres, to provide a single direction of restraint and to adjust the length or distance between the sockets. Six such adjustable links provide for six degrees of freedom for mounting an instrument on a support. The adjustable link has applications in any machine or instrument requiring precision adjustment in six degrees of freedom, isolation from deformations of the supporting platform, and/or additional structural damping. The damping is accomplished by using a hollow connection link that contains an inner rod and a viscoelastic separation layer between the two. 3 figs.
Teaching Practices and the Promotion of Achievement and Adjustment in First Grade
ERIC Educational Resources Information Center
Perry, Kathryn E.; Donohue, Kathleen M.; Weinstein, Rhona S.
2007-01-01
The effects of teacher practices in promoting student academic achievement, behavioral adjustment, and feelings of competence were investigated in a prospective study of 257 children in 14 first grade classrooms. Using hierarchical linear modeling and regression techniques, observed teaching practices in the fall were explored as predictors of…
Competing risks regression for clustered data.
Zhou, Bingqing; Fine, Jason; Latouche, Aurelien; Labopin, Myriam
2012-07-01
A population average regression model is proposed to assess the marginal effects of covariates on the cumulative incidence function when there is dependence across individuals within a cluster in the competing risks setting. This method extends the Fine-Gray proportional hazards model for the subdistribution to situations, where individuals within a cluster may be correlated due to unobserved shared factors. Estimators of the regression parameters in the marginal model are developed under an independence working assumption where the correlation across individuals within a cluster is completely unspecified. The estimators are consistent and asymptotically normal, and variance estimation may be achieved without specifying the form of the dependence across individuals. A simulation study evidences that the inferential procedures perform well with realistic sample sizes. The practical utility of the methods is illustrated with data from the European Bone Marrow Transplant Registry.
Competing risks regression for stratified data.
Zhou, Bingqing; Latouche, Aurelien; Rocha, Vanderson; Fine, Jason
2011-06-01
For competing risks data, the Fine-Gray proportional hazards model for subdistribution has gained popularity for its convenience in directly assessing the effect of covariates on the cumulative incidence function. However, in many important applications, proportional hazards may not be satisfied, including multicenter clinical trials, where the baseline subdistribution hazards may not be common due to varying patient populations. In this article, we consider a stratified competing risks regression, to allow the baseline hazard to vary across levels of the stratification covariate. According to the relative size of the number of strata and strata sizes, two stratification regimes are considered. Using partial likelihood and weighting techniques, we obtain consistent estimators of regression parameters. The corresponding asymptotic properties and resulting inferences are provided for the two regimes separately. Data from a breast cancer clinical trial and from a bone marrow transplantation registry illustrate the potential utility of the stratified Fine-Gray model.
Emptiness as defense in severe regressive states.
LaFarge, L
1989-01-01
This paper examines the empty states experienced by severely ill borderline patients. At times of stressful regression, these patients use complaints of emptiness to describe profound disturbances of affect, cognition, object relations, and bodily experience. Empty states may be seen as complex defensive configurations which protect a borderline level of psychic structure from the impact of aggressively charged object relations, and ward off further regression to states of fragmentation or fusion. Severely ill borderline patients consolidate an empty screen by means of a characteristic repertoire of primitive defenses consisting of various forms of projective identification, including bitriangulation and projective identification of psychic agencies, somatization, acting out, and specific alterations in cognition. The author describes the highly deviant organizations of the object world seen in empty states, and the complex and disturbing countertransferences which these states evoke.
Are increases in cigarette taxation regressive?
Borren, P; Sutton, M
1992-12-01
Using the latest published data from Tobacco Advisory Council surveys, this paper re-evaluates the question of whether or not increases in cigarette taxation are regressive in the United Kingdom. The extended data set shows no evidence of increasing price-elasticity by social class as found in a major previous study. To the contrary, there appears to be no clear pattern in the price responsiveness of smoking behaviour across different social classes. Increases in cigarette taxation, while reducing smoking levels in all groups, fall most heavily on men and women in the lowest social class. Men and women in social class five can expect to pay eight and eleven times more of a tax increase respectively, than their social class one counterparts. Taken as a proportion of relative incomes, the regressive nature of increases in cigarette taxation is even more pronounced.
Sigurdson, J F; Wallander, J; Sund, A M
2014-10-01
The aim was to examine prospectively associations between bullying involvement at 14-15 years of age and self-reported general health and psychosocial adjustment in young adulthood, at 26-27 years of age. A large representative sample (N=2,464) was recruited and assessed in two counties in Mid-Norway in 1998 (T1) and 1999/2000 (T2) when the respondents had a mean age of 13.7 and 14.9, respectively, leading to classification as being bullied, bully-victim, being aggressive toward others or non-involved. Information about general health and psychosocial adjustment was gathered at a follow-up in 2012 (T4) (N=1,266) with a respondent mean age of 27.2. Logistic regression and ANOVA analyses showed that groups involved in bullying of any type in adolescence had increased risk for lower education as young adults compared to those non-involved. The group aggressive toward others also had a higher risk of being unemployed and receiving any kind of social help. Compared with the non-involved, those being bullied and bully-victims had increased risk of poor general health and high levels of pain. Bully-victims and those aggressive toward others during adolescence subsequently had increased risk of tobacco use and lower job functioning than non-involved. Further, those being bullied and aggressive toward others had increased risk of illegal drug use. Relations to live-in spouse/partner were poorer among those being bullied. Involvement in bullying, either as victim or perpetrator, has significant social costs even 12 years after the bullying experience. Accordingly, it will be important to provide early intervention for those involved in bullying in adolescence.
Sigurdson, J F; Wallander, J; Sund, A M
2014-10-01
The aim was to examine prospectively associations between bullying involvement at 14-15 years of age and self-reported general health and psychosocial adjustment in young adulthood, at 26-27 years of age. A large representative sample (N=2,464) was recruited and assessed in two counties in Mid-Norway in 1998 (T1) and 1999/2000 (T2) when the respondents had a mean age of 13.7 and 14.9, respectively, leading to classification as being bullied, bully-victim, being aggressive toward others or non-involved. Information about general health and psychosocial adjustment was gathered at a follow-up in 2012 (T4) (N=1,266) with a respondent mean age of 27.2. Logistic regression and ANOVA analyses showed that groups involved in bullying of any type in adolescence had increased risk for lower education as young adults compared to those non-involved. The group aggressive toward others also had a higher risk of being unemployed and receiving any kind of social help. Compared with the non-involved, those being bullied and bully-victims had increased risk of poor general health and high levels of pain. Bully-victims and those aggressive toward others during adolescence subsequently had increased risk of tobacco use and lower job functioning than non-involved. Further, those being bullied and aggressive toward others had increased risk of illegal drug use. Relations to live-in spouse/partner were poorer among those being bullied. Involvement in bullying, either as victim or perpetrator, has significant social costs even 12 years after the bullying experience. Accordingly, it will be important to provide early intervention for those involved in bullying in adolescence. PMID:24972719
Kerr, Natalie C.
2011-01-01
Purpose Overcorrection of hypotropia subsequent to adjustable suture surgery following inferior rectus recession is undesirable, often resulting in persistent diplopia and reoperation. I hypothesized that overcorrection shift after suture adjustment may be unique to thyroid eye disease, and the use of a nonabsorbable suture may reduce the occurrence of overcorrection. Methods A retrospective chart review of adult patients who had undergone eye muscle surgery with an adjustable suture technique was performed. Overcorrection shifts that occurred between the time of suture adjustment and 2 months postoperatively were examined. Descriptive statistics, linear regression, Anderson-Darling tests, generalized Pareto distributions, odds ratios, and Fisher tests were performed for two overcorrection shift thresholds (>2 and >5 prism diopters [PD]). Results Seventy-seven patients were found: 34 had thyroid eye disease and inferior rectus recession, 30 had no thyroid eye disease and inferior rectus recession, and 13 patients had thyroid eye disease and medial rectus recession. Eighteen cases exceeded the 2 PD threshold, and 12 exceeded the 5 PD threshold. Statistical analyses indicated that overcorrection was associated with thyroid eye disease (P=6.7E-06), inferior rectus surgery (P=6.7E-06), and absorbable sutures (>2 PD: OR=3.7, 95% CI=0.4–35.0, P=0.19; and >5 PD: OR=6.0, 95% CI=1.1–33.5, P=0.041). Conclusions After unilateral muscle recession for hypotropia, overcorrection shifts are associated with thyroid eye disease, surgery of the inferior rectus, and use of absorbable sutures. Surgeons performing unilateral inferior rectus recession on adjustable suture in the setting of thyroid eye disease should consider using a nonabsorbable suture to reduce the incidence of postoperative overcorrection. PMID:22253487
Modeling confounding by half-sibling regression.
Schölkopf, Bernhard; Hogg, David W; Wang, Dun; Foreman-Mackey, Daniel; Janzing, Dominik; Simon-Gabriel, Carl-Johann; Peters, Jonas
2016-07-01
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
Model selection for logistic regression models
NASA Astrophysics Data System (ADS)
Duller, Christine
2012-09-01
Model selection for logistic regression models decides which of some given potential regressors have an effect and hence should be included in the final model. The second interesting question is whether a certain factor is heterogeneous among some subsets, i.e. whether the model should include a random intercept or not. In this paper these questions will be answered with classical as well as with Bayesian methods. The application show some results of recent research projects in medicine and business administration.
Differential correction schemes in nonlinear regression
NASA Technical Reports Server (NTRS)
Decell, H. P., Jr.; Speed, F. M.
1972-01-01
Classical iterative methods in nonlinear regression are reviewed and improved upon. This is accomplished by discussion of the geometrical and theoretical motivation for introducing modifications using generalized matrix inversion. Examples having inherent pitfalls are presented and compared in terms of results obtained using classical and modified techniques. The modification is shown to be useful alone or in conjunction with other modifications appearing in the literature.
Modeling confounding by half-sibling regression.
Schölkopf, Bernhard; Hogg, David W; Wang, Dun; Foreman-Mackey, Daniel; Janzing, Dominik; Simon-Gabriel, Carl-Johann; Peters, Jonas
2016-07-01
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.
Modeling confounding by half-sibling regression
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
Realization of Ridge Regression in MATLAB
NASA Astrophysics Data System (ADS)
Dimitrov, S.; Kovacheva, S.; Prodanova, K.
2008-10-01
The least square estimator (LSE) of the coefficients in the classical linear regression models is unbiased. In the case of multicollinearity of the vectors of design matrix, LSE has very big variance, i.e., the estimator is unstable. A more stable estimator (but biased) can be constructed using ridge-estimator (RE). In this paper the basic methods of obtaining of Ridge-estimators and numerical procedures of its realization in MATLAB are considered. An application to Pharmacokinetics problem is considered.
SNS shielding analyses overview
Popova, Irina; Gallmeier, Franz; Iverson, Erik B; Lu, Wei; Remec, Igor
2015-01-01
This paper gives an overview on on-going shielding analyses for Spallation Neutron Source. Presently, the most of the shielding work is concentrated on the beam lines and instrument enclosures to prepare for commissioning, save operation and adequate radiation background in the future. There is on-going work for the accelerator facility. This includes radiation-protection analyses for radiation monitors placement, designing shielding for additional facilities to test accelerator structures, redesigning some parts of the facility, and designing test facilities to the main accelerator structure for component testing. Neutronics analyses are required as well to support spent structure management, including waste characterisation analyses, choice of proper transport/storage package and shielding enhancement for the package if required.
Face Alignment via Regressing Local Binary Features.
Ren, Shaoqing; Cao, Xudong; Wei, Yichen; Sun, Jian
2016-03-01
This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.
Satellite rainfall retrieval by logistic regression
NASA Technical Reports Server (NTRS)
Chiu, Long S.
1986-01-01
The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.
General Regression and Representation Model for Classification
Qian, Jianjun; Yang, Jian; Xu, Yong
2014-01-01
Recently, the regularized coding-based classification methods (e.g. SRC and CRC) show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR) for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients) and the specific information (weight matrix of image pixels) to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel) weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR) and robust general regression and representation classifier (R-GRR). The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms. PMID:25531882
Quantile regression modeling for Malaysian automobile insurance premium data
NASA Astrophysics Data System (ADS)
Fuzi, Mohd Fadzli Mohd; Ismail, Noriszura; Jemain, Abd Aziz
2015-09-01
Quantile regression is a robust regression to outliers compared to mean regression models. Traditional mean regression models like Generalized Linear Model (GLM) are not able to capture the entire distribution of premium data. In this paper we demonstrate how a quantile regression approach can be used to model net premium data to study the effects of change in the estimates of regression parameters (rating classes) on the magnitude of response variable (pure premium). We then compare the results of quantile regression model with Gamma regression model. The results from quantile regression show that some rating classes increase as quantile increases and some decrease with decreasing quantile. Further, we found that the confidence interval of median regression (τ = O.5) is always smaller than Gamma regression in all risk factors.
Tools to support interpreting multiple regression in the face of multicollinearity.
Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K
2012-01-01
While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.
NASA Astrophysics Data System (ADS)
Bae, Gihyun; Huh, Hoon; Park, Sungho
This paper deals with a regression model for light weight and crashworthiness enhancement design of automotive parts in frontal car crash. The ULSAB-AVC model is employed for the crash analysis and effective parts are selected based on the amount of energy absorption during the crash behavior. Finite element analyses are carried out for designated design cases in order to investigate the crashworthiness and weight according to the material and thickness of main energy absorption parts. Based on simulations results, a regression analysis is performed to construct a regression model utilized for light weight and crashworthiness enhancement design of automotive parts. An example for weight reduction of main energy absorption parts demonstrates the validity of a regression model constructed.
A Hypothesis Verification Method Using Regression Tree for Semiconductor Yield Analysis
NASA Astrophysics Data System (ADS)
Tsuda, Hidetaka; Shirai, Hidehiro; Terabe, Masahiro; Hashimoto, Kazuo; Shinohara, Ayumi
Several researchers have reported the regression tree analysis for semiconductor yield. However, the scope of these analyses is restricted by the difficulty involved in applying the regression tree analysis to a small number of samples with many attributes. It is often observed that splitting attributes in the route node do not indicate the hypothesized causes of failure. We propose a method for verifying the hypothesized causes of failure, which reduces the number of verification hypotheses. Our method involves selecting sets of analysis data with the same cause of failure, extracting the hypothesis by applying the regression tree analysis separately to each set of analysis data, and merging and sorting attributes according to the t value. The results of an experiment conducted in a real environment show that the proposed method helps in widening the scope of applicability of the regression tree analysis for semiconductor yield.
Adjustable extender for instrument module
Sevec, J.B.; Stein, A.D.
1975-11-01
A blank extender module used to mount an instrument module in front of its console for repair or test purposes has been equipped with a rotatable mount and means for locking the mount at various angles of rotation for easy accessibility. The rotatable mount includes a horizontal conduit supported by bearings within the blank module. The conduit is spring-biased in a retracted position within the blank module and in this position a small gear mounted on the conduit periphery is locked by a fixed pawl. The conduit and instrument mount can be pulled into an extended position with the gear clearing the pawl to permit rotation and adjustment of the instrument.
Tso, Ivy F.; Grove, Tyler B.; Taylor, Stephan F.
2009-01-01
Background Emotion abnormalities are prominent features of schizophrenia. While the capacity for emotions is essential to social adaptation, little is known about the role of emotional experience in the social dysfunction observed in schizophrenia. Objective This study examined the contribution of emotional experience, neurocognition, and social cognition to functional outcome in schizophrenia. Method Self-reported emotional experience (anhedonia, affect intensity, emotion frequency) was assessed in 33 stable schizophrenic/schizoaffective patients and 33 healthy controls. Symptoms, neurocognition, social cognition, and functional outcome were also assessed. Results Patients and controls exhibited good internal reliability on all self-report scales, except for negative affect intensity. Patients reported equally intense but less frequent positive emotions, more intense and frequent negative emotions, and more anhedonia. Results of hierarchical regression analyses showed that emotional experience accounted for significant amounts of variance of social adjustment independent of neurocognition and social cognition. Conclusion These data show that emotional experience can be reliably assessed and is an important determinant of functional outcome in schizophrenia. PMID:20051314
Spatial quantile regression using INLA with applications to childhood overweight in Malawi.
Mtambo, Owen P L; Masangwi, Salule J; Kazembe, Lawrence N M
2015-04-01
Analyses of childhood overweight have mainly used mean regression. However, using quantile regression is more appropriate as it provides flexibility to analyse the determinants of overweight corresponding to quantiles of interest. The main objective of this study was to fit a Bayesian additive quantile regression model with structured spatial effects for childhood overweight in Malawi using the 2010 Malawi DHS data. Inference was fully Bayesian using R-INLA package. The significant determinants of childhood overweight ranged from socio-demographic factors such as type of residence to child and maternal factors such as child age and maternal BMI. We observed significant positive structured spatial effects on childhood overweight in some districts of Malawi. We recommended that the childhood malnutrition policy makers should consider timely interventions based on risk factors as identified in this paper including spatial targets of interventions.
Spatial quantile regression using INLA with applications to childhood overweight in Malawi.
Mtambo, Owen P L; Masangwi, Salule J; Kazembe, Lawrence N M
2015-04-01
Analyses of childhood overweight have mainly used mean regression. However, using quantile regression is more appropriate as it provides flexibility to analyse the determinants of overweight corresponding to quantiles of interest. The main objective of this study was to fit a Bayesian additive quantile regression model with structured spatial effects for childhood overweight in Malawi using the 2010 Malawi DHS data. Inference was fully Bayesian using R-INLA package. The significant determinants of childhood overweight ranged from socio-demographic factors such as type of residence to child and maternal factors such as child age and maternal BMI. We observed significant positive structured spatial effects on childhood overweight in some districts of Malawi. We recommended that the childhood malnutrition policy makers should consider timely interventions based on risk factors as identified in this paper including spatial targets of interventions. PMID:26046633
Recurrent Dreams and Psychosocial Adjustment in Preteenaged Children.
Gauchat, Aline; Zadra, Antonio; Tremblay, Richard E; Zelazo, Philip David; Séguin, Jean R
2009-06-01
Research indicates that recurrent dreams in adults are associated with impoverished psychological well-being. Whether similar associations exist in children remains unknown. The authors hypothesized that children reporting recurrent dreams would show poorer psychosocial adjustment than children without recurrent dreams. One hundred sixty-eight 11-year-old children self-reported on their recurrent dreams and on measures of psychosocial adjustment. Although 35% of children reported having experienced a recurrent dream during the past year, our hypothesis was only partially supported. Multivariate analyses revealed a marginally significant interaction between gender and recurrent dream presence and a significant main effect of gender. Univariate analyses revealed that boys reporting recurrent dreams reported significantly higher scores on reactive aggression than those who did not (d = 0.58). This suggests that by age 11 years, the presence of recurrent dreams may already reflect underlying emotional difficulties in boys but not necessarily in girls. Challenges in addressing this developmental question are discussed.
Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.
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.
Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.
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. PMID:27104857
ERIC Educational Resources Information Center
Ozechowski, Timothy J.; Turner, Charles W.; Hops, Hyman
2007-01-01
This article demonstrates the use of mixed-effects logistic regression (MLR) for conducting sequential analyses of binary observational data. MLR is a special case of the mixed-effects logit modeling framework, which may be applied to multicategorical observational data. The MLR approach is motivated in part by G. A. Dagne, G. W. Howe, C. H.…
Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...
Interpretation of Structure Coefficients Can Prevent Erroneous Conclusions about Regression Results.
ERIC Educational Resources Information Center
Whitaker, Jean S.
The increased use of multiple regression analysis in research warrants closer examination of the coefficients produced in these analyses, especially ones which are often ignored, such as structure coefficients. Structure coefficients are bivariate correlation coefficients between a predictor variable and the synthetic variable. When predictor…
Mapping the results of local statistics: Using geographically weighted regression
Matthews, Stephen A.; Yang, Tse-Chuan
2014-01-01
The application of geographically weighted regression (GWR) – a local spatial statistical technique used to test for spatial nonstationarity – has grown rapidly in the social, health and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity in relationships between predictors and the outcome variable. A major challenge to GWR users, however, is how best to map these parameter estimates. This paper introduces a simple mapping technique that combines local parameter estimates and local t-values on one map. The resultant map can facilitate the exploration and interpretation of nonstationarity. PMID:25578024
Meta-Analyses of the 5-HTTLPR Polymorphisms and Post-Traumatic Stress Disorder
Navarro-Mateu, Fernando; Escámez, Teresa; Koenen, Karestan C.; Alonso, Jordi; Sánchez-Meca, Julio
2013-01-01
Objective To conduct a meta-analysis of all published genetic association studies of 5-HTTLPR polymorphisms performed in PTSD cases Methods Data Sources Potential studies were identified through PubMed/MEDLINE, EMBASE, Web of Science databases (Web of Knowledge, WoK), PsychINFO, PsychArticles and HuGeNet (Human Genome Epidemiology Network) up until December 2011. Study Selection: Published observational studies reporting genotype or allele frequencies of this genetic factor in PTSD cases and in non-PTSD controls were all considered eligible for inclusion in this systematic review. Data Extraction: Two reviewers selected studies for possible inclusion and extracted data independently following a standardized protocol. Statistical analysis: A biallelic and a triallelic meta-analysis, including the total S and S' frequencies, the dominant (S+/LL and S'+/L'L') and the recessive model (SS/L+ and S'S'/L'+), was performed with a random-effect model to calculate the pooled OR and its corresponding 95% CI. Forest plots and Cochran's Q-Statistic and I2 index were calculated to check for heterogeneity. Subgroup analyses and meta-regression were carried out to analyze potential moderators. Publication bias and quality of reporting were also analyzed. Results 13 studies met our inclusion criteria, providing a total sample of 1874 patients with PTSD and 7785 controls in the biallelic meta-analyses and 627 and 3524, respectively, in the triallelic. None of the meta-analyses showed evidence of an association between 5-HTTLPR and PTSD but several characteristics (exposure to the same principal stressor for PTSD cases and controls, adjustment for potential confounding variables, blind assessment, study design, type of PTSD, ethnic distribution and Total Quality Score) influenced the results in subgroup analyses and meta-regression. There was no evidence of potential publication bias. Conclusions Current evidence does not support a direct effect of 5-HTTLPR polymorphisms on PTSD
Parenting Perfectionism and Parental Adjustment.
Lee, Meghan A; Schoppe-Sullivan, Sarah J; Kamp Dush, Claire M
2012-02-01
The parental role is expected to be one of the most gratifying and rewarding roles in life. As expectations of parenting become ever higher, the implications of parenting perfectionism for parental adjustment warrant investigation. Using longitudinal data from 182 couples, this study examined the associations between societal- and self-oriented parenting perfectionism and new mothers' and fathers' parenting self-efficacy, stress, and satisfaction. For mothers, societal-oriented parenting perfectionism was associated with lower parenting self-efficacy, but self-oriented parenting perfectionism was associated with higher parenting satisfaction. For fathers, societal-oriented parenting perfectionism was associated with higher parenting stress, whereas higher levels of self-oriented parenting perfectionism were associated with higher parenting self-efficacy, lower parenting stress, and greater parenting satisfaction. These findings support the distinction between societal- and self-oriented perfectionism, extend research on perfectionism to interpersonal adjustment in the parenting domain, and provide the first evidence for the potential consequences of holding excessively high standards for parenting. PMID:22328797
Lasso adjustments of treatment effect estimates in randomized experiments
Bloniarz, Adam; Liu, Hanzhong; Zhang, Cun-Hui; Sekhon, Jasjeet S.; Yu, Bin
2016-01-01
We provide a principled way for investigators to analyze randomized experiments when the number of covariates is large. Investigators often use linear multivariate regression to analyze randomized experiments instead of simply reporting the difference of means between treatment and control groups. Their aim is to reduce the variance of the estimated treatment effect by adjusting for covariates. If there are a large number of covariates relative to the number of observations, regression may perform poorly because of overfitting. In such cases, the least absolute shrinkage and selection operator (Lasso) may be helpful. We study the resulting Lasso-based treatment effect estimator under the Neyman–Rubin model of randomized experiments. We present theoretical conditions that guarantee that the estimator is more efficient than the simple difference-of-means estimator, and we provide a conservative estimator of the asymptotic variance, which can yield tighter confidence intervals than the difference-of-means estimator. Simulation and data examples show that Lasso-based adjustment can be advantageous even when the number of covariates is less than the number of observations. Specifically, a variant using Lasso for selection and ordinary least squares (OLS) for estimation performs particularly well, and it chooses a smoothing parameter based on combined performance of Lasso and OLS. PMID:27382153
Lasso adjustments of treatment effect estimates in randomized experiments.
Bloniarz, Adam; Liu, Hanzhong; Zhang, Cun-Hui; Sekhon, Jasjeet S; Yu, Bin
2016-07-01
We provide a principled way for investigators to analyze randomized experiments when the number of covariates is large. Investigators often use linear multivariate regression to analyze randomized experiments instead of simply reporting the difference of means between treatment and control groups. Their aim is to reduce the variance of the estimated treatment effect by adjusting for covariates. If there are a large number of covariates relative to the number of observations, regression may perform poorly because of overfitting. In such cases, the least absolute shrinkage and selection operator (Lasso) may be helpful. We study the resulting Lasso-based treatment effect estimator under the Neyman-Rubin model of randomized experiments. We present theoretical conditions that guarantee that the estimator is more efficient than the simple difference-of-means estimator, and we provide a conservative estimator of the asymptotic variance, which can yield tighter confidence intervals than the difference-of-means estimator. Simulation and data examples show that Lasso-based adjustment can be advantageous even when the number of covariates is less than the number of observations. Specifically, a variant using Lasso for selection and ordinary least squares (OLS) for estimation performs particularly well, and it chooses a smoothing parameter based on combined performance of Lasso and OLS. PMID:27382153
Adjusting the Contour of Reflector Panels
NASA Technical Reports Server (NTRS)
Palmer, W. B.; Giebler, M. M.
1984-01-01
Postfabrication adjustment of contour of panels for reflector, such as parabolic reflector for radio antennas, possible with simple mechanism consisting of threaded stud, two nuts, and flexure. Contours adjusted manually.
Research Design in Marital Adjustment Studies.
ERIC Educational Resources Information Center
Croake, James W.; Lyon, Rebecca S.
1978-01-01
The numerous marital adjustment studies which exist in the literature are confounded by basic design problems. Marital stability should be the baseline for data. It is then possible to discuss "happiness,""success,""adjustment," and "satisfaction." (Author)
Analyzing Historical Count Data: Poisson and Negative Binomial Regression Models.
ERIC Educational Resources Information Center
Beck, E. M.; Tolnay, Stewart E.
1995-01-01
Asserts that traditional approaches to multivariate analysis, including standard linear regression techniques, ignore the special character of count data. Explicates three suitable alternatives to standard regression techniques, a simple Poisson regression, a modified Poisson regression, and a negative binomial model. (MJP)
The Regression Trunk Approach to Discover Treatment Covariate Interaction
ERIC Educational Resources Information Center
Dusseldorp, Elise; Meulman, Jacqueline J.
2004-01-01
The regression trunk approach (RTA) is an integration of regression trees and multiple linear regression analysis. In this paper RTA is used to discover treatment covariate interactions, in the regression of one continuous variable on a treatment variable with "multiple" covariates. The performance of RTA is compared to the classical method of…
Generalized adjustment by least squares ( GALS).
Elassal, A.A.
1983-01-01
The least-squares principle is universally accepted as the basis for adjustment procedures in the allied fields of geodesy, photogrammetry and surveying. A prototype software package for Generalized Adjustment by Least Squares (GALS) is described. The package is designed to perform all least-squares-related functions in a typical adjustment program. GALS is capable of supporting development of adjustment programs of any size or degree of complexity. -Author
Laurin, Raphaël; Nicolas, Michel; Labruère-Chazal, Catherine; Lacassagne, Marie-Françoise
2008-08-01
The aim of this study was to develop a questionnaire to measure adjustment of teenagers at soccer training centers, particularly newcomers. The Soccer Trainee Adjustment Scale was adapted from the Institutional Integration Scale and assesses the trainee's adjustment to operating and social activities. The scale was tested on a sample of 136 trainees from four soccer centers. Exploratory analysis indicated that the 13 items formed five factors: peer adjustment, boarding supervisor adjustment, soccer adjustment, scholastic adjustment, and boarding adjustment. These factors had internal consistency reliability ranging from .76 to .94.
Support Vector Machine algorithm for regression and classification
2001-08-01
The software is an implementation of the Support Vector Machine (SVM) algorithm that was invented and developed by Vladimir Vapnik and his co-workers at AT&T Bell Laboratories. The specific implementation reported here is an Active Set method for solving a quadratic optimization problem that forms the major part of any SVM program. The implementation is tuned to specific constraints generated in the SVM learning. Thus, it is more efficient than general-purpose quadratic optimization programs. Amore » decomposition method has been implemented in the software that enables processing large data sets. The size of the learning data is virtually unlimited by the capacity of the computer physical memory. The software is flexible and extensible. Two upper bounds are implemented to regulate the SVM learning for classification, which allow users to adjust the false positive and false negative rates. The software can be used either as a standalone, general-purpose SVM regression or classification program, or be embedded into a larger software system.« less
Survival analysis of cervical cancer using stratified Cox regression
NASA Astrophysics Data System (ADS)
Purnami, S. W.; Inayati, K. D.; Sari, N. W. Wulan; Chosuvivatwong, V.; Sriplung, H.
2016-04-01
Cervical cancer is one of the mostly widely cancer cause of the women death in the world including Indonesia. Most cervical cancer patients come to the hospital already in an advanced stadium. As a result, the treatment of cervical cancer becomes more difficult and even can increase the death's risk. One of parameter that can be used to assess successfully of treatment is the probability of survival. This study raises the issue of cervical cancer survival patients at Dr. Soetomo Hospital using stratified Cox regression based on six factors such as age, stadium, treatment initiation, companion disease, complication, and anemia. Stratified Cox model is used because there is one independent variable that does not satisfy the proportional hazards assumption that is stadium. The results of the stratified Cox model show that the complication variable is significant factor which influent survival probability of cervical cancer patient. The obtained hazard ratio is 7.35. It means that cervical cancer patient who has complication is at risk of dying 7.35 times greater than patient who did not has complication. While the adjusted survival curves showed that stadium IV had the lowest probability of survival.
Support Vector Machine algorithm for regression and classification
Yu, Chenggang; Zavaljevski, Nela
2001-08-01
The software is an implementation of the Support Vector Machine (SVM) algorithm that was invented and developed by Vladimir Vapnik and his co-workers at AT&T Bell Laboratories. The specific implementation reported here is an Active Set method for solving a quadratic optimization problem that forms the major part of any SVM program. The implementation is tuned to specific constraints generated in the SVM learning. Thus, it is more efficient than general-purpose quadratic optimization programs. A decomposition method has been implemented in the software that enables processing large data sets. The size of the learning data is virtually unlimited by the capacity of the computer physical memory. The software is flexible and extensible. Two upper bounds are implemented to regulate the SVM learning for classification, which allow users to adjust the false positive and false negative rates. The software can be used either as a standalone, general-purpose SVM regression or classification program, or be embedded into a larger software system.
Evolutionary product unit based neural networks for regression.
Martínez-Estudillo, Alfonso; Martínez-Estudillo, Francisco; Hervás-Martínez, César; García-Pedrajas, Nicolás
2006-05-01
This paper presents a new method for regression based on the evolution of a type of feed-forward neural networks whose basis function units are products of the inputs raised to real number power. These nodes are usually called product units. The main advantage of product units is their capacity for implementing higher order functions. Nevertheless, the training of product unit based networks poses several problems, since local learning algorithms are not suitable for these networks due to the existence of many local minima on the error surface. Moreover, it is unclear how to establish the structure of the network since, hitherto, all learning methods described in the literature deal only with parameter adjustment. In this paper, we propose a model of evolution of product unit based networks to overcome these difficulties. The proposed model evolves both the weights and the structure of these networks by means of an evolutionary programming algorithm. The performance of the model is evaluated in five widely used benchmark functions and a hard real-world problem of microbial growth modeling. Our evolutionary model is compared to a multistart technique combined with a Levenberg-Marquardt algorithm and shows better overall performance in the benchmark functions as well as the real-world problem.
Barkhouse, K L; Van Vleck, L D; Cundiff, L V; Buchanan, D S; Marshall, D M
1998-09-01
Records on growth traits were obtained from five Midwestern agricultural experiment stations as part of a beef cattle crossbreeding project (NC-196). Records on birth weight (BWT, n =3,490), weaning weight (WWT, n = 3,237), and yearling weight (YWT, n = 1,372) were analyzed within locations and pooled across locations to obtain estimates of breed of sire differences. Solutions for breed of sire differences were adjusted to the common base year of 1993. Then, factors to use with within-breed expected progeny differences (EPD) to obtain across-breed EPD were calculated. These factors were compared with factors obtained from similar analyses of records from the U. S. Meat Animal Research Center (MARC). Progeny of Brahman sires mated to Bos taurus cows were heaviest at birth and among the lightest at weaning. Simmental and Gelbvieh sires produced the heaviest progeny at weaning. Estimates of heritability pooled across locations were .34, .19, and .07 for BWT, WWT, and YWT, respectively. Regression coefficients of progeny performance on EPD of sire were 1.25+/-.09, .98+/-.13, and .62+/-.18 for BWT, WWT, and YWT, respectively. Rankings of breeds of sire generally did not change when adjusted for sire sampling. Rankings were generally similar to those previously reported for MARC data, except for Limousin and Charolais sires, which ranked lower for BWT and WWT at NC-196 locations than at MARC. Adjustment factors used to obtain across-breed EPD were largest for Brahman for BWT and for Gelbvieh for WWT. The data for YWT allow only comparison of Angus with Simmental and of Gelbvieh with Limousin. PMID:9781484
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.
Mapping geogenic radon potential by regression kriging.
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. PMID:26706761
Monthly streamflow forecasting using Gaussian Process Regression
NASA Astrophysics Data System (ADS)
Sun, Alexander Y.; Wang, Dingbao; Xu, Xianli
2014-04-01
Streamflow forecasting plays a critical role in nearly all aspects of water resources planning and management. In this work, Gaussian Process Regression (GPR), an effective kernel-based machine learning algorithm, is applied to probabilistic streamflow forecasting. GPR is built on Gaussian process, which is a stochastic process that generalizes multivariate Gaussian distribution to infinite-dimensional space such that distributions over function values can be defined. The GPR algorithm provides a tractable and flexible hierarchical Bayesian framework for inferring the posterior distribution of streamflows. The prediction skill of the algorithm is tested for one-month-ahead prediction using the MOPEX database, which includes long-term hydrometeorological time series collected from 438 basins across the U.S. from 1948 to 2003. Comparisons with linear regression and artificial neural network models indicate that GPR outperforms both regression methods in most cases. The GPR prediction of MOPEX basins is further examined using the Budyko framework, which helps to reveal the close relationships among water-energy partitions, hydrologic similarity, and predictability. Flow regime modification and the resulting loss of predictability have been a major concern in recent years because of climate change and anthropogenic activities. The persistence of streamflow predictability is thus examined by extending the original MOPEX data records to 2012. Results indicate relatively strong persistence of streamflow predictability in the extended period, although the low-predictability basins tend to show more variations. Because many low-predictability basins are located in regions experiencing fast growth of human activities, the significance of sustainable development and water resources management can be even greater for those regions.
Mapping geogenic radon potential by regression kriging.
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.
NASA Technical Reports Server (NTRS)
Slivon, L. E.; Hernon-Kenny, L. A.; Katona, V. R.; Dejarme, L. E.
1995-01-01
This report describes analytical methods and results obtained from chemical analysis of 31 charcoal samples in five sets. Each set was obtained from a single scrubber used to filter ambient air on board a Spacelab mission. Analysis of the charcoal samples was conducted by thermal desorption followed by gas chromatography/mass spectrometry (GC/MS). All samples were analyzed using identical methods. The method used for these analyses was able to detect compounds independent of their polarity or volatility. In addition to the charcoal samples, analyses of three Environmental Control and Life Support System (ECLSS) water samples were conducted specifically for trimethylamine.
Convex Regression with Interpretable Sharp Partitions
Petersen, Ashley; Simon, Noah; Witten, Daniela
2016-01-01
We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We propose convex regression with interpretable sharp partitions (CRISP) for this task. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. We explore the properties of CRISP, and evaluate its performance in a simulation study and on a housing price data set.
A method for nonlinear exponential regression analysis
NASA Technical Reports Server (NTRS)
Junkin, B. G.
1971-01-01
A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.
SPE dose prediction using locally weighted regression.
Hines, J W; Townsend, L W; Nichols, T F
2005-01-01
When astronauts are outside Earth's protective magnetosphere, they are subject to large radiation doses resulting from solar particle events. The total dose received from a major solar particle event in deep space could cause severe radiation poisoning. The dose is usually received over a 20-40 h time interval but the event's effects may be reduced with an early warning system. This paper presents a method to predict the total dose early in the event. It uses a locally weighted regression model, which is easier to train, and provides predictions as accurate as the neural network models that were used previously. PMID:16604613
An operational GLS model for hydrologic regression
Tasker, Gary D.; Stedinger, J.R.
1989-01-01
Recent Monte Carlo studies have documented the value of generalized least squares (GLS) procedures to estimate empirical relationships between streamflow statistics and physiographic basin characteristics. This paper presents a number of extensions of the GLS method that deal with realities and complexities of regional hydrologic data sets that were not addressed in the simulation studies. These extensions include: (1) a more realistic model of the underlying model errors; (2) smoothed estimates of cross correlation of flows; (3) procedures for including historical flow data; (4) diagnostic statistics describing leverage and influence for GLS regression; and (5) the formulation of a mathematical program for evaluating future gaging activities. ?? 1989.
Convex Regression with Interpretable Sharp Partitions
Petersen, Ashley; Simon, Noah; Witten, Daniela
2016-01-01
We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We propose convex regression with interpretable sharp partitions (CRISP) for this task. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. We explore the properties of CRISP, and evaluate its performance in a simulation study and on a housing price data set. PMID:27635120
Triton,... electron,... cosmon,...: An infinite regression?
Dehmelt, H
1989-11-01
I propose an elementary particle model in which the simplest near-Dirac particles triton, proton, and electron are members of the three top layers of a bottomless stack. Each particle is a composite of three particles from the next layer below in an infinite regression approaching Dirac point particles. The cosmon, an immensely heavy lower layer subquark, is the elementary particle. The world-atom, a tightly bound cosmon/anticosmon pair of zero relativistic total mass, arose from the nothing state in a quantum jump. Rapid decay of the pair launched the big bang and created the universe. PMID:16594084
Significant Scoliosis Regression following Syringomyelia Decompression
Mollano, Anthony V; Weinstein, Stuart L; Menezes, Arnold H
2005-01-01
We present the case of a 5-year-old boy presenting with a 54-degree scoliosis secondary to a Chiari I malformation with a holocord syringomyelia extending from C1 to T10. Neurosurgical treatment involved posterior fossa craniectomy with decompression, and partial C1 laminectomy. At follow-up 7 years later, at age 12, radiographs revealed only a 4-degree scoliosis, and follow-up MRI revealed a deflated syrinx. We report this case to reveal the most significant scoliosis regression seen in our experience that may occur in younger patients after neurosurgical syringomyelia decompression for Chiari I hindbrain herniation. PMID:16089074
ERIC Educational Resources Information Center
Proper, Elizabeth C.; And Others
This segment of the national evaluation study of the Follow Through Planned Variation Model discusses findings of analyses of achievement test data which have been adjusted to take into consideration the preschool experience of children in three Follow Through cohorts. These analyses serve as a supplement to analyses presented in Volume IV-A of…
19 CFR 201.205 - Salary adjustments.
Code of Federal Regulations, 2011 CFR
2011-04-01
... 19 Customs Duties 3 2011-04-01 2011-04-01 false Salary adjustments. 201.205 Section 201.205 Customs Duties UNITED STATES INTERNATIONAL TRADE COMMISSION GENERAL RULES OF GENERAL APPLICATION Debt Collection § 201.205 Salary adjustments. Any negative adjustment to pay arising out of an employee's...
19 CFR 201.205 - Salary adjustments.
Code of Federal Regulations, 2010 CFR
2010-04-01
... 19 Customs Duties 3 2010-04-01 2010-04-01 false Salary adjustments. 201.205 Section 201.205 Customs Duties UNITED STATES INTERNATIONAL TRADE COMMISSION GENERAL RULES OF GENERAL APPLICATION Debt Collection § 201.205 Salary adjustments. Any negative adjustment to pay arising out of an employee's...
24 CFR 5.611 - Adjusted income.
Code of Federal Regulations, 2010 CFR
2010-04-01
... 24 Housing and Urban Development 1 2010-04-01 2010-04-01 false Adjusted income. 5.611 Section 5... Serving Persons with Disabilities: Family Income and Family Payment; Occupancy Requirements for Section 8 Project-Based Assistance Family Income § 5.611 Adjusted income. Adjusted income means annual income...
34 CFR 36.2 - Penalty adjustment.
Code of Federal Regulations, 2014 CFR
2014-07-01
... 34 Education 1 2014-07-01 2014-07-01 false Penalty adjustment. 36.2 Section 36.2 Education Office of the Secretary, Department of Education ADJUSTMENT OF CIVIL MONETARY PENALTIES FOR INFLATION § 36.2..., Section 36.2—Civil Monetary Penalty Inflation Adjustments Statute Description New maximum (and minimum,...
34 CFR 36.2 - Penalty adjustment.
Code of Federal Regulations, 2011 CFR
2011-07-01
... 34 Education 1 2011-07-01 2011-07-01 false Penalty adjustment. 36.2 Section 36.2 Education Office of the Secretary, Department of Education ADJUSTMENT OF CIVIL MONETARY PENALTIES FOR INFLATION § 36.2..., Section 36.2—Civil Monetary Penalty Inflation Adjustments Statute Description New maximum (and minimum,...
34 CFR 36.2 - Penalty adjustment.
Code of Federal Regulations, 2010 CFR
2010-07-01
... 34 Education 1 2010-07-01 2010-07-01 false Penalty adjustment. 36.2 Section 36.2 Education Office of the Secretary, Department of Education ADJUSTMENT OF CIVIL MONETARY PENALTIES FOR INFLATION § 36.2..., Section 36.2—Civil Monetary Penalty Inflation Adjustments Statute Description New maximum (and minimum,...
34 CFR 36.2 - Penalty adjustment.
Code of Federal Regulations, 2013 CFR
2013-07-01
... 34 Education 1 2013-07-01 2013-07-01 false Penalty adjustment. 36.2 Section 36.2 Education Office of the Secretary, Department of Education ADJUSTMENT OF CIVIL MONETARY PENALTIES FOR INFLATION § 36.2..., Section 36.2—Civil Monetary Penalty Inflation Adjustments Statute Description New maximum (and minimum,...
12 CFR 1780.80 - Inflation adjustments.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 12 Banks and Banking 7 2011-01-01 2011-01-01 false Inflation adjustments. 1780.80 Section 1780.80... DEVELOPMENT RULES OF PRACTICE AND PROCEDURE RULES OF PRACTICE AND PROCEDURE Civil Money Penalty Inflation Adjustments § 1780.80 Inflation adjustments. The maximum amount of each civil money penalty within...
12 CFR 1780.80 - Inflation adjustments.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 12 Banks and Banking 7 2010-01-01 2010-01-01 false Inflation adjustments. 1780.80 Section 1780.80... DEVELOPMENT RULES OF PRACTICE AND PROCEDURE RULES OF PRACTICE AND PROCEDURE Civil Money Penalty Inflation Adjustments § 1780.80 Inflation adjustments. The maximum amount of each civil money penalty within...
The survival analyses of 2738 patients with simple pneumoconiosis.
Yi, Q; Zhang, Z
1996-01-01
OBJECTIVES: To explore whether the inhalation of coal mine dust increases the risk of premature death in miners, a survival analysis was conducted in a cohort of 2738 patients with simple pneumoconiosis in the Huai-Bei coal mine, in China. METHODS: Age specific mortalities were calculated by disease severity in terms of pneumoconiotic category with the life table method. The progressions from simple pneumoconiosis to death or progressive massive fibrosis (PMF) were analysed with the Cox's regression model with time as the dependent variable to identify risk factors. RESULTS: During a follow up period (mean 8 y) 3.2% of patients with simple pneumoconiosis developed PMF. The patients with development of PMF presented higher age specific mortalities than those remaining in a state of simple pneumoconiosis (SMR: 3.42; P < 0.01). After adjustment for tuberculosis and duration of work, the relative risk of premature death due to development of PMF was 2.4. Tuberculosis was found to be a main risk factor which not only facilitated premature death (relative risk (RR): 2.0; P < 0.01), but was also a strong facilitator for development of PMF (RR: 7.0; P < 0.01). Also, a long term of work underground and drilling as a main job were identified as risk factors for development of PMF. CONCLUSION: The results imply that patients with simple pneumoconiosis will have altered survival, and premature death among them is related to an increased risk of the development of PMF and the complication of tuberculosis. PMID:8777450
ERIC Educational Resources Information Center
López-López, José Antonio; Botella, Juan; Sánchez-Meca, Julio; Marín-Martínez, Fulgencio
2013-01-01
Since heterogeneity between reliability coefficients is usually found in reliability generalization studies, moderator analyses constitute a crucial step for that meta-analytic approach. In this study, different procedures for conducting mixed-effects meta-regression analyses were compared. Specifically, four transformation methods for the…
ERIC Educational Resources Information Center
Giannotti, Flavia; Cortesi, Flavia; Cerquiglini, Antonella; Miraglia, Daniela; Vagnoni, Cristina; Sebastiani, Teresa; Bernabei, Paola
2008-01-01
This study investigated sleep of children with autism and developmental regression and the possible relationship with epilepsy and epileptiform abnormalities. Participants were 104 children with autism (70 non-regressed, 34 regressed) and 162 typically developing children (TD). Results suggested that the regressed group had higher incidence of…
Wavelet Analyses and Applications
ERIC Educational Resources Information Center
Bordeianu, Cristian C.; Landau, Rubin H.; Paez, Manuel J.
2009-01-01
It is shown how a modern extension of Fourier analysis known as wavelet analysis is applied to signals containing multiscale information. First, a continuous wavelet transform is used to analyse the spectrum of a nonstationary signal (one whose form changes in time). The spectral analysis of such a signal gives the strength of the signal in each…
NASA Technical Reports Server (NTRS)
Taylor, G. R.
1972-01-01
Extensive microbiological analyses that were performed on the Apollo 14 prime and backup crewmembers and ancillary personnel are discussed. The crewmembers were subjected to four separate and quite different environments during the 137-day monitoring period. The relation between each of these environments and observed changes in the microflora of each astronaut are presented.
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%.
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.