Poisson Mixture Regression Models for Heart Disease Prediction.

Science.gov (United States)

Mufudza, Chipo; Erol, Hamza

2016-01-01

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

Poisson Mixture Regression Models for Heart Disease Prediction

Science.gov (United States)

Erol, Hamza

2016-01-01

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

Mixture of Regression Models with Single-Index

OpenAIRE

Xiang, Sijia; Yao, Weixin

2016-01-01

In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for...

An Additive-Multiplicative Cox-Aalen Regression Model

DEFF Research Database (Denmark)

Scheike, Thomas H.; Zhang, Mei-Jie

2002-01-01

Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...

[From clinical judgment to linear regression model.

Science.gov (United States)

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.

Regression models of reactor diagnostic signals

International Nuclear Information System (INIS)

Vavrin, J.

1989-01-01

The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)

Forecasting with Dynamic Regression Models

CERN Document Server

Pankratz, Alan

2012-01-01

One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.

Animal models of exercise and obesity.

Science.gov (United States)

Kasper, Christine E

2013-01-01

Animal models have been invaluable in the conduct of nursing research for the past 40 years. This review will focus on specific animal models that can be used in nursing research to study the physiologic phenomena of exercise and obesity when the use of human subjects is either scientifically premature or inappropriate because of the need for sampling tissue or the conduct of longitudinal studies of aging. There exists an extensive body of literature reporting the experimental use of various animal models, in both exercise science and the study of the mechanisms of obesity. Many of these studies are focused on the molecular and genetic mechanisms of organ system adaptation and plasticity in response to exercise, obesity, or both. However, this review will narrowly focus on the models useful to nursing research in the study of exercise in the clinical context of increasing performance and mobility, atrophy and bedrest, fatigue, and aging. Animal models of obesity focus on those that best approximate clinical pathology.

Categorical regression dose-response modeling

Science.gov (United States)

The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...

Mixed-effects regression models in linguistics

CERN Document Server

Heylen, Kris; Geeraerts, Dirk

2018-01-01

When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed.  In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addres...

Moderation analysis using a two-level regression model.

Science.gov (United States)

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

Variable selection and model choice in geoadditive regression models.

Science.gov (United States)

Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

The MIDAS Touch: Mixed Data Sampling Regression Models

OpenAIRE

Ghysels, Eric; Santa-Clara, Pedro; Valkanov, Rossen

2004-01-01

We introduce Mixed Data Sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Technically speaking MIDAS models specify conditional expectations as a distributed lag of regressors recorded at some higher sampling frequencies. We examine the asymptotic properties of MIDAS regression estimation and compare it with traditional distributed lag models. MIDAS regressions have wide applicability in macroeconomics and �nance.

Regression analysis for the social sciences

CERN Document Server

Gordon, Rachel A

2010-01-01

The book provides graduate students in the social sciences with the basic skills that they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.

Longitudinal associations between exercise identity and exercise motivation: A multilevel growth curve model approach.

Science.gov (United States)

Ntoumanis, N; Stenling, A; Thøgersen-Ntoumani, C; Vlachopoulos, S; Lindwall, M; Gucciardi, D F; Tsakonitis, C

2018-02-01

Past work linking exercise identity and exercise motivation has been cross-sectional. This is the first study to model the relations between different types of exercise identity and exercise motivation longitudinally. Understanding the dynamic associations between these sets of variables has implications for theory development and applied research. This was a longitudinal survey study. Participants were 180 exercisers (79 men, 101 women) from Greece, who were recruited from fitness centers and were asked to complete questionnaires assessing exercise identity (exercise beliefs and role-identity) and exercise motivation (intrinsic, identified, introjected, external motivation, and amotivation) three times within a 6 month period. Multilevel growth curve modeling examined the role of motivational regulations as within- and between-level predictors of exercise identity, and a model in which exercise identity predicted exercise motivation at the within- and between-person levels. Results showed that within-person changes in intrinsic motivation, introjected, and identified regulations were positively and reciprocally related to within-person changes in exercise beliefs; intrinsic motivation was also a positive predictor of within-person changes in role-identity but not vice versa. Between-person differences in the means of predictor variables were predictive of initial levels and average rates of change in the outcome variables. The findings show support to the proposition that a strong exercise identity (particularly exercise beliefs) can foster motivation for behaviors that reinforce this identity. We also demonstrate that such relations can be reciprocal overtime and can depend on the type of motivation in question as well as between-person differences in absolute levels of these variables. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Introduction to the use of regression models in epidemiology.

Science.gov (United States)

Bender, Ralf

2009-01-01

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

Real estate value prediction using multivariate regression models

Science.gov (United States)

Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

2017-11-01

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

Model-based Quantile Regression for Discrete Data

KAUST Repository

Padellini, Tullia

2018-04-10

Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite the fact that this leads to a proper posterior for the regression coefficients, the resulting posterior variance is however affected by an unidentifiable parameter, hence any inferential procedure beside point estimation is unreliable. We propose a model-based approach for quantile regression that considers quantiles of the generating distribution directly, and thus allows for a proper uncertainty quantification. We then create a link between quantile regression and generalised linear models by mapping the quantiles to the parameter of the response variable, and we exploit it to fit the model with R-INLA. We extend it also in the case of discrete responses, where there is no 1-to-1 relationship between quantiles and distribution\\'s parameter, by introducing continuous generalisations of the most common discrete variables (Poisson, Binomial and Negative Binomial) to be exploited in the fitting.

Variable importance in latent variable regression models

NARCIS (Netherlands)

Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.

2014-01-01

The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable

Regression analysis for the social sciences

CERN Document Server

Gordon, Rachel A

2015-01-01

Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.

Exercise identity as a risk factor for exercise dependence.

Science.gov (United States)

Murray, Aja L; McKenzie, Karen; Newman, Emily; Brown, Erin

2013-05-01

The aim of the study was to explore the relationship between exercise identity and exercise dependence. We hypothesized that stronger exercise identities would be associated with greater odds of experiencing exercise dependence symptoms. Logistic regression was used to assess the extent of association between exercise identity and the risk of experiencing exercise dependence symptoms. Participants (101) were recruited online via sports clubs and social networking sites and were asked to complete online measures of exercise identity and exercise dependence. The overall model fit was a significant improvement on the baseline model, but only the exercise beliefs factor was significantly associated with the odds of dependence symptoms, with higher scores on the belief scale predicting greater odds of experiencing dependence symptoms. Exercise role identity, in contrast, was not significantly associated with odds of experiencing dependence symptoms. Per cent correct classification was 55.9% for asymptomatic and 88.2% for symptomatic individuals and the overall per cent correct classification was 77.5%. The relation between identity and dependence could represent both a fruitful research avenue and a potential therapeutic target for those experiencing dependence symptoms; although our findings only showed a relationship between one of the two factors of the exercise identity measure and dependence. Longitudinal research is required to examine the relationship between identity and dependence in the context of other variables to better understand why some individuals become exercise dependent whereas others do not. What is already known on this subject? Exercise identity has been identified as an important determinant of exercise behaviour and studies within the exercise identity framework have proven elucidative with respect to the psychological processes that may underpin commitment to exercise. It has separately been established that some individuals may become

A simple approach to power and sample size calculations in logistic regression and Cox regression models.

Science.gov (United States)

Vaeth, Michael; Skovlund, Eva

2004-06-15

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.

Regression modeling methods, theory, and computation with SAS

CERN Document Server

Panik, Michael

2009-01-01

Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,

Using social cognitive theory to explain discretionary, "leisure-time" physical exercise among high school students.

Science.gov (United States)

Winters, Eric R; Petosa, Rick L; Charlton, Thomas E

2003-06-01

To examine whether knowledge of high school students' actions of self-regulation, and perceptions of self-efficacy to overcome exercise barriers, social situation, and outcome expectation will predict non-school related moderate and vigorous physical exercise. High school students enrolled in introductory Physical Education courses completed questionnaires that targeted selected Social Cognitive Theory variables. They also self-reported their typical "leisure-time" exercise participation using a standardized questionnaire. Bivariate correlation statistic and hierarchical regression were conducted on reports of moderate and vigorous exercise frequency. Each predictor variable was significantly associated with measures of moderate and vigorous exercise frequency. All predictor variables were significant in the final regression model used to explain vigorous exercise. After controlling for the effects of gender, the psychosocial variables explained 29% of variance in vigorous exercise frequency. Three of four predictor variables were significant in the final regression equation used to explain moderate exercise. The final regression equation accounted for 11% of variance in moderate exercise frequency. Professionals who attempt to increase the prevalence of physical exercise through educational methods should focus on the psychosocial variables utilized in this study.

Regression Models For Multivariate Count Data.

Science.gov (United States)

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

Self-determined to exercise? Leisure-time exercise behavior, exercise motivation, and exercise dependence in youth.

Science.gov (United States)

Symons Downs, Danielle; Savage, Jennifer S; DiNallo, Jennifer M

2013-02-01

Scant research has examined the determinants of primary exercise dependence symptoms in youth. Study purposes were to examine sex differences across leisure-time exercise behavior, motivation, and primary exercise dependence symptoms in youth and the extent to which exercise behavior and motivation predicted exercise dependence within the Self-Determination Theory framework. Adolescents (N = 805; mean age = 15 years; 46% girls) completed measures of exercise behavior, motivation, and exercise dependence in health/PE classes. One-way ANOVA revealed boys scored higher than girls on leisure-time exercise behavior, exercise dependence symptoms, and most of the exercise motivation subscales. Hierarchical regression analyses indicated a) sex, exercise behavior, motivation, and their interaction terms explained 39% of the variance in primary exercise dependence; b) Integrated Regulation and Introjected Regulation were important determinants of exercise dependence; and c) sex moderated the contributions of External Regulation for predicting exercise dependence such that boys in the high and low external regulation groups had higher symptoms than girls in the high and low external regulation groups. These preliminary findings support the controlled dimensions of Integrated Regulation (boys, girls), Introjected Regulation (boys, girls), and External Regulation (boys only) are important determinants of primary exercise dependence symptoms.

ERRICCA radon model intercomparison exercise

DEFF Research Database (Denmark)

Andersen, C.E.; Albarracín, D.; Csige, I.

1999-01-01

-state diffusive radon profiles in dry and wet soils, (2) steady-state entry of soil gas and radon into a house, (3) time-dependent radon exhalation from abuilding-material sample. These cases cover features such as: soil heterogeneity, anisotropy, 3D-effects, time dependency, combined advective and diffusive......, still remain. All in all, it seems that the exercise has served its purpose and stimulated improvements relating to the quality of numerical modelling of radon transport. To maintain a high quality of modelling, it is recommendedthat additional exercises are carried out....

A model to predict multivessel coronary artery disease from the exercise thallium-201 stress test

International Nuclear Information System (INIS)

Pollock, S.G.; Abbott, R.D.; Boucher, C.A.; Watson, D.D.; Kaul, S.

1991-01-01

The aim of this study was to (1) determine whether nonimaging variables add to the diagnostic information available from exercise thallium-201 images for the detection of multivessel coronary artery disease; and (2) to develop a model based on the exercise thallium-201 stress test to predict the presence of multivessel disease. The study populations included 383 patients referred to the University of Virginia and 325 patients referred to the Massachusetts General Hospital for evaluation of chest pain. All patients underwent both cardiac catheterization and exercise thallium-201 stress testing between 1978 and 1981. In the University of Virginia cohort, at each level of thallium-201 abnormality (no defects, one defect, more than one defect), ST depression and patient age added significantly in the detection of multivessel disease. Logistic regression analysis using data from these patients identified three independent predictors of multivessel disease: initial thallium-201 defects, ST depression, and age. A model was developed to predict multivessel disease based on these variables. As might be expected, the risk of multivessel disease predicted by the model was similar to that actually observed in the University of Virginia population. More importantly, however, the model was accurate in predicting the occurrence of multivessel disease in the unrelated population studied at the Massachusetts General Hospital. It is, therefore, concluded that (1) nonimaging variables (age and exercise-induced ST depression) add independent information to thallium-201 imaging data in the detection of multivessel disease; and (2) a model has been developed based on the exercise thallium-201 stress test that can accurately predict the probability of multivessel disease in other populations

Nonparametric Mixture of Regression Models.

Science.gov (United States)

Huang, Mian; Li, Runze; Wang, Shaoli

2013-07-01

Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.

Gaussian Process Regression Model in Spatial Logistic Regression

Science.gov (United States)

Sofro, A.; Oktaviarina, A.

2018-01-01

Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

International Nuclear Information System (INIS)

Jafri, Y.Z.; Kamal, L.

2007-01-01

Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)

Robust mislabel logistic regression without modeling mislabel probabilities.

Science.gov (United States)

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

Mixed Frequency Data Sampling Regression Models: The R Package midasr

Directory of Open Access Journals (Sweden)

Eric Ghysels

2016-08-01

Full Text Available When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002. In this article we define a general autoregressive MIDAS regression model with multiple variables of different frequencies and show how it can be specified using the familiar R formula interface and estimated using various optimization methods chosen by the researcher. We discuss how to check the validity of the estimated model both in terms of numerical convergence and statistical adequacy of a chosen regression specification, how to perform model selection based on a information criterion, how to assess forecasting accuracy of the MIDAS regression model and how to obtain a forecast aggregation of different MIDAS regression models. We illustrate the capabilities of the package with a simulated MIDAS regression model and give two empirical examples of application of MIDAS regression.

Impact of multicollinearity on small sample hydrologic regression models

Science.gov (United States)

Kroll, Charles N.; Song, Peter

2013-06-01

Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.

Applied Regression Modeling A Business Approach

CERN Document Server

Pardoe, Iain

2012-01-01

An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a

Testing homogeneity in Weibull-regression models.

Science.gov (United States)

Bolfarine, Heleno; Valença, Dione M

2005-10-01

In survival studies with families or geographical units it may be of interest testing whether such groups are homogeneous for given explanatory variables. In this paper we consider score type tests for group homogeneity based on a mixing model in which the group effect is modelled as a random variable. As opposed to hazard-based frailty models, this model presents survival times that conditioned on the random effect, has an accelerated failure time representation. The test statistics requires only estimation of the conventional regression model without the random effect and does not require specifying the distribution of the random effect. The tests are derived for a Weibull regression model and in the uncensored situation, a closed form is obtained for the test statistic. A simulation study is used for comparing the power of the tests. The proposed tests are applied to real data sets with censored data.

Model-based Quantile Regression for Discrete Data

KAUST Repository

Padellini, Tullia; Rue, Haavard

2018-01-01

Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite

Detection of epistatic effects with logic regression and a classical linear regression model.

Science.gov (United States)

Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

2014-02-01

To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION

OpenAIRE

Krzyśko, Mirosław; Smaga, Łukasz

2017-01-01

In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...

Exercise identity and attribution properties predict negative self-conscious emotions for exercise relapse.

Science.gov (United States)

Flora, Parminder K; Strachan, Shaelyn M; Brawley, Lawrence R; Spink, Kevin S

2012-10-01

Research on exercise identity (EXID) indicates that it is related to negative affect when exercisers are inconsistent or relapse. Although identity theory suggests that causal attributions about this inconsistency elicit negative self-conscious emotions of shame and guilt, no EXID studies have examined this for exercise relapse. Weiner's attribution-based theory of interpersonal motivation (2010) offers a means of testing the attribution-emotion link. Using both frameworks, we examined whether EXID and attributional properties predicted negative emotions for exercise relapse. Participants (n = 224) read an exercise relapse vignette, and then completed EXID, attributions, and emotion measures. Hierarchical multiple regression models using EXID and the attributional property of controllability significantly predicted each of shame and guilt, R² adjusted = .09, ps ≤ .001. Results support identity theory suggestions and Weiner's specific attribution-emotion hypothesis. This first demonstration of an interlinking of EXID, controllability, and negative self-conscious emotions offers more predictive utility using complementary theories than either theory alone.

Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

Science.gov (United States)

Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

2017-06-01

A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

Predictors of exercise capacity following exercise-based rehabilitation in patients with coronary heart disease and heart failure

DEFF Research Database (Denmark)

Uddin, Jamal; Zwisler, Ann-Dorthe; Lewinter, Christian

2016-01-01

.76-1.41) standard deviation units higher, and in trials reporting maximum oxygen uptake (VO2max) was 3.3 ml/kg.min(-1) (95% CI: 2.6-4.0) higher. There was evidence of a high level of statistical heterogeneity across trials (I(2) statistic > 50%). In multivariable meta-regression analysis, only exercise intervention......BACKGROUND: The aim of this study was to undertake a comprehensive assessment of the patient, intervention and trial-level factors that may predict exercise capacity following exercise-based rehabilitation in patients with coronary heart disease and heart failure. DESIGN: Meta-analysis and meta-regression...... analysis. METHODS: Randomized controlled trials of exercise-based rehabilitation were identified from three published systematic reviews. Exercise capacity was pooled across trials using random effects meta-analysis, and meta-regression used to examine the association between exercise capacity and a range...

Exercise promotion: an integration of exercise self-identity, beliefs, intention, and behaviour

NARCIS (Netherlands)

de Bruijn, G.-J.; van den Putte, B.

2012-01-01

We explored the role of exercise self-identity within the framework of the theory of planned behaviour (TPB). Participants were 538 undergraduate students who completed measures of exercise self-identity, exercise behaviour, TPB items, and behavioural and control beliefs. Regression analysis showed

Prognostic value of exercise capacity among patients with treated depression: The Henry Ford Exercise Testing (FIT) Project.

Science.gov (United States)

Ahmed, Amjad M; Qureshi, Waqas T; Sakr, Sherif; Blaha, Michael J; Brawner, Clinton A; Ehrman, Jonathan K; Keteyian, Steven J; Al-Mallah, Mouaz H

2018-04-01

Exercise capacity is associated with survival in the general population. Whether this applies to patients with treated depression is not clear. High exercise capacity remains associated with lower risk of all-cause mortality (ACM) and nonfatal myocardial infraction (MI) among patients with treated depression. We included 5128 patients on antidepressant medications who completed a clinically indicated exercise stress test between 1991 and 2009. Patients were followed for a median duration of 9.4 years for ACM and 4.5 years for MI. Exercise capacity was estimated in metabolic equivalents of tasks (METs). Cox proportional hazards regression models were used. Patients with treated depression who achieved ≥12 METs (vs those achieving model, exercise capacity was associated with a lower ACM (HR per 1-MET increase in exercise capacity: 0.82, 95% CI: 0.79-0.85, P capacity had an inverse association with both ACM and nonfatal MI in patients with treated depression, independent of cardiovascular risk factors. These results highlight the potential impact of assessing exercise capacity to identify risk, as well as promoting an active lifestyle among treated depression patients. © 2018 Wiley Periodicals, Inc.

Alternative regression models to assess increase in childhood BMI.

Science.gov (United States)

Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M

2008-09-08

Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.

The influence of training characteristics on the effect of aerobic exercise training in patients with chronic heart failure: A meta-regression analysis.

Science.gov (United States)

Vromen, T; Kraal, J J; Kuiper, J; Spee, R F; Peek, N; Kemps, H M

2016-04-01

Although aerobic exercise training has shown to be an effective treatment for chronic heart failure patients, there has been a debate about the design of training programs and which training characteristics are the strongest determinants of improvement in exercise capacity. Therefore, we performed a meta-regression analysis to determine a ranking of the individual effect of the training characteristics on the improvement in exercise capacity of an aerobic exercise training program in chronic heart failure patients. We focused on four training characteristics; session frequency, session duration, training intensity and program length, and their product; total energy expenditure. A systematic literature search was performed for randomized controlled trials comparing continuous aerobic exercise training with usual care. Seventeen unique articles were included in our analysis. Total energy expenditure appeared the only training characteristic with a significant effect on improvement in exercise capacity. However, the results were strongly dominated by one trial (HF-action trial), accounting for 90% of the total patient population and showing controversial results compared to other studies. A repeated analysis excluding the HF-action trial confirmed that the increase in exercise capacity is primarily determined by total energy expenditure, followed by session frequency, session duration and session intensity. These results suggest that the design of a training program requires high total energy expenditure as a main goal. Increases in training frequency and session duration appear to yield the largest improvement in exercise capacity. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Alternative regression models to assess increase in childhood BMI

OpenAIRE

Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M

2008-01-01

Abstract Background Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 childre...

Thermal Efficiency Degradation Diagnosis Method Using Regression Model

International Nuclear Information System (INIS)

Jee, Chang Hyun; Heo, Gyun Young; Jang, Seok Won; Lee, In Cheol

2011-01-01

This paper proposes an idea for thermal efficiency degradation diagnosis in turbine cycles, which is based on turbine cycle simulation under abnormal conditions and a linear regression model. The correlation between the inputs for representing degradation conditions (normally unmeasured but intrinsic states) and the simulation outputs (normally measured but superficial states) was analyzed with the linear regression model. The regression models can inversely response an associated intrinsic state for a superficial state observed from a power plant. The diagnosis method proposed herein is classified into three processes, 1) simulations for degradation conditions to get measured states (referred as what-if method), 2) development of the linear model correlating intrinsic and superficial states, and 3) determination of an intrinsic state using the superficial states of current plant and the linear regression model (referred as inverse what-if method). The what-if method is to generate the outputs for the inputs including various root causes and/or boundary conditions whereas the inverse what-if method is the process of calculating the inverse matrix with the given superficial states, that is, component degradation modes. The method suggested in this paper was validated using the turbine cycle model for an operating power plant

Random regression models for detection of gene by environment interaction

Directory of Open Access Journals (Sweden)

Meuwissen Theo HE

2007-02-01

Full Text Available Abstract Two random regression models, where the effect of a putative QTL was regressed on an environmental gradient, are described. The first model estimates the correlation between intercept and slope of the random regression, while the other model restricts this correlation to 1 or -1, which is expected under a bi-allelic QTL model. The random regression models were compared to a model assuming no gene by environment interactions. The comparison was done with regards to the models ability to detect QTL, to position them accurately and to detect possible QTL by environment interactions. A simulation study based on a granddaughter design was conducted, and QTL were assumed, either by assigning an effect independent of the environment or as a linear function of a simulated environmental gradient. It was concluded that the random regression models were suitable for detection of QTL effects, in the presence and absence of interactions with environmental gradients. Fixing the correlation between intercept and slope of the random regression had a positive effect on power when the QTL effects re-ranked between environments.

Alternative regression models to assess increase in childhood BMI

Directory of Open Access Journals (Sweden)

Mansmann Ulrich

2008-09-01

Full Text Available Abstract Background Body mass index (BMI data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs, quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS. We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. Results GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. Conclusion GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.

The microcomputer scientific software series 2: general linear model--regression.

Science.gov (United States)

Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

Wavelet regression model in forecasting crude oil price

Science.gov (United States)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Predicting and Modelling of Survival Data when Cox's Regression Model does not hold

DEFF Research Database (Denmark)

Scheike, Thomas H.; Zhang, Mei-Jie

2002-01-01

Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects...

Spatial stochastic regression modelling of urban land use

International Nuclear Information System (INIS)

Arshad, S H M; Jaafar, J; Abiden, M Z Z; Latif, Z A; Rasam, A R A

2014-01-01

Urbanization is very closely linked to industrialization, commercialization or overall economic growth and development. This results in innumerable benefits of the quantity and quality of the urban environment and lifestyle but on the other hand contributes to unbounded development, urban sprawl, overcrowding and decreasing standard of living. Regulation and observation of urban development activities is crucial. The understanding of urban systems that promotes urban growth are also essential for the purpose of policy making, formulating development strategies as well as development plan preparation. This study aims to compare two different stochastic regression modeling techniques for spatial structure models of urban growth in the same specific study area. Both techniques will utilize the same datasets and their results will be analyzed. The work starts by producing an urban growth model by using stochastic regression modeling techniques namely the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). The two techniques are compared to and it is found that, GWR seems to be a more significant stochastic regression model compared to OLS, it gives a smaller AICc (Akaike's Information Corrected Criterion) value and its output is more spatially explainable

Physics constrained nonlinear regression models for time series

International Nuclear Information System (INIS)

Majda, Andrew J; Harlim, John

2013-01-01

A central issue in contemporary science is the development of data driven statistical nonlinear dynamical models for time series of partial observations of nature or a complex physical model. It has been established recently that ad hoc quadratic multi-level regression (MLR) models can have finite-time blow up of statistical solutions and/or pathological behaviour of their invariant measure. Here a new class of physics constrained multi-level quadratic regression models are introduced, analysed and applied to build reduced stochastic models from data of nonlinear systems. These models have the advantages of incorporating memory effects in time as well as the nonlinear noise from energy conserving nonlinear interactions. The mathematical guidelines for the performance and behaviour of these physics constrained MLR models as well as filtering algorithms for their implementation are developed here. Data driven applications of these new multi-level nonlinear regression models are developed for test models involving a nonlinear oscillator with memory effects and the difficult test case of the truncated Burgers–Hopf model. These new physics constrained quadratic MLR models are proposed here as process models for Bayesian estimation through Markov chain Monte Carlo algorithms of low frequency behaviour in complex physical data. (paper)

Logistic regression modelling: procedures and pitfalls in developing and interpreting prediction models

Directory of Open Access Journals (Sweden)

Nataša Šarlija

2017-01-01

Full Text Available This study sheds light on the most common issues related to applying logistic regression in prediction models for company growth. The purpose of the paper is 1 to provide a detailed demonstration of the steps in developing a growth prediction model based on logistic regression analysis, 2 to discuss common pitfalls and methodological errors in developing a model, and 3 to provide solutions and possible ways of overcoming these issues. Special attention is devoted to the question of satisfying logistic regression assumptions, selecting and defining dependent and independent variables, using classification tables and ROC curves, for reporting model strength, interpreting odds ratios as effect measures and evaluating performance of the prediction model. Development of a logistic regression model in this paper focuses on a prediction model of company growth. The analysis is based on predominantly financial data from a sample of 1471 small and medium-sized Croatian companies active between 2009 and 2014. The financial data is presented in the form of financial ratios divided into nine main groups depicting following areas of business: liquidity, leverage, activity, profitability, research and development, investing and export. The growth prediction model indicates aspects of a business critical for achieving high growth. In that respect, the contribution of this paper is twofold. First, methodological, in terms of pointing out pitfalls and potential solutions in logistic regression modelling, and secondly, theoretical, in terms of identifying factors responsible for high growth of small and medium-sized companies.

Regression Models for Repairable Systems

Czech Academy of Sciences Publication Activity Database

Novák, Petr

2015-01-01

Roč. 17, č. 4 (2015), s. 963-972 ISSN 1387-5841 Institutional support: RVO:67985556 Keywords : Reliability analysis * Repair models * Regression Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.782, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/novak-0450902.pdf

Psychological determinants of exercise behavior of nursing students.

Science.gov (United States)

Chan, Joanne Chung-Yan

2014-01-01

Though expected to be role models in health promotion, research has shown that nursing students often have suboptimal exercise behavior. This study explored the psychological factors associated with the exercise behavior of nursing students. A total of 195 first-year undergraduate nursing students completed a cross-sectional quantitative survey questionnaire, which included measures of their exercise behavior, the Physical Exercise Self-efficacy Scale, and the Exercise Barriers/Benefits Scale. The results showed that male students spent more time exercising and had higher exercise self-efficacy compared with female students, but there were no gender differences in the perceived barriers to or benefits of exercise. Fatigue brought on by exercising was the greatest perceived barrier to exercise, whereas increasing physical fitness and mental health were the greatest perceived benefits of exercise. Multiple linear regression showed that gender, exercise self-efficacy, perceived barriers to exercise, and perceived benefits of exercise were independent predictors of exercise behavior. Nurse educators can endeavor to promote exercise behavior among nursing students by highlighting the specific benefits of exercise, empowering students to overcome their perceived barriers to exercise, and enhancing students' exercise self-efficacy.

DYNA3D/ParaDyn Regression Test Suite Inventory

Energy Technology Data Exchange (ETDEWEB)

Lin, Jerry I. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2016-09-01

The following table constitutes an initial assessment of feature coverage across the regression test suite used for DYNA3D and ParaDyn. It documents the regression test suite at the time of preliminary release 16.1 in September 2016. The columns of the table represent groupings of functionalities, e.g., material models. Each problem in the test suite is represented by a row in the table. All features exercised by the problem are denoted by a check mark (√) in the corresponding column. The definition of “feature” has not been subdivided to its smallest unit of user input, e.g., algorithmic parameters specific to a particular type of contact surface. This represents a judgment to provide code developers and users a reasonable impression of feature coverage without expanding the width of the table by several multiples. All regression testing is run in parallel, typically with eight processors, except problems involving features only available in serial mode. Many are strictly regression tests acting as a check that the codes continue to produce adequately repeatable results as development unfolds; compilers change and platforms are replaced. A subset of the tests represents true verification problems that have been checked against analytical or other benchmark solutions. Users are welcomed to submit documented problems for inclusion in the test suite, especially if they are heavily exercising, and dependent upon, features that are currently underrepresented.

Geographically weighted regression model on poverty indicator

Science.gov (United States)

Slamet, I.; Nugroho, N. F. T. A.; Muslich

2017-12-01

In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.

Using Social Network Analysis to Better Understand Compulsive Exercise Behavior Among a Sample of Sorority Members.

Science.gov (United States)

Patterson, Megan S; Goodson, Patricia

2017-05-01

Compulsive exercise, a form of unhealthy exercise often associated with prioritizing exercise and feeling guilty when exercise is missed, is a common precursor to and symptom of eating disorders. College-aged women are at high risk of exercising compulsively compared with other groups. Social network analysis (SNA) is a theoretical perspective and methodology allowing researchers to observe the effects of relational dynamics on the behaviors of people. SNA was used to assess the relationship between compulsive exercise and body dissatisfaction, physical activity, and network variables. Descriptive statistics were conducted using SPSS, and quadratic assignment procedure (QAP) analyses were conducted using UCINET. QAP regression analysis revealed a statistically significant model (R 2 = .375, P exercise behavior. Physical activity, body dissatisfaction, and network variables were statistically significant predictor variables in the QAP regression model. In our sample, women who are connected to "important" or "powerful" people in their network are likely to have higher compulsive exercise scores. This result provides healthcare practitioners key target points for intervention within similar groups of women. For scholars researching eating disorders and associated behaviors, this study supports looking into group dynamics and network structure in conjunction with body dissatisfaction and exercise frequency.

Arrhythmia and exercise intolerance in Fontan patients

DEFF Research Database (Denmark)

Idorn, L; Juul, K; Jensen, A S

2013-01-01

BACKGROUND: Long-term survival after the Fontan procedure shows excellent results but is associated with a persistent risk of arrhythmias and exercise intolerance. We aimed to analyze the current burden of clinically relevant arrhythmia and severe exercise intolerance in Danish Fontan patients...... and estimated to 99.1% per year. Prevalence of clinically relevant arrhythmia and severe exercise intolerance increased significantly with age and was found in 32% and 85% of patients ≥20years, respectively. Thus, from survival data and logistic regression models the future prevalence of patients, clinically...... relevant arrhythmia and severe exercise intolerance were estimated, revealing a considerable augmentation. Furthermore, resting and maximum cardiac index, resting stroke volume index and pulmonary diffusing capacity decreased significantly with age while diastolic and systolic ventricular function...

On a Robust MaxEnt Process Regression Model with Sample-Selection

Directory of Open Access Journals (Sweden)

Hea-Jung Kim

2018-04-01

Full Text Available In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.

On concurvity in nonlinear and nonparametric regression models

Directory of Open Access Journals (Sweden)

Sonia Amodio

2014-12-01

Full Text Available When data are affected by multicollinearity in the linear regression framework, then concurvity will be present in fitting a generalized additive model (GAM. The term concurvity describes nonlinear dependencies among the predictor variables. As collinearity results in inflated variance of the estimated regression coefficients in the linear regression model, the result of the presence of concurvity leads to instability of the estimated coefficients in GAMs. Even if the backfitting algorithm will always converge to a solution, in case of concurvity the final solution of the backfitting procedure in fitting a GAM is influenced by the starting functions. While exact concurvity is highly unlikely, approximate concurvity, the analogue of multicollinearity, is of practical concern as it can lead to upwardly biased estimates of the parameters and to underestimation of their standard errors, increasing the risk of committing type I error. We compare the existing approaches to detect concurvity, pointing out their advantages and drawbacks, using simulated and real data sets. As a result, this paper will provide a general criterion to detect concurvity in nonlinear and non parametric regression models.

The role of social capital and community belongingness for exercise adherence: An exploratory study of the CrossFit gym model.

Science.gov (United States)

Whiteman-Sandland, Jessica; Hawkins, Jemma; Clayton, Debbie

2016-08-01

This is the first study to measure the 'sense of community' reportedly offered by the CrossFit gym model. A cross-sectional study adapted Social Capital and General Belongingness scales to compare perceptions of a CrossFit gym and a traditional gym. CrossFit gym members reported significantly higher levels of social capital (both bridging and bonding) and community belongingness compared with traditional gym members. However, regression analysis showed neither social capital, community belongingness, nor gym type was an independent predictor of gym attendance. Exercise and health professionals may benefit from evaluating further the 'sense of community' offered by gym-based exercise programmes.

Semiparametric Mixtures of Regressions with Single-index for Model Based Clustering

OpenAIRE

Xiang, Sijia; Yao, Weixin

2017-01-01

In this article, we propose two classes of semiparametric mixture regression models with single-index for model based clustering. Unlike many semiparametric/nonparametric mixture regression models that can only be applied to low dimensional predictors, the new semiparametric models can easily incorporate high dimensional predictors into the nonparametric components. The proposed models are very general, and many of the recently proposed semiparametric/nonparametric mixture regression models a...

Cardiovascular Benefits of Moderate Exercise Training in Marfan Syndrome: Insights From an Animal Model.

Science.gov (United States)

Mas-Stachurska, Aleksandra; Siegert, Anna-Maria; Batlle, Monsterrat; Gorbenko Del Blanco, Darya; Meirelles, Thayna; Rubies, Cira; Bonorino, Fabio; Serra-Peinado, Carla; Bijnens, Bart; Baudin, Julio; Sitges, Marta; Mont, Lluís; Guasch, Eduard; Egea, Gustavo

2017-09-25

Marfan syndrome (MF) leads to aortic root dilatation and a predisposition to aortic dissection, mitral valve prolapse, and primary and secondary cardiomyopathy. Overall, regular physical exercise is recommended for a healthy lifestyle, but dynamic sports are strongly discouraged in MF patients. Nonetheless, evidence supporting this recommendation is lacking. Therefore, we studied the role of long-term dynamic exercise of moderate intensity on the MF cardiovascular phenotype. In a transgenic mouse model of MF ( Fbn1 C1039G/+ ), 4-month-old wild-type and MF mice were subjected to training on a treadmill for 5 months; sedentary littermates served as controls for each group. Aortic and cardiac remodeling was assessed by echocardiography and histology. The 4-month-old MF mice showed aortic root dilatation, elastic lamina rupture, and tunica media fibrosis, as well as cardiac hypertrophy, left ventricular fibrosis, and intramyocardial vessel remodeling. Over the 5-month experimental period, aortic root dilation rate was significantly greater in the sedentary MF group, compared with the wild-type group (∆mm, 0.27±0.07 versus 0.13±0.02, respectively). Exercise significantly blunted the aortic root dilation rate in MF mice compared with sedentary MF littermates (∆mm, 0.10±0.04 versus 0.27±0.07, respectively). However, these 2 groups were indistinguishable by aortic root stiffness, tunica media fibrosis, and elastic lamina ruptures. In MF mice, exercise also produced cardiac hypertrophy regression without changes in left ventricular fibrosis. Our results in a transgenic mouse model of MF indicate that moderate dynamic exercise mitigates the progression of the MF cardiovascular phenotype. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling

International Nuclear Information System (INIS)

Che Jinxing; Wang Jianzhou

2010-01-01

In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.

Modeling oil production based on symbolic regression

International Nuclear Information System (INIS)

Yang, Guangfei; Li, Xianneng; Wang, Jianliang; Lian, Lian; Ma, Tieju

2015-01-01

Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans. -- Highlights: •A data-driven approach has been shown to be effective at modeling the oil production. •The Hubbert model could be discovered automatically from data. •The peak of world oil production is predicted to appear in 2021. •The decline rate after peak is half of the increase rate before peak. •Oil production projected to decline 4% post-peak

Robust inference in the negative binomial regression model with an application to falls data.

Science.gov (United States)

Aeberhard, William H; Cantoni, Eva; Heritier, Stephane

2014-12-01

A popular way to model overdispersed count data, such as the number of falls reported during intervention studies, is by means of the negative binomial (NB) distribution. Classical estimating methods are well-known to be sensitive to model misspecifications, taking the form of patients falling much more than expected in such intervention studies where the NB regression model is used. We extend in this article two approaches for building robust M-estimators of the regression parameters in the class of generalized linear models to the NB distribution. The first approach achieves robustness in the response by applying a bounded function on the Pearson residuals arising in the maximum likelihood estimating equations, while the second approach achieves robustness by bounding the unscaled deviance components. For both approaches, we explore different choices for the bounding functions. Through a unified notation, we show how close these approaches may actually be as long as the bounding functions are chosen and tuned appropriately, and provide the asymptotic distributions of the resulting estimators. Moreover, we introduce a robust weighted maximum likelihood estimator for the overdispersion parameter, specific to the NB distribution. Simulations under various settings show that redescending bounding functions yield estimates with smaller biases under contamination while keeping high efficiency at the assumed model, and this for both approaches. We present an application to a recent randomized controlled trial measuring the effectiveness of an exercise program at reducing the number of falls among people suffering from Parkinsons disease to illustrate the diagnostic use of such robust procedures and their need for reliable inference. © 2014, The International Biometric Society.

Model performance analysis and model validation in logistic regression

Directory of Open Access Journals (Sweden)

Rosa Arboretti Giancristofaro

2007-10-01

Full Text Available In this paper a new model validation procedure for a logistic regression model is presented. At first, we illustrate a brief review of different techniques of model validation. Next, we define a number of properties required for a model to be considered "good", and a number of quantitative performance measures. Lastly, we describe a methodology for the assessment of the performance of a given model by using an example taken from a management study.

Model building strategy for logistic regression: purposeful selection.

Science.gov (United States)

Zhang, Zhongheng

2016-03-01

Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.

The APT model as reduced-rank regression

NARCIS (Netherlands)

Bekker, P.A.; Dobbelstein, P.; Wansbeek, T.J.

Integrating the two steps of an arbitrage pricing theory (APT) model leads to a reduced-rank regression (RRR) model. So the results on RRR can be used to estimate APT models, making estimation very simple. We give a succinct derivation of estimation of RRR, derive the asymptotic variance of RRR

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

Science.gov (United States)

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

2016-01-01

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19

Influence diagnostics in meta-regression model.

Science.gov (United States)

Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua

2017-09-01

This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.

Logistic Regression Modeling of Diminishing Manufacturing Sources for Integrated Circuits

National Research Council Canada - National Science Library

Gravier, Michael

1999-01-01

.... The research identified logistic regression as a powerful tool for analysis of DMSMS and further developed twenty models attempting to identify the "best" way to model and predict DMSMS using logistic regression...

Analysis of dental caries using generalized linear and count regression models

Directory of Open Access Journals (Sweden)

Javali M. Phil

2013-11-01

Full Text Available Generalized linear models (GLM are generalization of linear regression models, which allow fitting regression models to response data in all the sciences especially medical and dental sciences that follow a general exponential family. These are flexible and widely used class of such models that can accommodate response variables. Count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. Zero inflated count regression models such as the zero-inflated Poisson (ZIP, zero-inflated negative binomial (ZINB regression models have been used to handle dental caries count data with many zeros. We present an evaluation framework to the suitability of applying the GLM, Poisson, NB, ZIP and ZINB to dental caries data set where the count data may exhibit evidence of many zeros and over-dispersion. Estimation of the model parameters using the method of maximum likelihood is provided. Based on the Vuong test statistic and the goodness of fit measure for dental caries data, the NB and ZINB regression models perform better than other count regression models.

AIRLINE ACTIVITY FORECASTING BY REGRESSION MODELS

Directory of Open Access Journals (Sweden)

Н. Білак

2012-04-01

Full Text Available Proposed linear and nonlinear regression models, which take into account the equation of trend and seasonality indices for the analysis and restore the volume of passenger traffic over the past period of time and its prediction for future years, as well as the algorithm of formation of these models based on statistical analysis over the years. The desired model is the first step for the synthesis of more complex models, which will enable forecasting of passenger (income level airline with the highest accuracy and time urgency.

[Application of detecting and taking overdispersion into account in Poisson regression model].

Science.gov (United States)

Bouche, G; Lepage, B; Migeot, V; Ingrand, P

2009-08-01

Researchers often use the Poisson regression model to analyze count data. Overdispersion can occur when a Poisson regression model is used, resulting in an underestimation of variance of the regression model parameters. Our objective was to take overdispersion into account and assess its impact with an illustration based on the data of a study investigating the relationship between use of the Internet to seek health information and number of primary care consultations. Three methods, overdispersed Poisson, a robust estimator, and negative binomial regression, were performed to take overdispersion into account in explaining variation in the number (Y) of primary care consultations. We tested overdispersion in the Poisson regression model using the ratio of the sum of Pearson residuals over the number of degrees of freedom (chi(2)/df). We then fitted the three models and compared parameter estimation to the estimations given by Poisson regression model. Variance of the number of primary care consultations (Var[Y]=21.03) was greater than the mean (E[Y]=5.93) and the chi(2)/df ratio was 3.26, which confirmed overdispersion. Standard errors of the parameters varied greatly between the Poisson regression model and the three other regression models. Interpretation of estimates from two variables (using the Internet to seek health information and single parent family) would have changed according to the model retained, with significant levels of 0.06 and 0.002 (Poisson), 0.29 and 0.09 (overdispersed Poisson), 0.29 and 0.13 (use of a robust estimator) and 0.45 and 0.13 (negative binomial) respectively. Different methods exist to solve the problem of underestimating variance in the Poisson regression model when overdispersion is present. The negative binomial regression model seems to be particularly accurate because of its theorical distribution ; in addition this regression is easy to perform with ordinary statistical software packages.

Variable Selection for Regression Models of Percentile Flows

Science.gov (United States)

Fouad, G.

2017-12-01

Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high

[Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].

Science.gov (United States)

Gao, W L; Lin, H; Liu, X N; Ren, X W; Li, J S; Shen, X P; Zhu, S L

2017-03-10

To evaluate the estimation of prevalence ratio ( PR ) by using bayesian log-binomial regression model and its application, we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant' s risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences in PR 's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1: not adjusting for the covariates; model 2: adjusting for duration of caregivers' education, model 3: adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95 %CI : 1.005-1.265), 1.128(95 %CI : 1.001-1.264) and 1.132(95 %CI : 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95 % CI : 1.055-1.206) and 1.126(95 % CI : 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate PR , which was 1.125 (95 %CI : 1.051-1.200). In addition, the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model, but they had a good consistency in estimating PR . Therefore, bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.

Factors Associated with Exercise Motivation among African-American Men.

Science.gov (United States)

Mohammed, Alana; Harrell, Jules P; Makambi, Kepher H; Campbell, Alfonso L; Sloan, Lloyd Ren; Carter-Nolan, Pamela L; Taylor, Teletia R

2016-09-01

The primary aims of this study were to: (1) characterize exercise stages of change among a sample of African-American men, (2) determine if exercise motivation was associated with self-reported exercise behavior, and (3) examine if groups of personal (i.e., age, BMI, income, educational attainment, and perceived health), psycho-social (i.e., exercise self-efficacy, personality type, social influence), and environmental factors (i.e., neighborhood safety) predicted stages of change for physical exercise among African-American men. One hundred seventy African-American male participants were recruited for this study (age: 47.63(10.23) years). Participants completed a self-report questionnaire assessing study variables. Multinomial logistic regression models were used to examine the association of exercise stages of change with an array of personal, psychosocial, and environmental factors. BMI, exercise self-efficacy, and nighttime neighborhood safety were entered as independent variables in the full model. BMI and exercise self-efficacy continued to be significant predictors of exercise stages of change in the full model. Obese men had a 9.24 greater odds of being in the action stage of change than in the maintenance stage. Also, men reporting greater exercise self-efficacy had lower odds of being in the lower stages of change categories (pre-preparation, preparation, and action) than in the maintenance stage. Our results confirmed that using an ecological framework explained more of the variance in exercise stages of change than any of the individual components alone. Information gleaned from this study could inform interventionists of the best ways to create tailored exercise programs for African-American men.

Biomechanical Modeling Analysis of Loads Configuration for Squat Exercise

Science.gov (United States)

Gallo, Christopher A.; Thompson, William K.; Lewandowski, Beth E.; Jagodnik, Kathleen; De Witt, John K.

2017-01-01

INTRODUCTION: Long duration space travel will expose astronauts to extended periods of reduced gravity. Since gravity is not present to assist loading, astronauts will use resistive and aerobic exercise regimes for the duration of the space flight to minimize loss of bone density, muscle mass and aerobic capacity that occurs during exposure to a reduced gravity environment. Unlike the International Space Station (ISS), the area available for an exercise device in the next generation of spacecraft for travel to the Moon or to Mars is limited and therefore compact resistance exercise device prototypes are being developed. The Advanced Resistive Exercise Device (ARED) currently on the ISS is being used as a benchmark for the functional performance of these new devices. Biomechanical data collection and computational modeling aid the device design process by quantifying the joint torques and the musculoskeletal forces that occur during exercises performed on the prototype devices. METHODS The computational models currently under development utilize the OpenSim [1] software platform, consisting of open source code for musculoskeletal modeling, using biomechanical input data from test subjects for estimation of muscle and joint loads. The OpenSim Full Body Model [2] is used for all analyses. The model incorporates simplified wrap surfaces, a new knee model and updated lower body muscle parameters derived from cadaver measurements and magnetic resonance imaging of young adults. The upper body uses torque actuators at the lumbar and extremity joints. The test subjects who volunteer for this study are instrumented with reflective markers for motion capture data collection while performing squat exercising on the Hybrid Ultimate Lifting Kit (HULK) prototype device (ZIN Technologies, Middleburg Heights, OH). Ground reaction force data is collected with force plates under the feet, and device loading is recorded through load cells internal to the HULK. Test variables include

Geographically Weighted Logistic Regression Applied to Credit Scoring Models

Directory of Open Access Journals (Sweden)

Pedro Henrique Melo Albuquerque

Full Text Available Abstract This study used real data from a Brazilian financial institution on transactions involving Consumer Direct Credit (CDC, granted to clients residing in the Distrito Federal (DF, to construct credit scoring models via Logistic Regression and Geographically Weighted Logistic Regression (GWLR techniques. The aims were: to verify whether the factors that influence credit risk differ according to the borrower’s geographic location; to compare the set of models estimated via GWLR with the global model estimated via Logistic Regression, in terms of predictive power and financial losses for the institution; and to verify the viability of using the GWLR technique to develop credit scoring models. The metrics used to compare the models developed via the two techniques were the AICc informational criterion, the accuracy of the models, the percentage of false positives, the sum of the value of false positive debt, and the expected monetary value of portfolio default compared with the monetary value of defaults observed. The models estimated for each region in the DF were distinct in their variables and coefficients (parameters, with it being concluded that credit risk was influenced differently in each region in the study. The Logistic Regression and GWLR methodologies presented very close results, in terms of predictive power and financial losses for the institution, and the study demonstrated viability in using the GWLR technique to develop credit scoring models for the target population in the study.

Modeling maximum daily temperature using a varying coefficient regression model

Science.gov (United States)

Han Li; Xinwei Deng; Dong-Yum Kim; Eric P. Smith

2014-01-01

Relationships between stream water and air temperatures are often modeled using linear or nonlinear regression methods. Despite a strong relationship between water and air temperatures and a variety of models that are effective for data summarized on a weekly basis, such models did not yield consistently good predictions for summaries such as daily maximum temperature...

Statins are related to impaired exercise capacity in males but not females.

Science.gov (United States)

Bahls, Martin; Groß, Stefan; Ittermann, Till; Busch, Raila; Gläser, Sven; Ewert, Ralf; Völzke, Henry; Felix, Stephan B; Dörr, Marcus

2017-01-01

Exercise and statins reduce cardiovascular disease (CVD). Exercise capacity may be assessed using cardiopulmonary exercise testing (CPET). Whether statin medication is associated with CPET parameters is unclear. We investigated if statins are related with exercise capacity during CPET in the general population. Cross-sectional data of two independent cohorts of the Study of Health in Pomerania (SHIP) were merged (n = 3,500; 50% males). Oxygen consumption (VO2) at peak exercise (VO2peak) and anaerobic threshold (VO2@AT) was assessed during symptom-limited CPET. Two linear regression models related VO2peak with statin usage were calculated. Model 1 adjusted for age, sex, previous myocardial infarction, and physical inactivity and model 2 additionally for body mass index, smoking, hypertension, diabetes and estimated glomerular filtration rate. Propensity score matching was used for validation. Statin usage was associated with lower VO2peak (no statin: 2336; 95%-confidence interval [CI]: 2287-2,385 vs. statin 2090; 95%-CI: 2,031-2149 ml/min; P exercise capacity in males but not females. Sex specific effects of statins on cardiopulmonary exercise capacity deserve further research.

Structured Additive Regression Models: An R Interface to BayesX

Directory of Open Access Journals (Sweden)

Nikolaus Umlauf

2015-02-01

Full Text Available Structured additive regression (STAR models provide a flexible framework for model- ing possible nonlinear effects of covariates: They contain the well established frameworks of generalized linear models and generalized additive models as special cases but also allow a wider class of effects, e.g., for geographical or spatio-temporal data, allowing for specification of complex and realistic models. BayesX is standalone software package providing software for fitting general class of STAR models. Based on a comprehensive open-source regression toolbox written in C++, BayesX uses Bayesian inference for estimating STAR models based on Markov chain Monte Carlo simulation techniques, a mixed model representation of STAR models, or stepwise regression techniques combining penalized least squares estimation with model selection. BayesX not only covers models for responses from univariate exponential families, but also models from less-standard regression situations such as models for multi-categorical responses with either ordered or unordered categories, continuous time survival data, or continuous time multi-state models. This paper presents a new fully interactive R interface to BayesX: the R package R2BayesX. With the new package, STAR models can be conveniently specified using Rs formula language (with some extended terms, fitted using the BayesX binary, represented in R with objects of suitable classes, and finally printed/summarized/plotted. This makes BayesX much more accessible to users familiar with R and adds extensive graphics capabilities for visualizing fitted STAR models. Furthermore, R2BayesX complements the already impressive capabilities for semiparametric regression in R by a comprehensive toolbox comprising in particular more complex response types and alternative inferential procedures such as simulation-based Bayesian inference.

Multiple regression and beyond an introduction to multiple regression and structural equation modeling

CERN Document Server

Keith, Timothy Z

2014-01-01

Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.

Time series regression model for infectious disease and weather.

Science.gov (United States)

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. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Linear regression crash prediction models : issues and proposed solutions.

Science.gov (United States)

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

Identification of Influential Points in a Linear Regression Model

Directory of Open Access Journals (Sweden)

Jan Grosz

2011-03-01

Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.

The art of regression modeling in road safety

CERN Document Server

Hauer, Ezra

2015-01-01

This unique book explains how to fashion useful regression models from commonly available data to erect models essential for evidence-based road safety management and research. Composed from techniques and best practices presented over many years of lectures and workshops, The Art of Regression Modeling in Road Safety illustrates that fruitful modeling cannot be done without substantive knowledge about the modeled phenomenon. Class-tested in courses and workshops across North America, the book is ideal for professionals, researchers, university professors, and graduate students with an interest in, or responsibilities related to, road safety. This book also: · Presents for the first time a powerful analytical tool for road safety researchers and practitioners · Includes problems and solutions in each chapter as well as data and spreadsheets for running models and PowerPoint presentation slides · Features pedagogy well-suited for graduate courses and workshops including problems, solutions, and PowerPoint p...

Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

Directory of Open Access Journals (Sweden)

Hong-Juan Li

2013-04-01

Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

Robust geographically weighted regression of modeling the Air Polluter Standard Index (APSI)

Science.gov (United States)

Warsito, Budi; Yasin, Hasbi; Ispriyanti, Dwi; Hoyyi, Abdul

2018-05-01

The Geographically Weighted Regression (GWR) model has been widely applied to many practical fields for exploring spatial heterogenity of a regression model. However, this method is inherently not robust to outliers. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression model. One of solution to handle the outliers in the regression model is to use the robust models. So this model was called Robust Geographically Weighted Regression (RGWR). This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index - APSI) based on the RGWR approach. In this research, we also consider seven variables that are directly related to the air pollution level, which are the traffic velocity, the population density, the business center aspect, the air humidity, the wind velocity, the air temperature, and the area size of the urban forest. The best model is determined by the smallest AIC value. There are significance differences between Regression and RGWR in this case, but Basic GWR using the Gaussian kernel is the best model to modeling APSI because it has smallest AIC.

Self-determined motivation and exercise behaviour in COPD patients.

Science.gov (United States)

Cho, Hui-Ling; Tung, Heng-Hsin; Lin, Ming-Shian; Hsu, Wan-Chun; Lee, Chi-Pin

2017-06-01

The purpose of this study was to evaluate the self-determined motivation predictors of exercise behaviour following pulmonary rehabilitation in COPD recipients. This cross-sectional study was conducted with 135 COPD patients. A demographic questionnaire, clinical factors, behavioural regulations in exercise questionnaire, and leisure time exercise questionnaire were used to collect data. A logistic regression model was used to identify the predictors associated with demographics and self-determined motivation types regarding physical activity. Education level, episodes of acute exacerbation within 2 years, and identified regulation were significant predictors of executing physical activities with high metabolic equivalents. The results of this study imply that healthcare providers need to be aware of the importance of exercise motivation among COPD patients. © 2017 John Wiley & Sons Australia, Ltd.

Flexible competing risks regression modeling and goodness-of-fit

DEFF Research Database (Denmark)

Scheike, Thomas; Zhang, Mei-Jie

2008-01-01

In this paper we consider different approaches for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause...... models that is easy to fit and contains the Fine-Gray model as a special case. One advantage of this approach is that our regression modeling allows for non-proportional hazards. This leads to a new simple goodness-of-fit procedure for the proportional subdistribution hazards assumption that is very easy...... of the flexible regression models to analyze competing risks data when non-proportionality is present in the data....

Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling.

Science.gov (United States)

Chatzis, Sotirios P; Andreou, Andreas S

2015-11-01

Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.

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

Science.gov (United States)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

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

Bayesian Inference of a Multivariate Regression Model

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Marick S. Sinay

2014-01-01

Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.

General regression and representation model for classification.

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Jianjun Qian

Full Text Available 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.

A test for the parameters of multiple linear regression models ...

African Journals Online (AJOL)

A test for the parameters of multiple linear regression models is developed for conducting tests simultaneously on all the parameters of multiple linear regression models. The test is robust relative to the assumptions of homogeneity of variances and absence of serial correlation of the classical F-test. Under certain null and ...

The influence of exercise identity and social physique anxiety on exercise dependence.

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Cook, Brian; Karr, Trisha M; Zunker, Christie; Mitchell, James E; Thompson, Ron; Sherman, Roberta; Erickson, Ann; Cao, Li; Crosby, Ross D

2015-09-01

Previous research has identified exercise identity and social physique anxiety as two independent factors that are associated with exercise dependence. The purpose of our study was to investigate the unique and interactive effect of these two known correlates of exercise dependence in a sample of 1,766 female runners. Regression analyses tested the main effects of exercise identity and social physique anxiety on exercise dependence. An interaction term was calculated to examine the potential moderating effect of social physique anxiety on the exercise identity and exercise dependence relationship. Results indicate a main effect for exercise identity and social physique anxiety on exercise dependence; and the interaction of these factors explained exercise dependence scores beyond the independent effects. Thus, social physique anxiety acted as a moderator in the exercise identity and exercise dependence relationship. Our results indicate that individuals who strongly identify themselves as an exerciser and also endorse a high degree of social physique anxiety may be at risk for developing exercise dependence. Our study supports previous research which has examined factors that may contribute to the development of exercise dependence and also suggests a previously unknown moderating relationship for social physique anxiety on exercise dependence.

Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

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Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

2016-03-01

In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Trial Protocol: Home-based exercise programs to prevent falls and upper limb dysfunction among community-dwelling older people: study protocol for the BEST (Balance Exercise Strength Training at Home randomised, controlled trial

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Amanda Bates

2018-04-01

. Analysis: Negative binomial regression models will be used to estimate the between-group difference in fall rates. Modified Poisson regression models will be used to compare groups on dichotomous outcome measures. Linear regression models will be used to assess the effect of group allocation on the continuously scored measures, after adjusting for baseline scores. Two economic evaluations will be conducted: the first will assess cost-effectiveness of the lower limb program compared with the upper limb program; and the second will assess cost-effectiveness of the upper limb program compared with the lower limb program. Discussion: If effective, the trial will provide a model for both upper limb and lower limb exercise programs that can be performed at home and implemented at scale to community-dwelling older adults.

Trial Protocol: Home-based exercise programs to prevent falls and upper limb dysfunction among community-dwelling older people: study protocol for the BEST (Balance Exercise Strength Training) at Home randomised, controlled trial.

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Bates, Amanda; Furber, Susan; Tiedemann, Anne; Ginn, Karen; van den Dolder, Paul; Howard, Kirsten; Bauman, Adrian; Chittenden, Catherine; Franco, Lisa; Kershaw, Michelle; Sherrington, Catherine

2018-04-01

Falling when older is a major public health issue. There is compelling evidence to show that specific exercise programs can reduce the risk and rate of falls in community-dwelling older people. Another major health issue for older people living in the community is upper limb dysfunction, including shoulder pain. Home-based exercise programs appeal to some older people, due to their convenience. This trial aims to determine the effectiveness and cost-effectiveness of a home-based lower limb exercise program compared with a home-based upper limb exercise program to prevent falls and upper limb dysfunction among community-dwelling people aged 65+ years. Randomised, controlled trial. A total of 576 community-dwelling people will be recruited from the Illawarra and Shoalhaven regions of New South Wales, Australia. Participants will be randomised to either a home-based lower limb exercise intervention or a home-based upper limb exercise intervention. The lower limb program is designed to improve balance and strength in the lower limbs. The upper limb program is designed to improve upper limb strength and mobility. Participants will attend three group-based instruction sessions to learn and progress the exercises, and will be instructed to perform the exercises three times per week at home for 12 months. The two primary outcomes will be fall rates, recorded with monthly calendars for a 12-month period, and upper limb dysfunction, measured with the Disability of the Arm, Shoulder and Hand questionnaire. Secondary outcomes will include: lower limb strength and balance; shoulder strength and mobility; physical activity; quality of life; attitudes to exercise; proportion of fallers; fear of falling; and health and community service use. The cost-effectiveness of both exercise programs from a health and community service provider perspective will be evaluated. Negative binomial regression models will be used to estimate the between-group difference in fall rates. Modified

Contribution of Hamstring Fatigue to Quadriceps Inhibition Following Lumbar Extension Exercise

OpenAIRE

Hart, Joseph M.; Kerrigan, D. Casey; Fritz, Julie M.; Saliba, Ethan N.; Gansneder, Bruce; Ingersoll, Christopher D.

2006-01-01

The purpose of this study was to determine the contribution of hamstrings and quadriceps fatigue to quadriceps inhibition following lumbar extension exercise. Regression models were calculated consisting of the outcome variable: quadriceps inhibition and predictor variables: change in EMG median frequency in the quadriceps and hamstrings during lumbar fatiguing exercise. Twenty-five subjects with a history of low back pain were matched by gender, height and mass to 25 healthy controls. Subjec...

Application of the Transtheoretical Model to Predict Exercise Activities in the Students of Islamic Azad University of Sabzevar

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M. Mohammadi

2012-04-01

Full Text Available Background: Based on report of World Health Organization (WHO, about 60-85% of the world's population fails to complete the recommended amount of physical activity required to induce health benefits. It is necessary to assess health status for designing and programming about exercise activities. In this study the effectiveness of Transtheoretical Model (TTM in predicting exercise activities among the students of Islmaic Azad University of Sabzevar was examined. Methods: In this cross sectional-Correlational study. A random (clustered sample of 234 university students in Islamic Azad university of Sabzevar, participated in the study. A standard instrument was used to measure the variables of interest based on transtheoretical model. Reliability and validity of the questionnaire was examined by a panel of experts and cronbach alpha (N=30, α=0.83-0.95. The data were analyzed by SPSS 16.00 statistical software using Path analysis based regression, t-test and ANOVA and Correlation. Results: According to the results, the average age of students was 22.5±3.8 years. The distribution of the participants according to the stages of change model was as follows: pre-contemplation 36.3%, contemplation 25.6%, preparation, 18.9%, action, 10.5% and maintenance 8.7%.These were significant differences between mean of self efficacy, process of change, decisional balance by sex (p<0.05 and stages of change (p<0.01. Behavioral process of change (β=0.399 and self efficacy (β=0.350 were the most important variables for improving levels of exercise. Conclusion: Because the most students (62% were at precontemplation, contemplation and preparation stages and the results showed that behavioral process of change perceived barriers and self efficacy are the most important predictors for improving levels of exercise. Thus, policies and programs to strengthen these factors to promote exercise activities among students is recommended.

Logistic regression models

CERN Document Server

Hilbe, Joseph M

2009-01-01

This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...

NAFLD, Estrogens, and Physical Exercise: The Animal Model

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Jean-Marc Lavoie

2012-01-01

Full Text Available One segment of the population that is particularly inclined to liver fat accumulation is postmenopausal women. Although nonalcoholic hepatic steatosis is more common in men than in women, after menopause there is a reversal in gender distribution. At the present time, weight loss and exercise are regarded as first line treatments for NAFLD in postmenopausal women, as it is the case for the management of metabolic syndrome. In recent years, there has been substantial evidence coming mostly from the use of the animal model, that indeed estrogens withdrawal is associated with modifications of molecular markers favouring the activity of metabolic pathways ultimately leading to liver fat accumulation. In addition, the use of the animal model has provided physiological and molecular evidence that exercise training provides estrogens-like protective effects on liver fat accumulation and its consequences. The purpose of the present paper is to present information relative to the development of a state of NAFLD resulting from the absence of estrogens and the role of exercise training, emphasizing on the contribution of the animal model on these issues.

Linking Simple Economic Theory Models and the Cointegrated Vector AutoRegressive Model

DEFF Research Database (Denmark)

Møller, Niels Framroze

This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its stru....... Further fundamental extensions and advances to more sophisticated theory models, such as those related to dynamics and expectations (in the structural relations) are left for future papers......This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its......, it is demonstrated how other controversial hypotheses such as Rational Expectations can be formulated directly as restrictions on the CVAR-parameters. A simple example of a "Neoclassical synthetic" AS-AD model is also formulated. Finally, the partial- general equilibrium distinction is related to the CVAR as well...

Multiple Response Regression for Gaussian Mixture Models with Known Labels.

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Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng

2012-12-01

Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.

Extending the linear model with R generalized linear, mixed effects and nonparametric regression models

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Faraway, Julian J

2005-01-01

Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...

Exercisers' identities and exercise dependence: the mediating effect of exercise commitment.

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Lu, Frank Jing-Horng; Hsu, Eva Ya-Wen; Wang, Junn-Ming; Huang, Mei-Yao; Chang, Jo-Ning; Wang, Chien-Hsin

2012-10-01

The purpose of this study was to examine the associations of exercise identity, exercise commitment, exercise dependence, and, particularly, the mediating effects of exercise commitment on the relationship between exercise identity and exercise dependence. 253 Taiwanese regular exercisers completed measures, including the Exercise Dependence Scale-Revised, the Exercise Identity Scale, the Exercise Commitment Scale, and the Godin Leisure Time Exercise Questionnaire. Results showed that exercise identity, exercise dependence, and two types of exercise commitment were moderately to highly correlated. Furthermore, structural equation modelling indicated that a "have to" commitment partially mediated the relationship between exercise identity and exercise dependence. Based on the mediating role of a "have to" commitment, the findings are particularly informative to exercise instructors and for exercise program managers.

Transtheoretical Model Based Exercise Counseling Combined with Music Skipping Rope Exercise on Childhood Obesity.

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Ham, Ok Kyung; Sung, Kyung Mi; Lee, Bo Gyeong; Choi, Hee Won; Im, Eun-Ok

2016-06-01

The purpose was to evaluate the effects of a transtheoretical model (TTM) based exercise counseling offered with music skipping rope exercise on components of the TTM (stages of change, decisional balance, and self-efficacy), body mass index, glucose, and lipid profile of overweight/obese children in Korea. This study used a nonequivalent pretest and posttest experimental study design. A total of 75 overweight/obese children participated in the study. Eight sessions of exercise counseling combined with music skipping rope exercise for 12 weeks were offered for children in the experimental group, while one session of exercise counseling with music skipping rope exercise for 12 weeks was offered for children in the control group. Outcomes were measured at baseline, and 6 months after the intervention. After the intervention, self-efficacy significantly improved among children in the experimental group (p = .049), while these children maintained their baseline BMI at 6-month follow-up (p > .05). Among children in the control group, BMI significantly increased (p effective in maintaining BMI and improving self-efficacy of overweight/obese children. The TTM-based counseling combined with exercise classes has potential to control weight among overweight/obese children, while involvement of parents and children in the development of the theory-based intervention may generate further benefits regarding health and well-being of overweight/obese children. Copyright © 2016. Published by Elsevier B.V.

OpenSim Model Improvements to Support High Joint Angle Resistive Exercising

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Gallo, Christopher; Thompson, William; Lewandowski, Beth; Humphreys, Brad

2016-01-01

Long duration space travel to Mars or to an asteroid will expose astronauts to extended periods of reduced gravity. Since gravity is not present to aid loading, astronauts will use resistive and aerobic exercise regimes for the duration of the space flight to minimize the loss of bone density, muscle mass and aerobic capacity that occurs during exposure to a reduced gravity environment. Unlike the International Space Station (ISS), the area available for an exercise device in the next generation of spacecraft is limited. Therefore, compact resistance exercise device prototypes are being developed. The Advanced Resistive Exercise Device (ARED) currently on the ISS is being used as a benchmark for the functional performance of these new devices. Rigorous testing of these proposed devices in space flight is difficult so computational modeling provides an estimation of the muscle forces and joint loads during exercise to gain insight on the efficacy to protect the musculoskeletal health of astronauts. The NASA Digital Astronaut Project (DAP) is supporting the Advanced Exercise Concepts (AEC) Project, Exercise Physiology and Countermeasures (ExPC) project and the National Space Biomedical Research Institute (NSBRI) funded researchers by developing computational models of exercising with these new advanced exercise device concepts

Bayesian approach to errors-in-variables in regression models

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Rozliman, Nur Aainaa; Ibrahim, Adriana Irawati Nur; Yunus, Rossita Mohammad

2017-05-01

In many applications and experiments, data sets are often contaminated with error or mismeasured covariates. When at least one of the covariates in a model is measured with error, Errors-in-Variables (EIV) model can be used. Measurement error, when not corrected, would cause misleading statistical inferences and analysis. Therefore, our goal is to examine the relationship of the outcome variable and the unobserved exposure variable given the observed mismeasured surrogate by applying the Bayesian formulation to the EIV model. We shall extend the flexible parametric method proposed by Hossain and Gustafson (2009) to another nonlinear regression model which is the Poisson regression model. We shall then illustrate the application of this approach via a simulation study using Markov chain Monte Carlo sampling methods.

Habitual exercise instigation (vs. execution) predicts healthy adults' exercise frequency.

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Phillips, L Alison; Gardner, Benjamin

2016-01-01

Habit is thought to be conducive to health behavior maintenance, because habits prompt behavior with minimal cognitive resources. The precise role of habit in determining complex behavioral sequences, such as exercise, has been underresearched. It is possible that the habit process may initiate a behavioral sequence (instigation habit) or that, after instigation, movement through the sequence is automated (execution habit). We hypothesized that exercise instigation habit can be empirically distinguished from exercise execution habit and that instigation habit strength is most predictive of future exercise and reflective of longitudinal exercise behavior change. Further, we evaluated whether patterned exercise action-that is, engaging in the same exercise actions from session to session-can be distinct from exercise execution habit. Healthy adults (N = 123) rated their exercise instigation and execution habit strengths, patterned exercise actions, and exercise frequency in baseline and 1-month follow-up surveys. Participants reported exercise engagement via electronic daily diaries for 1 month. Hypotheses were tested with regression analyses and repeated-measures analyses of variance. Exercise instigation habit strength was the only unique predictor of exercise frequency. Frequency profiles (change from high to low or low to high, no change high, no change low) were associated with changes in instigation habit but not with execution habit or patterned exercise action. Results suggest that the separable components of exercise sessions may be more or less automatic, and they point to the importance of developing instigation habit for establishing frequent exercise. (c) 2015 APA, all rights reserved).

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

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

2017-04-08

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

Direction of Effects in Multiple Linear Regression Models.

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Wiedermann, Wolfgang; von Eye, Alexander

2015-01-01

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

Development of an anaesthetized-rat model of exercise hyperpnoea: an integrative model of respiratory control using an equilibrium diagram.

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Miyamoto, Tadayoshi; Manabe, Kou; Ueda, Shinya; Nakahara, Hidehiro

2018-05-01

What is the central question of this study? The lack of useful small-animal models for studying exercise hyperpnoea makes it difficult to investigate the underlying mechanisms of exercise-induced ventilatory abnormalities in various disease states. What is the main finding and its importance? We developed an anaesthetized-rat model for studying exercise hyperpnoea, using a respiratory equilibrium diagram for quantitative characterization of the respiratory chemoreflex feedback system. This experimental model will provide an opportunity to clarify the major determinant mechanisms of exercise hyperpnoea, and will be useful for understanding the mechanisms responsible for abnormal ventilatory responses to exercise in disease models. Exercise-induced ventilatory abnormalities in various disease states seem to arise from pathological changes of respiratory regulation. Although experimental studies in small animals are essential to investigate the pathophysiological basis of various disease models, the lack of an integrated framework for quantitatively characterizing respiratory regulation during exercise prevents us from resolving these problems. The purpose of this study was to develop an anaesthetized-rat model for studying exercise hyperpnoea for quantitative characterization of the respiratory chemoreflex feedback system. In 24 anaesthetized rats, we induced muscle contraction by stimulating bilateral distal sciatic nerves at low and high voltage to mimic exercise. We recorded breath-by-breath respiratory gas analysis data and cardiorespiratory responses while running two protocols to characterize the controller and plant of the respiratory chemoreflex. The controller was characterized by determining the linear relationship between end-tidal CO 2 pressure (P ETC O2) and minute ventilation (V̇E), and the plant by the hyperbolic relationship between V̇E and P ETC O2. During exercise, the controller curve shifted upward without change in controller gain, accompanying

Examining intrinsic versus extrinsic exercise goals: cognitive, affective, and behavioral outcomes.

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Sebire, Simon J; Standage, Martyn; Vansteenkiste, Maarten

2009-04-01

Grounded in self-determination theory (SDT), this study had two purposes: (a) examine the associations between intrinsic (relative to extrinsic) exercise goal content and cognitive, affective, and behavioral outcomes; and (b) test the mediating role of psychological need satisfaction in the Exercise Goal Content --> Outcomes relationship. Using a sample of 410 adults, hierarchical regression analysis showed relative intrinsic goal content to positively predict physical self-worth, self-reported exercise behavior, psychological well-being, and psychological need satisfaction and negatively predict exercise anxiety. Except for exercise behavior, the predictive utility of relative intrinsic goal content on the dependent variables of interest remained significant after controlling for participants' relative self-determined exercise motivation. Structural equation modeling analyses showed psychological need satisfaction to partially mediate the effect of relative intrinsic goal content on the outcome variables. Our findings support further investigation of exercise goals commensurate with the goal content perspective advanced in SDT.

A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design.

Science.gov (United States)

Meaney, Christopher; Moineddin, Rahim

2014-01-24

In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the

Tutorial on Using Regression Models with Count Outcomes Using R

Directory of Open Access Journals (Sweden)

A. Alexander Beaujean

2016-02-01

Full Text Available Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.

[A study of factors influenced by self-efficacy for exercise among community-dwelling elderly men in urban areas].

Science.gov (United States)

Takai, Itsushi

2012-01-01

It is important to promote self-efficacy for exercise for developing exercise habit. The purpose of this study was to investigate factors influenced by self-efficacy for exercise among community-dwelling elderly men in urban areas. The subjects were 69 elderly men (mean age of 74.2±2.0 SD) who had given approval for participation in the study. We examined the following factors: family situation, history of falls, frequency of going out, stage model of a change, self-efficacy for exercise, fall efficacy scale (FES), geriatric depression scale (GDS), subjective health, functional ability and motor function (5 m walking time, chair stand test-5times). Analysis of variance was used to assess a stage model of a change differences in self-efficacy for exercise and other measures. Correlation analysis and multiple regression analysis were performed to determine the relationships between self-efficacy for exercise and other measures. We found that self-efficacy of exercise, FES, GDS (pSelf-efficacy for exercise was found to correlate with psychological factors and functional ability (|r|=0.47-0.67). Multiple regression analysis revealed that the independent factors related to self-efficacy for exercise were FES and GDS. FES and GDS were found to be significant and independent predictors of self-efficacy for exercise in community-dwelling elderly men in urban areas. We should consider not only the approach based on behavioral science but also mental support for depression and fear of falling to promote exercise self-efficacy.

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

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

2017-12-18

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

Statistical approach for selection of regression model during validation of bioanalytical method

Directory of Open Access Journals (Sweden)

Natalija Nakov

2014-06-01

Full Text Available The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit using nonparametric statistical tests: One sample rank test and Wilcoxon signed rank test for two independent groups of samples. The results obtained with One sample rank test could not give statistical justification for the selection of linear vs. quadratic regression models because slight differences between the error (presented through the relative residuals were obtained. Estimation of the significance of the differences in the RR was achieved using Wilcoxon signed rank test, where linear and quadratic regression models were treated as two independent groups. The application of this simple non-parametric statistical test provides statistical confirmation of the choice of an adequate regression model.

Linear regression models for quantitative assessment of left ...

African Journals Online (AJOL)

Changes in left ventricular structures and function have been reported in cardiomyopathies. No prediction models have been established in this environment. This study established regression models for prediction of left ventricular structures in normal subjects. A sample of normal subjects was drawn from a large urban ...

Factors Influencing Amount of Weekly Exercise Time in Colorectal Cancer Survivors.

Science.gov (United States)

Chou, Yun-Jen; Lai, Yeur-Hur; Lin, Been-Ren; Liang, Jin-Tung; Shun, Shiow-Ching

Performing regular exercise of at least 150 minutes weekly has benefits for colorectal cancer survivors. However, barriers inhibit these survivors from performing regular exercise. The aim of this study was to explore exercise behaviors and significant factors influencing weekly exercise time of more than 150 minutes in colorectal cancer survivors. A cross-sectional study design was used to recruit participants in Taiwan. Guided by the ecological model of health behavior, exercise barriers were assessed including intrapersonal, interpersonal, and environment-related barriers. A multiple logistic regression was used to explore the factors associated with the amount of weekly exercise. Among 321 survivors, 57.0% of them had weekly exercise times of more than 150 minutes. The results identified multiple levels of significant factors related to weekly exercise times including intrapersonal factors (occupational status, functional status, pain, interest in exercise, and beliefs about the importance of exercise) and exercise barriers related to environmental factors (lack of time and bad weather). No interpersonal factors were found to be significant. Colorectal cancer survivors experienced low levels of physical and psychological distress. Multiple levels of significant factors related to exercise time including intrapersonal factors as well as exercise barriers related to environmental factors should be considered. Healthcare providers should discuss with their patients how to perform exercise programs; the discussion should address multiple levels of the ecological model such as any pain problems, functional status, employment status, and time limitations, as well as community environment.

Reduced Rank Regression

DEFF Research Database (Denmark)

Johansen, Søren

2008-01-01

The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...

Poisson regression for modeling count and frequency outcomes in trauma research.

Science.gov (United States)

Gagnon, David R; Doron-LaMarca, Susan; Bell, Margret; O'Farrell, Timothy J; Taft, Casey T

2008-10-01

The authors describe how the Poisson regression method for analyzing count or frequency outcome variables can be applied in trauma studies. The outcome of interest in trauma research may represent a count of the number of incidents of behavior occurring in a given time interval, such as acts of physical aggression or substance abuse. Traditional linear regression approaches assume a normally distributed outcome variable with equal variances over the range of predictor variables, and may not be optimal for modeling count outcomes. An application of Poisson regression is presented using data from a study of intimate partner aggression among male patients in an alcohol treatment program and their female partners. Results of Poisson regression and linear regression models are compared.

A Novel Exercise Thermophysiology Comfort Prediction Model with Fuzzy Logic

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Nan Jia

2016-01-01

Full Text Available Participation in a regular exercise program can improve health status and contribute to an increase in life expectancy. However, exercise accidents like dehydration, exertional heatstroke, syncope, and even sudden death exist. If these accidents can be analyzed or predicted before they happen, it will be beneficial to alleviate or avoid uncomfortable or unacceptable human disease. Therefore, an exercise thermophysiology comfort prediction model is needed. In this paper, coupling the thermal interactions among human body, clothing, and environment (HCE as well as the human body physiological properties, a human thermophysiology regulatory model is designed to enhance the human thermophysiology simulation in the HCE system. Some important thermal and physiological performances can be simulated. According to the simulation results, a human exercise thermophysiology comfort prediction method based on fuzzy inference system is proposed. The experiment results show that there is the same prediction trend between the experiment result and simulation result about thermophysiology comfort. At last, a mobile application platform for human exercise comfort prediction is designed and implemented.

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

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Minh Vu Trieu

2017-03-01

Full Text Available This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS, Brazilian tensile strength (BTS, rock brittleness index (BI, the distance between planes of weakness (DPW, and the alpha angle (Alpha between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP. Four (4 statistical regression models (two linear and two nonlinear are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2 of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

Science.gov (United States)

Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

2017-03-01

This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

Deep ensemble learning of sparse regression models for brain disease diagnosis.

Science.gov (United States)

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2017-04-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling of Teaching 5th-7th-Grade Boys Physical Exercises

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Т. Г. Абдулхалікова

2017-09-01

Full Text Available The research objective is to determine the effectiveness of orthogonal variants of teaching 5th-7th graders physical exercises. Materials and methods. The participants in the research were boys of the 5th grade (n = 32, 6th grade (n = 40, 7th grade (n = 52. To achieve the tasks set, the research used the following methods: analysis of scientific and methodological literature; pedagogical testing, pedagogical observation, timing of educational tasks; pedagogical experiment, medical and biological research methods; methods of mathematical statistics, methods of mathematical experiment planning. In order to achieve the objective set, the research has studied the effect of different variants of the educational process structure, namely: the number of repetitions (х1 and rest intervals (х2 when learning the technique of performing physical exercises. The research has conducted a complete factor experiment of type 22. According to the experiment plan, the 5th-7th graders were divided into training groups. In total, there were 12 experimental groups organized. Research results. The analysis of the regression equations shows that the teaching of physical exercises to the 5th-7th-grade boys is mostly influenced by rest intervals between repetitions (х2. The number of repetitions (х1 has somewhat less influence. The interaction of these factors is insignificant when teaching physical exercises and becomes much more influential only when teaching a switch leg pull-over exercise (х1х2. Conclusions. To increase the effectiveness of teaching 5th-7th graders physical exercises, it is necessary to shorten rest intervals between repetition to 60 s and to reduce the number of repetitions to six. When teaching boys the switch leg pull-over exercise, rest intervals should be increased to 120 s and the number of repetitions — to twelve.

A computational approach to compare regression modelling strategies in prediction research.

Science.gov (United States)

Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H

2016-08-25

It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.

Exercise as adjunctive treatment for alcohol use disorder

DEFF Research Database (Denmark)

Roessler, Kirsten K.; Bilberg, Randi; Søgaard Nielsen, Anette

2017-01-01

AIMS: To examine whether physical activity as an adjunct to outpatient alcohol treatment has an effect on alcohol consumption following participation in an exercise intervention of six months' duration, and at 12 months after treatment initiation. METHODS: The study is a randomized controlled stu...... was protective against excessive drinking following treatment. A dose-response effect of exercise on drinking outcome supports the need for implementing physically active lifestyles for patients in treatment for alcohol use disorder.......AIMS: To examine whether physical activity as an adjunct to outpatient alcohol treatment has an effect on alcohol consumption following participation in an exercise intervention of six months' duration, and at 12 months after treatment initiation. METHODS: The study is a randomized controlled study...... regression model was used to evaluate the odds of excessive drinking among the three groups, based on intention-to-treat. Changes in level of physical activity in all three groups were tested by using a generalized linear mixed model. A multiple linear model was used to test if there was an association...

A generalized right truncated bivariate Poisson regression model with applications to health data.

Science.gov (United States)

Islam, M Ataharul; Chowdhury, Rafiqul I

2017-01-01

A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model.

A Mathematical Model of Cardiovascular Response to Dynamic Exercise

National Research Council Canada - National Science Library

Magosso, E

2001-01-01

A mathematical model of cardiovascular response to dynamic exercise is presented, The model includes the pulsating heart, the systemic and pulmonary, circulation, a functional description of muscle...

Optimizing Cardiovascular Benefits of Exercise: A Review of Rodent Models

Science.gov (United States)

Davis, Brittany; Moriguchi, Takeshi; Sumpio, Bauer

2013-01-01

Although research unanimously maintains that exercise can ward off cardiovascular disease (CVD), the optimal type, duration, intensity, and combination of forms are yet not clear. In our review of existing rodent-based studies on exercise and cardiovascular health, we attempt to find the optimal forms, intensities, and durations of exercise. Using Scopus and Medline, a literature review of English language comparative journal studies of cardiovascular benefits and exercise was performed. This review examines the existing literature on rodent models of aerobic, anaerobic, and power exercise and compares the benefits of various training forms, intensities, and durations. The rodent studies reviewed in this article correlate with reports on human subjects that suggest regular aerobic exercise can improve cardiac and vascular structure and function, as well as lipid profiles, and reduce the risk of CVD. Findings demonstrate an abundance of rodent-based aerobic studies, but a lack of anaerobic and power forms of exercise, as well as comparisons of these three components of exercise. Thus, further studies must be conducted to determine a truly optimal regimen for cardiovascular health. PMID:24436579

Application of Transtheoretical Model to Exercise in Office Staff

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Mohammad Ali Morowati Sharif Abad

2013-01-01

Full Text Available Abstract:Background: Transtheoretical model is identified as a comprehensive model for behavior exercise. The aim of this study was to check the situation of stage of change in exercise behavior of office personnel of Yazd city using transtheoretical model.Methods: In a cross-sectional study, 220 office personnel selected from administrative offices of Yazd through two-stage cluster-sampling method. The instrument for data collection was a questionnaire that included demographic variables and constructs of transtheoretical model. The reliability and validity of the instruments were examined and approved by experts. The data was analyzed using SPSS soft ware.Results: 152 males (69.1% and 68 females (30.9% with an average age of 34±8.68 years were selected. Sixty percent of the subjects were in precontemplation and contemplation stages and only 7.3% were in action stages. Significant differences were found between TTM constructs and stages of change (P=0.000. The results also showed significant differences between components of decisional balance and behavioral process and cognitive process with the stages of change. We found that behavioral process of change and self efficacy were the most important variables for improving levels of exercise.Conclusion: Most of the participants were in the precontemplation and contemplation stages and most problems were related to behavioral process and self efficacy. Therefore, strategies and programs are needed to be taken into account to improve exercise among the staff.

Fatiguing exercise intensity influences the relationship between parameters reflecting neuromuscular function and postural control variables.

Directory of Open Access Journals (Sweden)

Sébastien Boyas

Full Text Available The purpose of this study was to investigate the influence of fatiguing exercise intensity on the nature and extent of fatigue-induced changes in neuromuscular function and postural stability in quiet standing. We also explored the contribution of selected neuromuscular mechanisms involved in force production to postural stability impairment observed following fatigue using an approach based on multivariate regressions. Eighteen young subjects performed 30-s postural trials on one leg with their eyes closed. Postural trials were performed before and after fatiguing exercises of different intensities: 25, 50 and 75% of maximal isometric plantarflexor torque. Fatiguing exercises consisted of sustaining a plantarflexor isometric contraction at the target intensity until task failure. Maximal isometric plantarflexor torque, electromyographic activity of plantarflexor and dorsiflexor muscles, activation level (twitch interpolation technique and twitch contractile properties of plantarflexors were used to characterize neuromuscular function. The 25% exercise was associated with greater central fatigue whereas the 50 and 75% exercises involved mostly peripheral fatigue. However, all fatiguing exercises induced similar alterations in postural stability, which was unexpected considering previous literature. Stepwise multiple regression analyses showed that fatigue-related changes in selected parameters related to neuromuscular function could explain more than half (0.51≤R(2≤0.82 of the changes in postural variables for the 25% exercise. On the other hand, regression models were less predictive (0.17≤R(2≤0.73 for the 50 and 75% exercises. This study suggests that fatiguing exercise intensity does not influence the extent of postural stability impairment, but does influence the type of fatigue induced and the neuromuscular function predictors explaining changes in postural variables.

Does Stress Result in You Exercising Less? Or Does Exercising Result in You Being Less Stressed? Or Is It Both? Testing the Bi-directional Stress-Exercise Association at the Group and Person (N of 1) Level.

Science.gov (United States)

Burg, Matthew M; Schwartz, Joseph E; Kronish, Ian M; Diaz, Keith M; Alcantara, Carmela; Duer-Hefele, Joan; Davidson, Karina W

2017-12-01

Psychosocial stress contributes to heart disease in part by adversely affecting maintenance of health behaviors, while exercise can reduce stress. Assessing the bi-directional relationship between stress and exercise has been limited by lack of real-time data and theoretical and statistical models. This lack may hinder efforts to promote exercise maintenance. We test the bi-directional relationship between stress and exercise using real-time data for the average person and the variability-individual differences-in this relationship. An observational study was conducted within a single cohort randomized controlled experiment. Healthy young adults, (n = 79) who reported only intermittent exercise, completed 12 months of stress monitoring by ecological momentary assessment (at the beginning of, end of, and during the day) and continuous activity monitoring by Fitbit. A random coefficients linear mixed model was used to predict end-of-day stress from the occurrence/non-occurrence of exercise that day; a logistic mixed model was used to predict the occurrence/non-occurrence of exercise from ratings of anticipated stress. Separate regression analyses were also performed for each participant. Sensitivity analysis tested all models, restricted to the first 180 days of observation (prior to randomization). We found a significant average inverse (i.e., negative) effect of exercise on stress and of stress on exercise. There was significant between-person variability. Of N = 69, exercise was associated with a stress reduction for 15, a stress increase for 2, and no change for the remainder. We also found that an increase in anticipated stress reported the previous night or that morning was associated with a significant 20-22% decrease (OR = 0.78-0.80) in the odds of exercising that day. Of N = 69, this increase in stress reduced the likelihood of exercise for 17, increased the odds for 1, and had no effect for the remainder. We were unable to identify psychosocial

Linear regression metamodeling as a tool to summarize and present simulation model results.

Science.gov (United States)

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

Hierarchical Neural Regression Models for Customer Churn Prediction

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Golshan Mohammadi

2013-01-01

Full Text Available As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN, self-organizing maps (SOM, alpha-cut fuzzy c-means (α-FCM, and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, and α-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, the α-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.

LINEAR REGRESSION MODEL ESTİMATİON FOR RIGHT CENSORED DATA

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Ersin Yılmaz

2016-05-01

Full Text Available In this study, firstly we will define a right censored data. If we say shortly right-censored data is censoring values that above the exact line. This may be related with scaling device. And then  we will use response variable acquainted from right-censored explanatory variables. Then the linear regression model will be estimated. For censored data’s existence, Kaplan-Meier weights will be used for  the estimation of the model. With the weights regression model  will be consistent and unbiased with that.   And also there is a method for the censored data that is a semi parametric regression and this method also give  useful results  for censored data too. This study also might be useful for the health studies because of the censored data used in medical issues generally.

Developing and testing a global-scale regression model to quantify mean annual streamflow

Science.gov (United States)

Barbarossa, Valerio; Huijbregts, Mark A. J.; Hendriks, A. Jan; Beusen, Arthur H. W.; Clavreul, Julie; King, Henry; Schipper, Aafke M.

2017-01-01

Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF based on a dataset unprecedented in size, using observations of discharge and catchment characteristics from 1885 catchments worldwide, measuring between 2 and 106 km2. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area and catchment averaged mean annual precipitation and air temperature, slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error (RMSE) values were lower (0.29-0.38 compared to 0.49-0.57) and the modified index of agreement (d) was higher (0.80-0.83 compared to 0.72-0.75). Our regression model can be applied globally to estimate MAF at any point of the river network, thus providing a feasible alternative to spatially explicit process-based global hydrological models.

ERRICCA radon model intercomparison exercise

International Nuclear Information System (INIS)

Andersen, C.E.; Albarracin, D.; Csige, I.; Graaf, E.R. van der; Jiranek, M.; Rehs, B.; Svoboda, Z.; Toro, L.

1999-04-01

Numerical models based on finite-difference or finite-element methods are used by various research groups in studies of radon-222 transport through soil and building materials. Applications range from design of radon remediation systems to more fundamental studies of radon transport. To ascertain that results obtained with these models are of good quality, it is necessary that such models are tested. This document reports on a benchmark test organized by the EU project ERRICCA: European Research into Radon in Construction Concerted Action. The test comprises the following cases: 1) Steady-state diffusive radon profiles in dry and wet soils, 2) steady-state entry of soil gas and radon into a house, 3) time-dependent radon exhalation from a building-material sample. These cases cover features such as: soil heterogeneity, anisotropy, 3D-effects, time dependency, combined advective and diffusive transport of radon, flux calculations, and partitioning of radon between air and water in soil pores. Seven groups participated in the intercomparison. All groups submitted results without knowing the results of others. For these results, relatively large group-to-group discrepancies were observed. Because of this, all groups scrutinized their computations (once more) and engaged in follow-up discussions with others. During this debugging process, problems were indeed identified (and eliminated). The accordingly revised results were in better agreement than those reported initially. Some discrepancies, however, still remain. All in all, it seems that the exercise has served its purpose and stimulated improvements relating to the quality of numerical modelling of radon transport. To maintain a high quality of modelling, it is recommended that additional exercises are carried out. (au)

Running exercise enhances motor functional recovery with inhibition of dendritic regression in the motor cortex after collagenase-induced intracerebral hemorrhage in rats.

Science.gov (United States)

Takamatsu, Yasuyuki; Tamakoshi, Keigo; Waseda, Yuya; Ishida, Kazuto

2016-03-01

Rehabilitative approaches benefit motor functional recovery after stroke and relate to neuronal plasticity. We investigated the effects of a treadmill running exercise on the motor functional recovery and neuronal plasticity after collagenase-induced striatal intracerebral hemorrhage (ICH) in rats. Male Wistar rats were injected with type IV collagenase into the left striatum to induce ICH. Sham-operated animals were injected with saline instead of collagenase. The animals were randomly assigned to the sham control (SC), the sham exercise (SE), the ICH control (IC), or the ICH exercise (IE) group. The exercise groups were forced to run on a treadmill at a speed of 9 m/min for 30 min/day between days 4 and 14 after surgery. Behavioral tests were performed using a motor deficit score, a beam-walking test and a cylinder test. At fifteen days after surgery, the animals were sacrificed, and their brains were removed. The motor function of the IE group significantly improved compared with the motor function of the IC group. No significant differences in cortical thickness were found between the groups. The IC group had fewer branches and shorter dendrite lengths compared with the sham groups. However, dendritic branches and lengths were not significantly different between the IE and the other groups. Tropomyosin-related kinase B (TrkB) expression levels increased in the IE compared with IC group, but no significant differences in other protein (brain-derived neurotrophic factor, BDNF; Nogo-A; Rho-A/Rho-associated protein kinase 2, ROCK2) expression levels were found between the groups. These results suggest that improved motor function after a treadmill running exercise after ICH may be related to the prevention of dendritic regression due to TrkB upregulation. Copyright © 2015. Published by Elsevier B.V.

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

Science.gov (United States)

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Adaptive regression for modeling nonlinear relationships

CERN Document Server

Knafl, George J

2016-01-01

This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the s...

Confidence bands for inverse regression models

International Nuclear Information System (INIS)

Birke, Melanie; Bissantz, Nicolai; Holzmann, Hajo

2010-01-01

We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two periodic functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt (1973 Ann. Stat. 1 1071–95) we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap and prove consistency of the bootstrap procedure. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract

Electricity consumption forecasting in Italy using linear regression models

Energy Technology Data Exchange (ETDEWEB)

Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio [DIAM, Seconda Universita degli Studi di Napoli, Via Roma 29, 81031 Aversa (CE) (Italy)

2009-09-15

The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of {+-}1% for the best case and {+-}11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

Electricity consumption forecasting in Italy using linear regression models

International Nuclear Information System (INIS)

Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio

2009-01-01

The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of ±1% for the best case and ±11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Directory of Open Access Journals (Sweden)

Drzewiecki Wojciech

2016-12-01

Full Text Available In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques.

Anatomical knowledge gain through a clay-modeling exercise compared to live and video observations.

Science.gov (United States)

Kooloos, Jan G M; Schepens-Franke, Annelieke N; Bergman, Esther M; Donders, Rogier A R T; Vorstenbosch, Marc A T M

2014-01-01

Clay modeling is increasingly used as a teaching method other than dissection. The haptic experience during clay modeling is supposed to correspond to the learning effect of manipulations during exercises in the dissection room involving tissues and organs. We questioned this assumption in two pretest-post-test experiments. In these experiments, the learning effects of clay modeling were compared to either live observations (Experiment I) or video observations (Experiment II) of the clay-modeling exercise. The effects of learning were measured with multiple choice questions, extended matching questions, and recognition of structures on illustrations of cross-sections. Analysis of covariance with pretest scores as the covariate was used to elaborate the results. Experiment I showed a significantly higher post-test score for the observers, whereas Experiment II showed a significantly higher post-test score for the clay modelers. This study shows that (1) students who perform clay-modeling exercises show less gain in anatomical knowledge than students who attentively observe the same exercise being carried out and (2) performing a clay-modeling exercise is better in anatomical knowledge gain compared to the study of a video of the recorded exercise. The most important learning effect seems to be the engagement in the exercise, focusing attention and stimulating time on task. © 2014 American Association of Anatomists.

Dynamics and rate-dependence of the spatial angle between ventricular depolarization and repolarization wave fronts during exercise ECG.

Science.gov (United States)

Kenttä, Tuomas; Karsikas, Mari; Kiviniemi, Antti; Tulppo, Mikko; Seppänen, Tapio; Huikuri, Heikki V

2010-07-01

QRS/T angle and the cosine of the angle between QRS and T-wave vectors (TCRT), measured from standard 12-lead electrocardiogram (ECG), have been used in risk stratification of patients. This study assessed the possible rate dependence of these variables during exercise ECG in healthy subjects. Forty healthy volunteers, 20 men and 20 women, aged 34.6 +/- 3.4, underwent an exercise ECG testing. Twelve-lead ECG was recorded from each test subject and the spatial QRS/T angle and TCRT were automatically analyzed in a beat-to-beat manner with custom-made software. The individual TCRT/RR and QRST/RR patterns were fitted with seven different regression models, including a linear model and six nonlinear models. TCRT and QRS/T angle showed a significant rate dependence, with decreased values at higher heart rates (HR). In individual subjects, the second-degree polynomic model was the best regression model for TCRT/RR and QRST/RR slopes. It provided the best fit for both exercise and recovery. The overall TCRT/RR and QRST/RR slopes were similar between men and women during exercise and recovery. However, women had predominantly higher TCRT and QRS/T values. With respect to time, the dynamics of TCRT differed significantly between men and women; with a steeper exercise slope in women (women, -0.04/min vs -0.02/min in men, P exercise. The individual patterns of TCRT and QRS/T angle are affected by HR and gender. Delayed rate adaptation creates hysteresis in the TCRT/RR slopes.

Modelling infant mortality rate in Central Java, Indonesia use generalized poisson regression method

Science.gov (United States)

Prahutama, Alan; Sudarno

2018-05-01

The infant mortality rate is the number of deaths under one year of age occurring among the live births in a given geographical area during a given year, per 1,000 live births occurring among the population of the given geographical area during the same year. This problem needs to be addressed because it is an important element of a country’s economic development. High infant mortality rate will disrupt the stability of a country as it relates to the sustainability of the population in the country. One of regression model that can be used to analyze the relationship between dependent variable Y in the form of discrete data and independent variable X is Poisson regression model. Recently The regression modeling used for data with dependent variable is discrete, among others, poisson regression, negative binomial regression and generalized poisson regression. In this research, generalized poisson regression modeling gives better AIC value than poisson regression. The most significant variable is the Number of health facilities (X1), while the variable that gives the most influence to infant mortality rate is the average breastfeeding (X9).

Augmented Beta rectangular regression models: A Bayesian perspective.

Science.gov (United States)

Wang, Jue; Luo, Sheng

2016-01-01

Mixed effects Beta regression models based on Beta distributions have been widely used to analyze longitudinal percentage or proportional data ranging between zero and one. However, Beta distributions are not flexible to extreme outliers or excessive events around tail areas, and they do not account for the presence of the boundary values zeros and ones because these values are not in the support of the Beta distributions. To address these issues, we propose a mixed effects model using Beta rectangular distribution and augment it with the probabilities of zero and one. We conduct extensive simulation studies to assess the performance of mixed effects models based on both the Beta and Beta rectangular distributions under various scenarios. The simulation studies suggest that the regression models based on Beta rectangular distributions improve the accuracy of parameter estimates in the presence of outliers and heavy tails. The proposed models are applied to the motivating Neuroprotection Exploratory Trials in Parkinson's Disease (PD) Long-term Study-1 (LS-1 study, n = 1741), developed by The National Institute of Neurological Disorders and Stroke Exploratory Trials in Parkinson's Disease (NINDS NET-PD) network. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bayesian semiparametric regression models to characterize molecular evolution

Directory of Open Access Journals (Sweden)

Datta Saheli

2012-10-01

Full Text Available Abstract Background Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties. We propose a Bayesian hierarchical regression model with a generalization of the Dirichlet process prior on the distribution of the regression coefficients that describes the relationship between the changes in amino acid distances and natural selection in protein-coding DNA sequence alignments. Results The Bayesian semiparametric approach is illustrated with simulated data and the abalone lysin sperm data. Our method identifies groups of properties which, for this particular dataset, have a similar effect on evolution. The model also provides nonparametric site-specific estimates for the strength of conservation of these properties. Conclusions The model described here is distinguished by its ability to handle a large number of amino acid properties simultaneously, while taking into account that such data can be correlated. The multi-level clustering ability of the model allows for appealing interpretations of the results in terms of properties that are roughly equivalent from the standpoint of molecular evolution.

Regression analysis of a chemical reaction fouling model

International Nuclear Information System (INIS)

Vasak, F.; Epstein, N.

1996-01-01

A previously reported mathematical model for the initial chemical reaction fouling of a heated tube is critically examined in the light of the experimental data for which it was developed. A regression analysis of the model with respect to that data shows that the reference point upon which the two adjustable parameters of the model were originally based was well chosen, albeit fortuitously. (author). 3 refs., 2 tabs., 2 figs

Correlation-regression model for physico-chemical quality of ...

African Journals Online (AJOL)

abusaad

areas, suggesting that groundwater quality in urban areas is closely related with land use ... the ground water, with correlation and regression model is also presented. ...... WHO (World Health Organization) (1985). Health hazards from nitrates.

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

Science.gov (United States)

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

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

Multivariate Frequency-Severity Regression Models in Insurance

Directory of Open Access Journals (Sweden)

Edward W. Frees

2016-02-01

Full Text Available In insurance and related industries including healthcare, it is common to have several outcome measures that the analyst wishes to understand using explanatory variables. For example, in automobile insurance, an accident may result in payments for damage to one’s own vehicle, damage to another party’s vehicle, or personal injury. It is also common to be interested in the frequency of accidents in addition to the severity of the claim amounts. This paper synthesizes and extends the literature on multivariate frequency-severity regression modeling with a focus on insurance industry applications. Regression models for understanding the distribution of each outcome continue to be developed yet there now exists a solid body of literature for the marginal outcomes. This paper contributes to this body of literature by focusing on the use of a copula for modeling the dependence among these outcomes; a major advantage of this tool is that it preserves the body of work established for marginal models. We illustrate this approach using data from the Wisconsin Local Government Property Insurance Fund. This fund offers insurance protection for (i property; (ii motor vehicle; and (iii contractors’ equipment claims. In addition to several claim types and frequency-severity components, outcomes can be further categorized by time and space, requiring complex dependency modeling. We find significant dependencies for these data; specifically, we find that dependencies among lines are stronger than the dependencies between the frequency and average severity within each line.

A virtual model of the bench press exercise.

Science.gov (United States)

Rahmani, Abderrahmane; Rambaud, Olivier; Bourdin, Muriel; Mariot, Jean-Pierre

2009-08-07

The objective of this study was to design and validate a three degrees of freedom model in the sagittal plane for the bench press exercise. The mechanical model was based on rigid segments connected by revolute and prismatic pairs, which enabled a kinematic approach and global force estimation. The method requires only three simple measurements: (i) horizontal position of the hand (x(0)); (ii) vertical displacement of the barbell (Z) and (iii) elbow angle (theta). Eight adult male throwers performed maximal concentric bench press exercises against different masses. The kinematic results showed that the vertical displacement of each segment and the global centre of mass followed the vertical displacement of the lifted mass. Consequently, the vertical velocity and acceleration of the combined centre of mass and the lifted mass were identical. Finally, for each lifted mass, there were no practical differences between forces calculated from the bench press model and those simultaneously measured with a force platform. The error was lower than 2.5%. The validity of the mechanical method was also highlighted by a standard error of the estimate (SEE) ranging from 2.0 to 6.6N in absolute terms, a coefficient of variation (CV) or =0.99 for all the lifts (pbench press exercises in both field and laboratory conditions.

Quantile Regression Methods

DEFF Research Database (Denmark)

Fitzenberger, Bernd; Wilke, Ralf Andreas

2015-01-01

if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...

Third Radiation Transfer Model Intercomparison (RAMI) exercise : Documenting progress in canopy reflectance models

NARCIS (Netherlands)

Widlowski, J.-L.; Taberner, M.; Pinty, B.; Bruniquel-Pinel, V.; Disney, M.; Fernandes, R.; Gastellu-Etchegorry, J.P.; Gobron, N.; Kuusk, A.; Lavergne, T.; Leblanc, S.; Lewis, P.E.; Martin, E.; Mottus, M.; North, P.R.J.; Qin, W.; Robustelli, M.; Rochdi, N.; Ruiloba, R.; Soler, C.; Thompson, R.; Verhoef, W.; Xie, D.; Thompson, R.

2007-01-01

The Radiation Transfer Model Intercomparison (RAMI) initiative benchmarks canopy reflectance models under well‐controlled experimental conditions. Launched for the first time in 1999, this triennial community exercise encourages the systematic evaluation of canopy reflectance models on a voluntary

Application of random regression models to the genetic evaluation ...

African Journals Online (AJOL)

The model included fixed regression on AM (range from 30 to 138 mo) and the effect of herd-measurement date concatenation. Random parts of the model were RRM coefficients for additive and permanent environmental effects, while residual effects were modelled to account for heterogeneity of variance by AY. Estimates ...

Using built environment characteristics to predict walking for exercise

Directory of Open Access Journals (Sweden)

Siscovick David S

2008-02-01

Full Text Available Abstract Background Environments conducive to walking may help people avoid sedentary lifestyles and associated diseases. Recent studies developed walkability models combining several built environment characteristics to optimally predict walking. Developing and testing such models with the same data could lead to overestimating one's ability to predict walking in an independent sample of the population. More accurate estimates of model fit can be obtained by splitting a single study population into training and validation sets (holdout approach or through developing and evaluating models in different populations. We used these two approaches to test whether built environment characteristics near the home predict walking for exercise. Study participants lived in western Washington State and were adult members of a health maintenance organization. The physical activity data used in this study were collected by telephone interview and were selected for their relevance to cardiovascular disease. In order to limit confounding by prior health conditions, the sample was restricted to participants in good self-reported health and without a documented history of cardiovascular disease. Results For 1,608 participants meeting the inclusion criteria, the mean age was 64 years, 90 percent were white, 37 percent had a college degree, and 62 percent of participants reported that they walked for exercise. Single built environment characteristics, such as residential density or connectivity, did not significantly predict walking for exercise. Regression models using multiple built environment characteristics to predict walking were not successful at predicting walking for exercise in an independent population sample. In the validation set, none of the logistic models had a C-statistic confidence interval excluding the null value of 0.5, and none of the linear models explained more than one percent of the variance in time spent walking for exercise. We did not

Multitask Quantile Regression under the Transnormal Model.

Science.gov (United States)

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2016-01-01

We consider estimating multi-task quantile regression under the transnormal model, with focus on high-dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based ℓ 1 penalization with positive definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of alternating direction method of multipliers, nearest correlation matrix projection is introduced that inherits sampling properties of the unprojected one. Our work combines strengths of quantile regression and rank-based covariance regularization to simultaneously deal with nonlinearity and nonnormality for high-dimensional regression. Furthermore, the proposed method strikes a good balance between robustness and efficiency, achieves the "oracle"-like convergence rate, and provides the provable prediction interval under the high-dimensional setting. The finite-sample performance of the proposed method is also examined. The performance of our proposed rank-based method is demonstrated in a real application to analyze the protein mass spectroscopy data.

Affective Evaluations of Exercising: The Role of Automatic-Reflective Evaluation Discrepancy.

Science.gov (United States)

Brand, Ralf; Antoniewicz, Franziska

2016-12-01

Sometimes our automatic evaluations do not correspond well with those we can reflect on and articulate. We present a novel approach to the assessment of automatic and reflective affective evaluations of exercising. Based on the assumptions of the associative-propositional processes in evaluation model, we measured participants' automatic evaluations of exercise and then shared this information with them, asked them to reflect on it and rate eventual discrepancy between their reflective evaluation and the assessment of their automatic evaluation. We found that mismatch between self-reported ideal exercise frequency and actual exercise frequency over the previous 14 weeks could be regressed on the discrepancy between a relatively negative automatic and a more positive reflective evaluation. This study illustrates the potential of a dual-process approach to the measurement of evaluative responses and suggests that mistrusting one's negative spontaneous reaction to exercise and asserting a very positive reflective evaluation instead leads to the adoption of inflated exercise goals.

Squat Biomechanical Modeling Results from Exercising on the Hybrid Ultimate Lifting Kit

Science.gov (United States)

Gallo, Christopher A.; Thompson, William K.; Lewandowski, Beth E.; Jagodnik, Kathleen M.

2016-01-01

Long duration space travel will expose astronauts to extended periods of reduced gravity. Since gravity is not present to aid loading, astronauts will use resistive and aerobic exercise regimes for the duration of the space flight to minimize loss of bone density, muscle mass and aerobic capacity that occurs during exposure to a reduced gravity environment. Unlike the International Space Station (ISS), the area available for an exercise device in the next generation of spacecraft is limited and therefore compact resistance exercise device prototypes are being developed. The Advanced Resistive Exercise Device (ARED) currently on the ISS is being used as a benchmark for the functional performance of these new devices. Biomechanical data collection and computational modeling aid the device design process by quantifying the joint torques and the musculoskeletal forces that occur during exercises performed on the prototype devices. The computational models currently under development utilize the OpenSim software, an open source code for musculoskeletal modeling, with biomechanical input data from test subjects for estimation of muscle and joint loads. The subjects are instrumented with reflective markers for motion capture data collection while exercising on the Hybrid Ultimate Lifting Kit (HULK) prototype device. Ground reaction force data is collected with force plates under the feet and device loading is recorded through load cells internal to the HULK. Test variables include applied device load, narrow or wide foot stance, slow or fast cadence and the harness or long bar interface between the test subject and the device. Data is also obtained using free weights for a comparison to the resistively loaded exercise device. This data is input into the OpenSim biomechanical model, which has been scaled to match the anthropometrics of the test subject, to calculate the body loads. The focus of this presentation is to summarize the results from the full squat exercises

Approximating prediction uncertainty for random forest regression models

Science.gov (United States)

John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

2016-01-01

Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

Profile-driven regression for modeling and runtime optimization of mobile networks

DEFF Research Database (Denmark)

McClary, Dan; Syrotiuk, Violet; Kulahci, Murat

2010-01-01

Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization...... of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike...

Accounting for measurement error in log regression models with applications to accelerated testing.

Science.gov (United States)

Richardson, Robert; Tolley, H Dennis; Evenson, William E; Lunt, Barry M

2018-01-01

In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

Accounting for measurement error in log regression models with applications to accelerated testing.

Directory of Open Access Journals (Sweden)

Robert Richardson

Full Text Available In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

Science.gov (United States)

Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

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

Science.gov (United States)

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

2018-04-01

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

Direct modeling of regression effects for transition probabilities in the progressive illness-death model

DEFF Research Database (Denmark)

Azarang, Leyla; Scheike, Thomas; de Uña-Álvarez, Jacobo

2017-01-01

In this work, we present direct regression analysis for the transition probabilities in the possibly non-Markov progressive illness–death model. The method is based on binomial regression, where the response is the indicator of the occupancy for the given state along time. Randomly weighted score...

SPSS macros to compare any two fitted values from a regression model.

Science.gov (United States)

Weaver, Bruce; Dubois, Sacha

2012-12-01

In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests-particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.

Parameter estimation and statistical test of geographically weighted bivariate Poisson inverse Gaussian regression models

Science.gov (United States)

Amalia, Junita; Purhadi, Otok, Bambang Widjanarko

2017-11-01

Poisson distribution is a discrete distribution with count data as the random variables and it has one parameter defines both mean and variance. Poisson regression assumes mean and variance should be same (equidispersion). Nonetheless, some case of the count data unsatisfied this assumption because variance exceeds mean (over-dispersion). The ignorance of over-dispersion causes underestimates in standard error. Furthermore, it causes incorrect decision in the statistical test. Previously, paired count data has a correlation and it has bivariate Poisson distribution. If there is over-dispersion, modeling paired count data is not sufficient with simple bivariate Poisson regression. Bivariate Poisson Inverse Gaussian Regression (BPIGR) model is mix Poisson regression for modeling paired count data within over-dispersion. BPIGR model produces a global model for all locations. In another hand, each location has different geographic conditions, social, cultural and economic so that Geographically Weighted Regression (GWR) is needed. The weighting function of each location in GWR generates a different local model. Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model is used to solve over-dispersion and to generate local models. Parameter estimation of GWBPIGR model obtained by Maximum Likelihood Estimation (MLE) method. Meanwhile, hypothesis testing of GWBPIGR model acquired by Maximum Likelihood Ratio Test (MLRT) method.

Accuracy of physical self-description among chronic exercisers and non-exercisers

Directory of Open Access Journals (Sweden)

Joseph M. Berning

2014-10-01

Full Text Available This study addressed the role of chronic exercise to enhance physical self-description as measured by self-estimated percent body fat. Accuracy of physical self-description was determined in normal-weight, regularly exercising and non-exercising males with similar body mass index (BMI’s and females with similar BMI’s (n=42 males and 45 females of which 23 males and 23 females met criteria to be considered chronic exercisers. Statistical analyses were conducted to determine the degree of agreement between self-estimated percent body fat and actual laboratory measurements (hydrostatic weighing. Three statistical techniques were employed: Pearson correlation coefficients, Bland and Altman plots, and regression analysis. Agreement between measured and self-estimated percent body fat was superior for males and females who exercised chronically, compared to non-exercisers. The clinical implications are as follows. Satisfaction with one’s body can be influenced by several factors, including self-perceived body composition. Dissatisfaction can contribute to maladaptive and destructive weight management behaviors. The present study suggests that regular exercise provides a basis for more positive weight management behaviors by enhancing the accuracy of self-assessed body composition.

Can We Use Regression Modeling to Quantify Mean Annual Streamflow at a Global-Scale?

Science.gov (United States)

Barbarossa, V.; Huijbregts, M. A. J.; Hendriks, J. A.; Beusen, A.; Clavreul, J.; King, H.; Schipper, A.

2016-12-01

Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for a number of applications, including assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF using observations of discharge and catchment characteristics from 1,885 catchments worldwide, ranging from 2 to 106 km2 in size. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB [van Beek et al., 2011] by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area, mean annual precipitation and air temperature, average slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error values were lower (0.29 - 0.38 compared to 0.49 - 0.57) and the modified index of agreement was higher (0.80 - 0.83 compared to 0.72 - 0.75). Our regression model can be applied globally at any point of the river network, provided that the input parameters are within the range of values employed in the calibration of the model. The performance is reduced for water scarce regions and further research should focus on improving such an aspect for regression-based global hydrological models.

Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression

Science.gov (United States)

Khikmah, L.; Wijayanto, H.; Syafitri, U. D.

2017-04-01

The problem often encounters in logistic regression modeling are multicollinearity problems. Data that have multicollinearity between explanatory variables with the result in the estimation of parameters to be bias. Besides, the multicollinearity will result in error in the classification. In general, to overcome multicollinearity in regression used stepwise regression. They are also another method to overcome multicollinearity which involves all variable for prediction. That is Principal Component Analysis (PCA). However, classical PCA in only for numeric data. Its data are categorical, one method to solve the problems is Categorical Principal Component Analysis (CATPCA). Data were used in this research were a part of data Demographic and Population Survey Indonesia (IDHS) 2012. This research focuses on the characteristic of women of using the contraceptive methods. Classification results evaluated using Area Under Curve (AUC) values. The higher the AUC value, the better. Based on AUC values, the classification of the contraceptive method using stepwise method (58.66%) is better than the logistic regression model (57.39%) and CATPCA (57.39%). Evaluation of the results of logistic regression using sensitivity, shows the opposite where CATPCA method (99.79%) is better than logistic regression method (92.43%) and stepwise (92.05%). Therefore in this study focuses on major class classification (using a contraceptive method), then the selected model is CATPCA because it can raise the level of the major class model accuracy.

Time series modeling by a regression approach based on a latent process.

Science.gov (United States)

Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice

2009-01-01

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.

Regression to Causality : Regression-style presentation influences causal attribution

DEFF Research Database (Denmark)

Bordacconi, Mats Joe; Larsen, Martin Vinæs

2014-01-01

of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

Science.gov (United States)

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.

Science.gov (United States)

Schmidt, Amand F; Klungel, Olaf H; Groenwold, Rolf H H

2016-01-01

Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.

Regression Model to Predict Global Solar Irradiance in Malaysia

Directory of Open Access Journals (Sweden)

Hairuniza Ahmed Kutty

2015-01-01

Full Text Available A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE, mean bias error (MBE, and the coefficient of determination (R2 with other models available from literature studies. Seven models based on single parameters (PM1 to PM7 and five multiple-parameter models (PM7 to PM12 are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%, R2 ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.

Incremental value of clinical assessment, supine exercise electrocardiography, and biplane exercise radionuclide ventriculography in the prediction of coronary artery disease in men with chest pain

International Nuclear Information System (INIS)

Currie, P.J.; Kelly, M.J.; Harper, R.W.; Federman, J.; Kalff, V.; Anderson, S.T.; Pitt, A.

1983-01-01

The incremental value of clinical assessment, exercise electrocardiography (ECG) and biplane radionuclide ventriculography (RVG) in the prediction of coronary artery disease (CAD) was assessed in 105 men without myocardial infarction who were undergoing coronary angiography for investigation of chest pain. Independent clinical assessment of chest pain was made prospectively by 2 physicians. Graded supine bicycle exercise testing was symptom-limited. Right anterior oblique ECG-gated first-pass RVG and left anterior oblique ECG-gated equilibrium RVG were performed at rest and exercise. Regional wall motion abnormalities were defined by agreement of 2 of 3 blinded observers. A combined strongly positive exercise ECG response was defined as greater than or equal to 2 mm ST depression or 1.0 to 1.9 mm ST depression with exercise-induced chest pain. A multivariate logistic regression model for the preexercise prediction of CAD was derived from the clinical data and selected 2 variables: chest pain class and cholesterol level. A second model assessed the incremental value of the exercise test in prediction of CAD and found 2 exercise variables that improved prediction: RVG wall motion abnormalities, and a combined strongly positive ECG response. Applying the derived predictive models, 37 of the 58 patients (64%) with preexercise probabilities of 10 to 90% crossed either below the 10% probability threshold or above the 90% threshold and 28 (48%) also moved across the 5 and 95% thresholds. Supine exercise testing with ECG and biplane RVG together, but neither test alone, effectively adds to clinical prediction of CAD. It is most useful in men with atypical chest pain and when the ECG and RVG results are concordant

Digital Astronaut Project Biomechanical Models: Biomechanical Modeling of Squat, Single-Leg Squat and Heel Raise Exercises on the Hybrid Ultimate Lifting Kit (HULK)

Science.gov (United States)

Thompson, William K.; Gallo, Christopher A.; Crentsil, Lawton; Lewandowski, Beth E.; Humphreys, Brad T.; DeWitt, John K.; Fincke, Renita S.; Mulugeta, Lealem

2015-01-01

The NASA Digital Astronaut Project (DAP) implements well-vetted computational models to predict and assess spaceflight health and performance risks, and to enhance countermeasure development. The DAP Musculoskeletal Modeling effort is developing computational models to inform exercise countermeasure development and to predict physical performance capabilities after a length of time in space. For example, integrated exercise device-biomechanical models can determine localized loading, which will be used as input to muscle and bone adaptation models to estimate the effectiveness of the exercise countermeasure. In addition, simulations of mission tasks can be used to estimate the astronaut's ability to perform the task after exposure to microgravity and after using various exercise countermeasures. The software package OpenSim (Stanford University, Palo Alto, CA) (Ref. 1) is being used to create the DAP biomechanical models and its built-in muscle model is the starting point for the DAP muscle model. During Exploration missions, such as those to asteroids and Mars, astronauts will be exposed to reduced gravity for extended periods. Therefore, the crew must have access to exercise countermeasures that can maintain their musculoskeletal and aerobic health. Exploration vehicles may have very limited volume and power available to accommodate such capabilities, even more so than the International Space Station (ISS). The exercise devices flown on Exploration missions must be designed to provide sufficient load during the performance of various resistance and aerobic/anaerobic exercises while meeting potential additional requirements of limited mass, volume and power. Given that it is not practical to manufacture and test (ground, analog and/or flight) all candidate devices, nor is it always possible to obtain data such as localized muscle and bone loading empirically, computational modeling can estimate the localized loading during various exercise modalities performed on

Polynomial regression analysis and significance test of the regression function

International Nuclear Information System (INIS)

Gao Zhengming; Zhao Juan; He Shengping

2012-01-01

In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)

Mathematical finance theory review and exercises from binomial model to risk measures

CERN Document Server

Gianin, Emanuela Rosazza

2013-01-01

The book collects over 120 exercises on different subjects of Mathematical Finance, including Option Pricing, Risk Theory, and Interest Rate Models. Many of the exercises are solved, while others are only proposed. Every chapter contains an introductory section illustrating the main theoretical results necessary to solve the exercises. The book is intended as an exercise textbook to accompany graduate courses in mathematical finance offered at many universities as part of degree programs in Applied and Industrial Mathematics, Mathematical Engineering, and Quantitative Finance.

Logistic regression for risk factor modelling in stuttering research.

Science.gov (United States)

Reed, Phil; Wu, Yaqionq

2013-06-01

To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.

Efficient estimation of an additive quantile regression model

NARCIS (Netherlands)

Cheng, Y.; de Gooijer, J.G.; Zerom, D.

2011-01-01

In this paper, two non-parametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a more viable alternative to existing kernel-based approaches. The second estimator

The third RAdiation transfer Model Intercomparison (RAMI) exercise: Documenting progress in canopy reflectance models

NARCIS (Netherlands)

Widlowski, J.L.; Taberner, M.; Pinty, B.; Bruniquel-Pinel, V.; Disney, M.I.; Fernandes, R.; Gastellu-Etchegorry, J.P.; Gobron, N.; Kuusk, A.; Lavergne, T.; LeBlanc, S.; Lewis, P.E.; Martin, E.; Mõttus, M.; North, P.R.J.; Qin, W.; Robustelli, M.; Rochdi, N.; Ruiloba, R.; Thompson, R.; Verhoef, W.; Verstraete, M.M.; Xie, D.

2007-01-01

[1] The Radiation Transfer Model Intercomparison ( RAMI) initiative benchmarks canopy reflectance models under well-controlled experimental conditions. Launched for the first time in 1999, this triennial community exercise encourages the systematic evaluation of canopy reflectance models on a

Advanced statistics: linear regression, part I: simple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

Advanced statistics: linear regression, part II: multiple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Exercise testing in asymptomatic or minimally symptomatic aortic regurgitation: relationship of left ventricular ejection fraction to left ventricular filling pressure during exercise

International Nuclear Information System (INIS)

Boucher, C.A.; Wilson, R.A.; Kanarek, D.J.; Hutter, A.M. Jr.; Okada, R.D.; Liberthson, R.R.; Strauss, H.W.; Pohost, G.M.

1983-01-01

Exercise radionuclide angiography is being used to evaluate left ventricular function in patients with aortic regurgitation. Ejection fraction is the most common variable analyzed. To better understand the rest and exercise ejection fraction in this setting, 20 patients with asymptomatic or minimally symptomatic severe aortic regurgitation were studied. All underwent simultaneous supine exercise radionuclide angiography and pulmonary gas exchange measurement and underwent rest and exercise measurement of pulmonary artery wedge pressure (PAWP) during cardiac catheterization. Eight patients had a peak exercise PAWP less than 15 mm Hg (group 1) and 12 had a peak exercise PAWP greater than or equal to 15 mm Hg (group 2). Group 1 patients were younger and more were in New York Heart Association class I. The two groups had similar cardiothoracic ratios, changes in ejection fractions with exercise, and rest and exercise regurgitant indexes. Using multiple regression analysis, the best correlate of the exercise PAWP was peak oxygen uptake (r . -0.78, p less than 0.01). No other measurement added significantly to the regression. When peak oxygen uptake was excluded, rest and exercise ejection fraction also correlated significantly (r . -0.62 and r . -0.60, respectively, p less than 0.01). Patients with asymptomatic or minimally symptomatic severe aortic regurgitation have a wide spectrum of cardiac performance in terms of the PAWP during exercise. The absolute rest and exercise ejection fraction and the level of exercise achieved are noninvasive variables that correlate with exercise PAWP in aortic regurgitation, but the change in ejection fraction with exercise by itself is not

Understanding exercise uptake and adherence for people with chronic conditions: a new model demonstrating the importance of exercise identity, benefits of attending and support.

Science.gov (United States)

Pentecost, C; Taket, A

2011-10-01

Understanding the factors influencing uptake and adherence to exercise for people with chronic conditions from different ages, genders and ethnicities is important for planning exercise services. This paper presents evidence supporting a new model of exercise uptake and adherence applicable to people with chronic conditions from diverse socio-demographic backgrounds. The study is based on 130 semi-structured interviews with people with chronic conditions, including both those who did and those who did not attend exercise services, and supporters of those who attended. Analysis followed the guidelines of 'framework analysis'. Results show that three factors were particularly important in influencing adherence behavior: (i) exercise identity, (ii) support and (iii) perceived benefits of attending. Social and cultural identities impacted on willingness to exercise, importance of exercise and perceived appropriateness of exercising. Having at least one supporter providing different types of support was associated with high levels of attendance. Those people who valued the social and psychological benefits of attending were more likely to be high attenders. The new model illustrates interaction between these three factors and discusses how these can be taken into account when planning exercise services for people with chronic conditions drawn from diverse socio-demographic groups.

Crime Modeling using Spatial Regression Approach

Science.gov (United States)

Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.

2018-01-01

Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.

Perceived exercise barriers explain exercise participation in Australian women treated for breast cancer better than perceived exercise benefits.

Science.gov (United States)

Gho, Sheridan A; Munro, Bridget J; Jones, Sandra C; Steele, Julie R

2014-12-01

This study aimed to determine the effect of perceived exercise benefits and barriers on exercise levels among women who have been treated for breast cancer and have not participated in a formal exercise intervention. This was an anonymous, national, online cross-sectional survey study. Four hundred thirty-two women treated for breast cancer completed an online survey covering their treatment and demographic background, current exercise levels, and perceived exercise benefits and barriers. Each perceived benefit and barrier was considered in a binary logistic regression against reported exercise levels to ascertain significant relationships and associative values (odds ratio [OR]). Agreement with 16 out of 19 exercise barriers was significantly related to being more likely to report insufficient exercise levels, whereas agreement with 6 out of 15 exercise benefits was significantly related to being less likely to report insufficient levels of exercise. Feeling too weak, lacking self-discipline, and not making exercise a priority were the barriers with the largest association to insufficient exercise levels (OR=10.97, 95% confidence interval [CI]=3.90, 30.86; OR=8.12, 95% CI=4.73, 13.93; and OR=7.43, 95% CI=3.72, 14.83, respectively). Conversely, exercise enjoyment, improved feelings of well-being, and decreased feelings of stress and tension were the top 3 benefits associated with being less likely to have insufficient exercise levels (OR=0.21, 95% CI=0.11, 0.39; OR=0.21, 95% CI=0.07, 0.63; and OR=0.31, 95% CI=0.15, 0.63, respectively). Self-reported data measures were used to collect exercise data. Targeting exercise barriers specific to women treated for breast cancer may improve exercise participation levels in this cohort. Awareness of the impact of exercise barriers identified in the present study will enable physical therapists to better plan exercise interventions that support all women treated for breast cancer. © 2014 American Physical Therapy Association.

Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

Science.gov (United States)

Nikbakht, Roya; Bahrampour, Abbas

2017-01-01

Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

Efficient estimation of an additive quantile regression model

NARCIS (Netherlands)

Cheng, Y.; de Gooijer, J.G.; Zerom, D.

2009-01-01

In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By

Efficient estimation of an additive quantile regression model

NARCIS (Netherlands)

Cheng, Y.; de Gooijer, J.G.; Zerom, D.

2010-01-01

In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By

Boosted beta regression.

Directory of Open Access Journals (Sweden)

Matthias Schmid

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

Using the classical linear regression model in analysis of the dependences of conveyor belt life

Directory of Open Access Journals (Sweden)

Miriam Andrejiová

2013-12-01

Full Text Available The paper deals with the classical linear regression model of the dependence of conveyor belt life on some selected parameters: thickness of paint layer, width and length of the belt, conveyor speed and quantity of transported material. The first part of the article is about regression model design, point and interval estimation of parameters, verification of statistical significance of the model, and about the parameters of the proposed regression model. The second part of the article deals with identification of influential and extreme values that can have an impact on estimation of regression model parameters. The third part focuses on assumptions of the classical regression model, i.e. on verification of independence assumptions, normality and homoscedasticity of residuals.

Measurement of exercise habits and prediction of leisure-time activity in established exercise.

Science.gov (United States)

Tappe, Karyn A; Glanz, Karen

2013-01-01

Habit formation may be important to maintaining repetitive healthy behaviors like exercise. Existing habit questionnaires only measure part of the definition of habit (automaticity; frequency). A novel habit questionnaire was evaluated that measured contextual cueing. We designed a two-stage observational cohort study of regular exercisers. For stage 1, we conducted an in-person interview on a university campus. For stage 2, we conducted an internet-based survey. Participants were 156 adults exercising at least once per week. A novel measure, The Exercise Habit Survey (EHS) assessed contextual cueing through 13 questions on constancy of place, time, people, and exercise behaviors. A subset of the Self-Report Habit Index (SRHI), measuring automaticity, was also collected along with measures of intention and self-efficacy, and the International Physical Activity Questionnaire (IPAQ), leisure-time section. The EHS was evaluated using factor analysis and test-retest reliability. Its correlation to other exercise predictors and exercise behavior was evaluated using Pearson's r and hierarchical regression. Results suggested that the EHS comprised four subscales (People, Place, Time, Exercise Constancy). Only Exercise Constancy correlated significantly with SRHI. Only the People subscale predicted IPAQ exercise metabolic equivalents. The SRHI was a strong predictor. Contextual cueing is an important aspect of habit but measurement methodologies warrant refinement and comparison by different methods.

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

Science.gov (United States)

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

2014-01-01

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

Use of empirical likelihood to calibrate auxiliary information in partly linear monotone regression models.

Science.gov (United States)

Chen, Baojiang; Qin, Jing

2014-05-10

In statistical analysis, a regression model is needed if one is interested in finding the relationship between a response variable and covariates. When the response depends on the covariate, then it may also depend on the function of this covariate. If one has no knowledge of this functional form but expect for monotonic increasing or decreasing, then the isotonic regression model is preferable. Estimation of parameters for isotonic regression models is based on the pool-adjacent-violators algorithm (PAVA), where the monotonicity constraints are built in. With missing data, people often employ the augmented estimating method to improve estimation efficiency by incorporating auxiliary information through a working regression model. However, under the framework of the isotonic regression model, the PAVA does not work as the monotonicity constraints are violated. In this paper, we develop an empirical likelihood-based method for isotonic regression model to incorporate the auxiliary information. Because the monotonicity constraints still hold, the PAVA can be used for parameter estimation. Simulation studies demonstrate that the proposed method can yield more efficient estimates, and in some situations, the efficiency improvement is substantial. We apply this method to a dementia study. Copyright © 2013 John Wiley & Sons, Ltd.

[Factors regarding awareness of preventive care exercises: Distance to exercise facilities and their social networks].

Science.gov (United States)

Soma, Yuki; Tsunoda, Kenji; Kitano, Naruki; Jindo, Takashi; Okura, Tomohiro

2015-01-01

The present study examines factors affecting individuals' awareness of certain types of preventive care exercises, particularly the distance from their home to an exercise facility and their social networks. Participants were 3206 men (age, 73.0±6.2 years) and 3395 women (age, 73.2±6.4 years) aged ≥65 years who had not been certified as persons with care needs and who had responded to an inventory survey conducted in Kasama City, Japan, in 2013. We performed multiple logistic regression analysis to assess the characteristics associated with participants' awareness of two types of exercises for preventive care: "silver rehabili taisou" (SRT) and "square-stepping exercise" (SSE). Independent variables were distance from home to the exercise facility, social networks, transportation availability, physical function, cognitive function, and neighborhood population density. Older adults who were aware of the exercises lived significantly closer to an exercise facility (SRT, aware: 1,148.5±961.3 m vs. unaware: 1,284.2±1,027.4 m; SSE, aware: 1,415.9±1104.1 m vs. unaware: 1,615.7±1,172.2 m). Multiple logistic regression analysis showed that participation in community activities (men, SRT-odds ratio [OR]=2.54 and SSE-OR=2.19; women, SRT-OR=4.14 and SSE-OR=3.34] and visiting friends (men, SRT-OR=1.45 and SSE-OR=1.49; women SRT-OR=1.44 and SSE-OR=1.73) were promoting factors for awareness of both types of exercises. In men and women, low physical function (SRT-OR=0.73 and SSE-OR=0.56) and dependence on another person to drive them to the destination (SRT-OR=0.79 and SSE-OR=0.78) were inhibiting factors, respectively. A distance of >500 m between their home and the facility tended to be an inhibiting factor. A shorter distance from home to an exercise facility and better social networks increased awareness of preventive care exercises in both sexes and for both types of exercise. Establishing exercise centers and devising effective methods of imparting information to

Predictive market segmentation model: An application of logistic regression model and CHAID procedure

Directory of Open Access Journals (Sweden)

Soldić-Aleksić Jasna

2009-01-01

Full Text Available Market segmentation presents one of the key concepts of the modern marketing. The main goal of market segmentation is focused on creating groups (segments of customers that have similar characteristics, needs, wishes and/or similar behavior regarding the purchase of concrete product/service. Companies can create specific marketing plan for each of these segments and therefore gain short or long term competitive advantage on the market. Depending on the concrete marketing goal, different segmentation schemes and techniques may be applied. This paper presents a predictive market segmentation model based on the application of logistic regression model and CHAID analysis. The logistic regression model was used for the purpose of variables selection (from the initial pool of eleven variables which are statistically significant for explaining the dependent variable. Selected variables were afterwards included in the CHAID procedure that generated the predictive market segmentation model. The model results are presented on the concrete empirical example in the following form: summary model results, CHAID tree, Gain chart, Index chart, risk and classification tables.

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

Science.gov (United States)

Ulbrich, Norbert Manfred

2013-01-01

A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.

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

Science.gov (United States)

Madarang, Krish J; Kang, Joo-Hyon

2014-06-01

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

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Science.gov (United States)

Drzewiecki, Wojciech

2016-12-01

In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

Regularized multivariate regression models with skew-t error distributions

KAUST Repository

Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi

2014-01-01

We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both

The acute effect of exercise modality and nutrition manipulations on post-exercise resting energy expenditure and respiratory exchange ratio in women: a randomized trial.

Science.gov (United States)

Wingfield, Hailee L; Smith-Ryan, Abbie E; Melvin, Malia N; Roelofs, Erica J; Trexler, Eric T; Hackney, Anthony C; Weaver, Mark A; Ryan, Eric D

2015-12-01

The purpose of this study was to examine the effect of exercise modality and pre-exercise carbohydrate (CHO) or protein (PRO) ingestion on post-exercise resting energy expenditure (REE) and respiratory exchange ratio (RER) in women. Twenty recreationally active women (mean ± SD; age 24.6 ± 3.9 years; height 164.4 ± 6.6 cm; weight 62.7 ± 6.6 kg) participated in this randomized, crossover, double-blind study. Each participant completed six exercise sessions, consisting of three exercise modalities: aerobic endurance exercise (AEE), high-intensity interval running (HIIT), and high-intensity resistance training (HIRT); and two acute nutritional interventions: CHO and PRO. Salivary samples were collected before each exercise session to determine estradiol-β-17 and before and after to quantify cortisol. Post-exercise REE and RER were analyzed via indirect calorimetry at the following: baseline, immediately post (IP), 30 minutes (30 min) post, and 60 minutes (60 min) post exercise. A mixed effects linear regression model, controlling for estradiol, was used to compare mean longitudinal changes in REE and RER. On average, HIIT produced a greater REE than AEE and HIRT (p HIIT produced lower RER compared to either AEE or HIRT after 30 min (p HIIT resulted in the largest increase in REE and largest reduction in RER.

Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system

International Nuclear Information System (INIS)

Fang, Tingting; Lahdelma, Risto

2016-01-01

Highlights: • Social factor is considered for the linear regression models besides weather file. • Simultaneously optimize all the coefficients for linear regression models. • SARIMA combined with linear regression is used to forecast the heat demand. • The accuracy for both linear regression and time series models are evaluated. - Abstract: Forecasting heat demand is necessary for production and operation planning of district heating (DH) systems. In this study we first propose a simple regression model where the hourly outdoor temperature and wind speed forecast the heat demand. Weekly rhythm of heat consumption as a social component is added to the model to significantly improve the accuracy. The other type of model is the seasonal autoregressive integrated moving average (SARIMA) model with exogenous variables as a combination to take weather factors, and the historical heat consumption data as depending variables. One outstanding advantage of the model is that it peruses the high accuracy for both long-term and short-term forecast by considering both exogenous factors and time series. The forecasting performance of both linear regression models and time series model are evaluated based on real-life heat demand data for the city of Espoo in Finland by out-of-sample tests for the last 20 full weeks of the year. The results indicate that the proposed linear regression model (T168h) using 168-h demand pattern with midweek holidays classified as Saturdays or Sundays gives the highest accuracy and strong robustness among all the tested models based on the tested forecasting horizon and corresponding data. Considering the parsimony of the input, the ease of use and the high accuracy, the proposed T168h model is the best in practice. The heat demand forecasting model can also be developed for individual buildings if automated meter reading customer measurements are available. This would allow forecasting the heat demand based on more accurate heat consumption

Validation of regression models for nitrate concentrations in the upper groundwater in sandy soils

International Nuclear Information System (INIS)

Sonneveld, M.P.W.; Brus, D.J.; Roelsma, J.

2010-01-01

For Dutch sandy regions, linear regression models have been developed that predict nitrate concentrations in the upper groundwater on the basis of residual nitrate contents in the soil in autumn. The objective of our study was to validate these regression models for one particular sandy region dominated by dairy farming. No data from this area were used for calibrating the regression models. The model was validated by additional probability sampling. This sample was used to estimate errors in 1) the predicted areal fractions where the EU standard of 50 mg l -1 is exceeded for farms with low N surpluses (ALT) and farms with higher N surpluses (REF); 2) predicted cumulative frequency distributions of nitrate concentration for both groups of farms. Both the errors in the predicted areal fractions as well as the errors in the predicted cumulative frequency distributions indicate that the regression models are invalid for the sandy soils of this study area. - This study indicates that linear regression models that predict nitrate concentrations in the upper groundwater using residual soil N contents should be applied with care.

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

Science.gov (United States)

Caimmi, R.

2011-08-01

Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts ( York, 1966, 1969) is reviewed using a new formalism in terms of deviation (matrix) traces which, for unweighted data, reduce to usual quantities leaving aside an unessential (but dimensional) multiplicative factor. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. The classes of linear models considered are regression lines in the general case of correlated errors in X and in Y for weighted data, and in the opposite limiting situations of (i) uncorrelated errors in X and in Y, and (ii) completely correlated errors in X and in Y. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases, namely: (Y) errors in X negligible (ideally null) with respect to errors in Y; (X) errors in Y negligible (ideally null) with respect to errors in X; (O) genuine orthogonal regression; (R) reduced major-axis regression. In the limit of unweighted data, the results determined for functional models are compared with their counterparts related to extreme structural models i.e. the instrumental scatter is negligible (ideally null) with respect to the intrinsic scatter ( Isobe et al., 1990; Feigelson and Babu, 1992). While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with the exception of Y models. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors (∓ σ) for both

Statistical Shape Modelling and Markov Random Field Restoration (invited tutorial and exercise)

DEFF Research Database (Denmark)

Hilger, Klaus Baggesen

This tutorial focuses on statistical shape analysis using point distribution models (PDM) which is widely used in modelling biological shape variability over a set of annotated training data. Furthermore, Active Shape Models (ASM) and Active Appearance Models (AAM) are based on PDMs and have proven...... deformation field between shapes. The tutorial demonstrates both generative active shape and appearance models, and MRF restoration on 3D polygonized surfaces. ''Exercise: Spectral-Spatial classification of multivariate images'' From annotated training data this exercise applies spatial image restoration...... using Markov random field relaxation of a spectral classifier. Keywords: the Ising model, the Potts model, stochastic sampling, discriminant analysis, expectation maximization....

Modeling and prediction of flotation performance using support vector regression

Directory of Open Access Journals (Sweden)

Despotović Vladimir

2017-01-01

Full Text Available Continuous efforts have been made in recent year to improve the process of paper recycling, as it is of critical importance for saving the wood, water and energy resources. Flotation deinking is considered to be one of the key methods for separation of ink particles from the cellulose fibres. Attempts to model the flotation deinking process have often resulted in complex models that are difficult to implement and use. In this paper a model for prediction of flotation performance based on Support Vector Regression (SVR, is presented. Representative data samples were created in laboratory, under a variety of practical control variables for the flotation deinking process, including different reagents, pH values and flotation residence time. Predictive model was created that was trained on these data samples, and the flotation performance was assessed showing that Support Vector Regression is a promising method even when dataset used for training the model is limited.

Self-rating level of perceived exertion for guiding exercise intensity during a 12-week cardiac rehabilitation programme and the influence of heart rate reducing medication

DEFF Research Database (Denmark)

Tang, Lars H.; Zwisler, Ann-Dorthe; Taylor, Rod S

2016-01-01

OBJECTIVES: To investigate whether self-rating level of perceived exertion can adequately guide exercise intensity during a 12-week cardiac rehabilitation programme. DESIGN: Linear regression analysis using rehabilitation data from two randomised controlled trials. METHODS: Patients undergoing ra......-led and self-regulated model using rating of perceived exertion can help guide exercise intensity in everyday clinical practice among patients with heart disease, irrespective if they are taking heart rate-reducing medication....... radiofrequency ablation for atrial fibrillation or following heart valve surgery and participating in exercise-based rehabilitation were included. The 12-week rehabilitation outpatient programme comprised three weekly training sessions, each consisting of 20min aerobic exercise divided into three steps. Patients...... were asked to base their exercise intensity for each step on a predefined rating of perceived exertion specified in a training diary. Exercise intensity was objectively measured by heart rate during the last 2min for each exercise step. Comparative analysis and linear regression of the rating...

Forecasting daily meteorological time series using ARIMA and regression models

Science.gov (United States)

Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

2018-04-01

The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

Replica analysis of overfitting in regression models for time-to-event data

Science.gov (United States)

Coolen, A. C. C.; Barrett, J. E.; Paga, P.; Perez-Vicente, C. J.

2017-09-01

Overfitting, which happens when the number of parameters in a model is too large compared to the number of data points available for determining these parameters, is a serious and growing problem in survival analysis. While modern medicine presents us with data of unprecedented dimensionality, these data cannot yet be used effectively for clinical outcome prediction. Standard error measures in maximum likelihood regression, such as p-values and z-scores, are blind to overfitting, and even for Cox’s proportional hazards model (the main tool of medical statisticians), one finds in literature only rules of thumb on the number of samples required to avoid overfitting. In this paper we present a mathematical theory of overfitting in regression models for time-to-event data, which aims to increase our quantitative understanding of the problem and provide practical tools with which to correct regression outcomes for the impact of overfitting. It is based on the replica method, a statistical mechanical technique for the analysis of heterogeneous many-variable systems that has been used successfully for several decades in physics, biology, and computer science, but not yet in medical statistics. We develop the theory initially for arbitrary regression models for time-to-event data, and verify its predictions in detail for the popular Cox model.

Cross-validation pitfalls when selecting and assessing regression and classification models.

Science.gov (United States)

Krstajic, Damjan; Buturovic, Ljubomir J; Leahy, David E; Thomas, Simon

2014-03-29

We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe and evaluate best practices which improve reliability and increase confidence in selected models. A key operational component of the proposed methods is cloud computing which enables routine use of previously infeasible approaches. We describe in detail an algorithm for repeated grid-search V-fold cross-validation for parameter tuning in classification and regression, and we define a repeated nested cross-validation algorithm for model assessment. As regards variable selection and parameter tuning we define two algorithms (repeated grid-search cross-validation and double cross-validation), and provide arguments for using the repeated grid-search in the general case. We show results of our algorithms on seven QSAR datasets. The variation of the prediction performance, which is the result of choosing different splits of the dataset in V-fold cross-validation, needs to be taken into account when selecting and assessing classification and regression models. We demonstrate the importance of repeating cross-validation when selecting an optimal model, as well as the importance of repeating nested cross-validation when assessing a prediction error.

A LATENT CLASS POISSON REGRESSION-MODEL FOR HETEROGENEOUS COUNT DATA

NARCIS (Netherlands)

WEDEL, M; DESARBO, WS; BULT, [No Value; RAMASWAMY, [No Value

1993-01-01

In this paper an approach is developed that accommodates heterogeneity in Poisson regression models for count data. The model developed assumes that heterogeneity arises from a distribution of both the intercept and the coefficients of the explanatory variables. We assume that the mixing

Patients' preference for exercise setting and its influence on the health benefits gained from exercise-based cardiac rehabilitation.

Science.gov (United States)

Tang, Lars H; Kikkenborg Berg, Selina; Christensen, Jan; Lawaetz, Jannik; Doherty, Patrick; Taylor, Rod S; Langberg, Henning; Zwisler, Ann-Dorthe

2017-04-01

To assess patient preference for exercise setting and examine if choice of setting influences the long-term health benefit of exercise-based cardiac rehabilitation. Patients participating in a randomised controlled trial following either heart valve surgery, or radiofrequency ablation for atrial fibrillation were given the choice to perform a 12-week exercise programme in either a supervised centre-based, or a self-management home-based setting. Exercise capacity and physical and mental health outcomes were assessed for up to 24months after hospital discharge. Outcomes between settings were compared using a time×setting interaction using a mixed effects regression model. Across the 158 included patients, an equivalent proportion preferred to undertake exercise rehabilitation in a centre-based setting (55%, 95% CI: 45% to 63%) compared to a home-based setting (45%, 95% CI: 37% to 53%, p=0.233). At baseline, those who preferred a home-based setting reported better physical health (mean difference in physical component score: 5.0, 95% CI 2.3 to 7.4; p=0.001) and higher exercise capacity (mean between group difference 15.9watts, 95% CI 3.7 to 28.1; p=0.011). With the exception of the depression score in the Hospital Anxiety and Depression Score (F(3.65), p=0.004), there was no evidence of a significant difference in outcomes between settings. The preference of patients to participate in home-based and centre-based exercise programmes appears to be equivalent and provides similar health benefits. Whilst these findings support that patients should be given the choice between exercise-settings when initiating cardiac rehabilitation, further confirmatory evidence is needed. Copyright © 2017. Published by Elsevier B.V.

Continuous validation of ASTEC containment models and regression testing

International Nuclear Information System (INIS)

Nowack, Holger; Reinke, Nils; Sonnenkalb, Martin

2014-01-01

The focus of the ASTEC (Accident Source Term Evaluation Code) development at GRS is primarily on the containment module CPA (Containment Part of ASTEC), whose modelling is to a large extent based on the GRS containment code COCOSYS (COntainment COde SYStem). Validation is usually understood as the approval of the modelling capabilities by calculations of appropriate experiments done by external users different from the code developers. During the development process of ASTEC CPA, bugs and unintended side effects may occur, which leads to changes in the results of the initially conducted validation. Due to the involvement of a considerable number of developers in the coding of ASTEC modules, validation of the code alone, even if executed repeatedly, is not sufficient. Therefore, a regression testing procedure has been implemented in order to ensure that the initially obtained validation results are still valid with succeeding code versions. Within the regression testing procedure, calculations of experiments and plant sequences are performed with the same input deck but applying two different code versions. For every test-case the up-to-date code version is compared to the preceding one on the basis of physical parameters deemed to be characteristic for the test-case under consideration. In the case of post-calculations of experiments also a comparison to experimental data is carried out. Three validation cases from the regression testing procedure are presented within this paper. The very good post-calculation of the HDR E11.1 experiment shows the high quality modelling of thermal-hydraulics in ASTEC CPA. Aerosol behaviour is validated on the BMC VANAM M3 experiment, and the results show also a very good agreement with experimental data. Finally, iodine behaviour is checked in the validation test-case of the THAI IOD-11 experiment. Within this test-case, the comparison of the ASTEC versions V2.0r1 and V2.0r2 shows how an error was detected by the regression testing

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

Science.gov (United States)

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

2005-01-01

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

Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling.

Science.gov (United States)

Kawashima, Issaku; Kumano, Hiroaki

2017-01-01

Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling

Directory of Open Access Journals (Sweden)

Issaku Kawashima

2017-07-01

Full Text Available Mind-wandering (MW, task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

Expected for acquisition movement exercise is more effective for functional recovery than simple exercise in a rat model of hemiplegia.

Science.gov (United States)

Ikeda, Satoshi; Ohwatashi, Akihiko; Harada, Katsuhiro; Kamikawa, Yurie; Yoshida, Akira

2013-01-01

The use of novel rehabilitative approaches for effecting functional recovery following stroke is controversial. Effects of different but effective rehabilitative interventions in the hemiplegic patient are not clear. We studied the effects of different rehabilitative approaches on functional recovery in the rat photochecmical cerebral infarction model. Twenty-four male Wistar rats aged 8 weeks were used. The cranial bone was exposed under deep anesthesia. Rose bengal (20 mg/kg) was injected intravenously, and the sensorimotor area of the cerebral cortex was irradiated transcranially for 20 min with a light beam of 533-nm wavelength. Animals were divided into 3 groups. In the simple-exercise group, treadmill exercise was performed for 20 min every day. In the expected for acquisition movement-training group, beam-walking exercise was done for 20 min daily. The control group was left to recover without additional intervention. Hindlimb function was evaluated with the beam-walking test. Following cerebral infarction, dysfunction of the contralateral extremities was observed. Functional recovery was observed earlier in the expected for acquisition training group than in the other groups. Although rats in the treadmill group recovered more quickly than controls, the beam-walking group had the shortest overall recovery time. Exercise facilitated functional recovery in the rat hemiplegic model, and expected for acquisition exercise was more effective than simple exercise. These findings are considered to have important implications for the future development of clinical rehabilitation programs.

Reconstruction of missing daily streamflow data using dynamic regression models

Science.gov (United States)

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.

Pathological motivations for exercise and eating disorder specific health-related quality of life.

Science.gov (United States)

Cook, Brian; Engel, Scott; Crosby, Ross; Hausenblas, Heather; Wonderlich, Stephen; Mitchell, James

2014-04-01

To examine associations among pathological motivations for exercise with eating disorder (ED) specific health-related quality of life (HRQOL). Survey data assessing ED severity (i.e., Eating Disorder Diagnostic Survey), ED specific HRQOL (i.e., Eating Disorders Quality of Life Instrument), and pathological motivations for exercise (i.e., Exercise Dependence Scale) were collected from female students (N = 387) at seven universities throughout the United States. Regression analyses were conducted to examine the associations among exercise dependence, ED-specific HRQOL and ED severity, and the interaction of exercise dependence and ED severity on HRQOL scores. The overall model examining the impact of ED severity and exercise dependence (independent variables) on HRQOL (dependent variable) was significant and explained 16.1% of the variance in HRQOL scores. Additionally, the main effects for ED severity and exercise dependence and the interaction among ED severity and exercise dependence were significant, suggesting that the combined effects of ED severity and exercise dependence significantly impacts HRQOL. Our results suggest that pathological motivations for exercise may exacerbate ED's detrimental impact on HRQOL. Our results offer one possible insight into why exercise may be associated with deleterious effects on ED HRQOL. Future research is needed to elucidate the relationship among psychological aspects of exercise, ED, and HRQOL. Copyright © 2013 Wiley Periodicals, Inc.

Detection of Outliers in Regression Model for Medical Data

Directory of Open Access Journals (Sweden)

Stephen Raj S

2017-07-01

Full Text Available In regression analysis, an outlier is an observation for which the residual is large in magnitude compared to other observations in the data set. The detection of outliers and influential points is an important step of the regression analysis. Outlier detection methods have been used to detect and remove anomalous values from data. In this paper, we detect the presence of outliers in simple linear regression models for medical data set. Chatterjee and Hadi mentioned that the ordinary residuals are not appropriate for diagnostic purposes; a transformed version of them is preferable. First, we investigate the presence of outliers based on existing procedures of residuals and standardized residuals. Next, we have used the new approach of standardized scores for detecting outliers without the use of predicted values. The performance of the new approach was verified with the real-life data.

An Active Learning Exercise for Introducing Agent-Based Modeling

Science.gov (United States)

Pinder, Jonathan P.

2013-01-01

Recent developments in agent-based modeling as a method of systems analysis and optimization indicate that students in business analytics need an introduction to the terminology, concepts, and framework of agent-based modeling. This article presents an active learning exercise for MBA students in business analytics that demonstrates agent-based…

Computational Modeling Using OpenSim to Simulate a Squat Exercise Motion

Science.gov (United States)

Gallo, C. A.; Thompson, W. K.; Lewandowski, B. E.; Humphreys, B. T.; Funk, J. H.; Funk, N. H.; Weaver, A. S.; Perusek, G. P.; Sheehan, C. C.; Mulugeta, L.

2015-01-01

Long duration space travel to destinations such as Mars or an asteroid will expose astronauts to extended periods of reduced gravity. Astronauts will use an exercise regime for the duration of the space flight to minimize the loss of bone density, muscle mass and aerobic capacity that occurs during exposure to a reduced gravity environment. Since the area available in the spacecraft for an exercise device is limited and gravity is not present to aid loading, compact resistance exercise device prototypes are being developed. Since it is difficult to rigorously test these proposed devices in space flight, computational modeling provides an estimation of the muscle forces, joint torques and joint loads during exercise to gain insight on the efficacy to protect the musculoskeletal health of astronauts.

Estimasi Model Seemingly Unrelated Regression (SUR dengan Metode Generalized Least Square (GLS

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Ade Widyaningsih

2015-04-01

Full Text Available Regression analysis is a statistical tool that is used to determine the relationship between two or more quantitative variables so that one variable can be predicted from the other variables. A method that can used to obtain a good estimation in the regression analysis is ordinary least squares method. The least squares method is used to estimate the parameters of one or more regression but relationships among the errors in the response of other estimators are not allowed. One way to overcome this problem is Seemingly Unrelated Regression model (SUR in which parameters are estimated using Generalized Least Square (GLS. In this study, the author applies SUR model using GLS method on world gasoline demand data. The author obtains that SUR using GLS is better than OLS because SUR produce smaller errors than the OLS.

Estimasi Model Seemingly Unrelated Regression (SUR dengan Metode Generalized Least Square (GLS

Directory of Open Access Journals (Sweden)

Ade Widyaningsih

2014-06-01

Full Text Available Regression analysis is a statistical tool that is used to determine the relationship between two or more quantitative variables so that one variable can be predicted from the other variables. A method that can used to obtain a good estimation in the regression analysis is ordinary least squares method. The least squares method is used to estimate the parameters of one or more regression but relationships among the errors in the response of other estimators are not allowed. One way to overcome this problem is Seemingly Unrelated Regression model (SUR in which parameters are estimated using Generalized Least Square (GLS. In this study, the author applies SUR model using GLS method on world gasoline demand data. The author obtains that SUR using GLS is better than OLS because SUR produce smaller errors than the OLS.

On pseudo-values for regression analysis in competing risks models

DEFF Research Database (Denmark)

Graw, F; Gerds, Thomas Alexander; Schumacher, M

2009-01-01

For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15-27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their...

CONTRIBUTION OF HAMSTRING FATIGUE TO QUADRICEPS INHIBITION FOLLOWING LUMBAR EXTENSION EXERCISE

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Joseph M. Hart

2006-03-01

Full Text Available The purpose of this study was to determine the contribution of hamstrings and quadriceps fatigue to quadriceps inhibition following lumbar extension exercise. Regression models were calculated consisting of the outcome variable: quadriceps inhibition and predictor variables: change in EMG median frequency in the quadriceps and hamstrings during lumbar fatiguing exercise. Twenty-five subjects with a history of low back pain were matched by gender, height and mass to 25 healthy controls. Subjects performed two sets of fatiguing isometric lumbar extension exercise until mild (set 1 and moderate (set 2 fatigue of the lumbar paraspinals. Quadriceps and hamstring EMG median frequency were measured while subjects performed fatiguing exercise. A burst of electrical stimuli was superimposed while subjects performed an isometric maximal quadriceps contraction to estimate quadriceps inhibition after each exercise set. Results indicate the change in hamstring median frequency explained variance in quadriceps inhibition following the exercise sets in the history of low back pain group only. Change in quadriceps median frequency explained variance in quadriceps inhibition following the first exercise set in the control group only. In conclusion, persons with a history of low back pain whose quadriceps become inhibited following lumbar paraspinal exercise may be adapting to the fatigue by using their hamstring muscles more than controls

Modeling and prediction of Turkey's electricity consumption using Support Vector Regression

International Nuclear Information System (INIS)

Kavaklioglu, Kadir

2011-01-01

Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, ε-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values; and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best ε-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption. (author)

Cluster regression model and level fluctuation features of Van Lake, Turkey

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Z. Şen

1999-02-01

Full Text Available Lake water levels change under the influences of natural and/or anthropogenic environmental conditions. Among these influences are the climate change, greenhouse effects and ozone layer depletions which are reflected in the hydrological cycle features over the lake drainage basins. Lake levels are among the most significant hydrological variables that are influenced by different atmospheric and environmental conditions. Consequently, lake level time series in many parts of the world include nonstationarity components such as shifts in the mean value, apparent or hidden periodicities. On the other hand, many lake level modeling techniques have a stationarity assumption. The main purpose of this work is to develop a cluster regression model for dealing with nonstationarity especially in the form of shifting means. The basis of this model is the combination of transition probability and classical regression technique. Both parts of the model are applied to monthly level fluctuations of Lake Van in eastern Turkey. It is observed that the cluster regression procedure does preserve the statistical properties and the transitional probabilities that are indistinguishable from the original data.Key words. Hydrology (hydrologic budget; stochastic processes · Meteorology and atmospheric dynamics (ocean-atmosphere interactions

Cluster regression model and level fluctuation features of Van Lake, Turkey

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Z. Şen

Full Text Available Lake water levels change under the influences of natural and/or anthropogenic environmental conditions. Among these influences are the climate change, greenhouse effects and ozone layer depletions which are reflected in the hydrological cycle features over the lake drainage basins. Lake levels are among the most significant hydrological variables that are influenced by different atmospheric and environmental conditions. Consequently, lake level time series in many parts of the world include nonstationarity components such as shifts in the mean value, apparent or hidden periodicities. On the other hand, many lake level modeling techniques have a stationarity assumption. The main purpose of this work is to develop a cluster regression model for dealing with nonstationarity especially in the form of shifting means. The basis of this model is the combination of transition probability and classical regression technique. Both parts of the model are applied to monthly level fluctuations of Lake Van in eastern Turkey. It is observed that the cluster regression procedure does preserve the statistical properties and the transitional probabilities that are indistinguishable from the original data.

Key words. Hydrology (hydrologic budget; stochastic processes · Meteorology and atmospheric dynamics (ocean-atmosphere interactions

Improved model of the retardance in citric acid coated ferrofluids using stepwise regression

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Lin, J. F.; Qiu, X. R.

2017-06-01

Citric acid (CA) coated Fe3O4 ferrofluids (FFs) have been conducted for biomedical application. The magneto-optical retardance of CA coated FFs was measured by a Stokes polarimeter. Optimization and multiple regression of retardance in FFs were executed by Taguchi method and Microsoft Excel previously, and the F value of regression model was large enough. However, the model executed by Excel was not systematic. Instead we adopted the stepwise regression to model the retardance of CA coated FFs. From the results of stepwise regression by MATLAB, the developed model had highly predictable ability owing to F of 2.55897e+7 and correlation coefficient of one. The average absolute error of predicted retardances to measured retardances was just 0.0044%. Using the genetic algorithm (GA) in MATLAB, the optimized parametric combination was determined as [4.709 0.12 39.998 70.006] corresponding to the pH of suspension, molar ratio of CA to Fe3O4, CA volume, and coating temperature. The maximum retardance was found as 31.712°, close to that obtained by evolutionary solver in Excel and a relative error of -0.013%. Above all, the stepwise regression method was successfully used to model the retardance of CA coated FFs, and the maximum global retardance was determined by the use of GA.

EFFECTS OF EXERCISE AND CAFFEIC ACID PHENETHYL ESTER AFTER CHRONIC EXERCISE RAT MODEL

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Ayse Alp

2011-01-01

Full Text Available In order to understand whether exercise and caffeic acid phenethyl ester (CAPE has an effect on obesity and weight control, we investigated the effects of CAPE, and exercise on lipid parameters (triglyceride, total cholesterol, HDL-C, LDL-C, and adipokine substances such as leptin and resistin in rats. 40 male rat were randomly assigned into 4 groups. It was determined that CAPE does not have any significant effect on these parameters but that lipid parameters and leptin values in exercise groups decreased considerably, while no significant change occurred in resistin levels. In order to understand whether diet has an effect on exercise, body weights of all animal groups in pre and post-exercise were compared. A significant weight gain was observed (p = 0.005 in all groups. This study concluded that exercise has a considerable effect on leptin and lipid parameters; however, exercise alone was not sufficient for weight control and could be effective in weight control only when accompanied by a restricted diet.

Exercise during pregnancy and its association with gestational weight gain.

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Harris, Shericka T; Liu, Jihong; Wilcox, Sara; Moran, Robert; Gallagher, Alexa

2015-03-01

We examined the association between exercise during pregnancy and meeting gestational weight gain recommendations. Data came from the 2009 South Carolina Pregnancy Risk Assessment Monitoring System (n = 856). Women reported their participation in exercise/sports activities before and during pregnancy, including the number of months and types of exercise. We developed an exercise index (EI), the product of the number of months spent in exercise and average metabolic equivalents for specific exercise. The 2009 Institute of Medicine's guideline was used to categorize gestational weight gain into three classes: inadequate, adequate, and excessive. Multinomial logistic regression models were used to adjust for confounders. Over 46 % of women exceeded the recommended weight gain during pregnancy. Nearly one third (31.9 %) of women reported exercising ≥3 times a week at any time during pregnancy. Compared to women who did not report this level of exercise during pregnancy, exercising women were more likely to meet gestational weight gain recommendations (32.7 vs. 18.7 %) and had a lower odds of excessive gestational weight gain [adjusted odds ratio (AOR) 0.43, 95 % confidence interval 0.24-0.78]. Women with an EI above the median value of those women who exercised or women who exercised ≥3 times a week for 6-9 months during pregnancy had lower odds of excessive gestational weight gain (AOR for EI 0.20, 0.08-0.49; AOR for months 0.26, 0.12-0.56, respectively). Our findings support the need to promote or increase exercise during pregnancy to reduce the high proportion of women who are gaining excessive weight.

Accounting for spatial effects in land use regression for urban air pollution modeling.

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Bertazzon, Stefania; Johnson, Markey; Eccles, Kristin; Kaplan, Gilaad G

2015-01-01

In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Regression analysis understanding and building business and economic models using Excel

CERN Document Server

Wilson, J Holton

2012-01-01

The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book will teach you the essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The authors take a non-theoretical treatment that is accessible even if you have a limited statistical background. It is specifically designed to teach the correct use of regression, while advising you of its limitations and teaching about common pitfalls. This book describe

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

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

2011-12-01

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

A primer for biomedical scientists on how to execute model II linear regression analysis.

Science.gov (United States)

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Exercise Decreases and Smoking Increases Bladder Cancer Mortality.

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Liss, Michael A; White, Martha; Natarajan, Loki; Parsons, J Kellogg

2017-06-01

The aim of this study was to investigate modifiable lifestyle factors of smoking, exercise, and obesity with bladder cancer mortality. We used mortality-linked data from the National Health Information Survey from 1998 through 2006. The primary outcome was bladder cancer-specific mortality. The primary exposures were self-reported smoking status (never- vs. former vs. current smoker), self-reported exercise (dichotomized as "did no exercise" vs. "light, moderate, or vigorous exercise in ≥ 10-minute bouts"), and body mass index. We utilized multivariable adjusted Cox proportional hazards regression models, with delayed entry to account for age at survey interview. Complete data were available on 222,163 participants, of whom 96,715 (44%) were men and 146,014 (66%) were non-Hispanic whites, and among whom we identified 83 bladder cancer-specific deaths. In multivariate analyses, individuals who reported any exercise were 47% less likely (adjusted hazard ratio [HR adj ], 0.53; 95% confidence interval [CI], 0.29-0.96; P = .038) to die of bladder cancer than "no exercise". Compared with never-smokers, current (HR adj , 4.24; 95% CI, 1.89-9.65; P = .001) and former (HR adj , 2.95; 95% CI, 1.50-5.79; P = .002) smokers were 4 and 3 times more likely, respectively, to die of bladder cancer. There were no significant associations of body mass index with bladder cancer mortality. Exercise decreases and current smoking increases the risk of bladder cancer-specific mortality. These data suggest that exercise and smoking cessation interventions may reduce bladder cancer death. Published by Elsevier Inc.

Applied logistic regression

CERN Document Server

Hosmer, David W; Sturdivant, Rodney X

2013-01-01

 A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-

Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS

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Soyoung Park

2017-07-01

Full Text Available This study mapped and analyzed groundwater potential using two different models, logistic regression (LR and multivariate adaptive regression splines (MARS, and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m3/d were extracted, and 859 locations (70% were used for model training, whereas the other 365 locations (30% were used for model validation. We analyzed 16 groundwater influence factors including altitude, slope degree, slope aspect, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport index, distance from drainage, drainage density, lithology, distance from fault, fault density, distance from lineament, lineament density, and land cover. Groundwater potential maps (GPMs were constructed using LR and MARS models and tested using a receiver operating characteristics curve. Based on this analysis, the area under the curve (AUC for the success rate curve of GPMs created using the MARS and LR models was 0.867 and 0.838, and the AUC for the prediction rate curve was 0.836 and 0.801, respectively. This implies that the MARS model is useful and effective for groundwater potential analysis in the study area.

Sample size calculation to externally validate scoring systems based on logistic regression models.

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Antonio Palazón-Bru

Full Text Available A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence. Scoring systems based on binary logistic regression models are a specific type of predictive model.The aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study.The algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units.In the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature.An algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases.

It Takes One to Know One: A Class Exercise in Mental Models

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Wilson, Theresa

2014-01-01

An active learning class exercise is presented that gives students the personal experience of the decision-making limitations of mental models. This innovative exercise was shown to increase student learning through greater understanding of the concept and higher retention of knowledge. The results suggest that student critical thinking skills…

Detection of Cutting Tool Wear using Statistical Analysis and Regression Model

Science.gov (United States)

Ghani, Jaharah A.; Rizal, Muhammad; Nuawi, Mohd Zaki; Haron, Che Hassan Che; Ramli, Rizauddin

2010-10-01

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter technique, called I-kaz was used for developing a regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I-kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I-kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time tool wear monitoring.

Using the Logistic Regression model in supporting decisions of establishing marketing strategies

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Cristinel CONSTANTIN

2015-12-01

Full Text Available This paper is about an instrumental research regarding the using of Logistic Regression model for data analysis in marketing research. The decision makers inside different organisation need relevant information to support their decisions regarding the marketing strategies. The data provided by marketing research could be computed in various ways but the multivariate data analysis models can enhance the utility of the information. Among these models we can find the Logistic Regression model, which is used for dichotomous variables. Our research is based on explanation the utility of this model and interpretation of the resulted information in order to help practitioners and researchers to use it in their future investigations

Vector regression introduced

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Mok Tik

2014-06-01

Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.

Feelings of energy, exercise-related self-efficacy, and voluntary exercise participation.

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Yoon, Seok; Buckworth, Janet; Focht, Brian; Ko, Bomna

2013-12-01

This study used a path analysis approach to examine the relationship between feelings of energy, exercise-related self-efficacy beliefs, and exercise participation. A cross-sectional mailing survey design was used to measure feelings of physical and mental energy, task and scheduling self-efficacy beliefs, and voluntary moderate and vigorous exercise participation in 368 healthy, full-time undergraduate students (mean age = 21.43 ± 2.32 years). The path analysis revealed that the hypothesized path model had a strong fit to the study data. The path model showed that feelings of physical energy had significant direct effects on task and scheduling self-efficacy beliefs as well as exercise behaviors. In addition, scheduling self-efficacy had direct effects on moderate and vigorous exercise participation. However, there was no significant direct relationship between task self-efficacy and exercise participation. The path model also revealed that scheduling self-efficacy partially mediated the relationship between feelings of physical energy and exercise participation.

Multiple regression models for energy use in air-conditioned office buildings in different climates

International Nuclear Information System (INIS)

Lam, Joseph C.; Wan, Kevin K.W.; Liu Dalong; Tsang, C.L.

2010-01-01

An attempt was made to develop multiple regression models for office buildings in the five major climates in China - severe cold, cold, hot summer and cold winter, mild, and hot summer and warm winter. A total of 12 key building design variables were identified through parametric and sensitivity analysis, and considered as inputs in the regression models. The coefficient of determination R 2 varies from 0.89 in Harbin to 0.97 in Kunming, indicating that 89-97% of the variations in annual building energy use can be explained by the changes in the 12 parameters. A pseudo-random number generator based on three simple multiplicative congruential generators was employed to generate random designs for evaluation of the regression models. The difference between regression-predicted and DOE-simulated annual building energy use are largely within 10%. It is envisaged that the regression models developed can be used to estimate the likely energy savings/penalty during the initial design stage when different building schemes and design concepts are being considered.

CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model

DEFF Research Database (Denmark)

Dyrholm, Mads; Hansen, Lars Kai

2004-01-01

We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....

Verification, Validation and Credibility Assessment of a Computational Model of the Advanced Resistive Exercise Device (ARED)

Science.gov (United States)

Werner, C. R.; Humphreys, B. T.; Mulugeta, L.

2014-01-01

The Advanced Resistive Exercise Device (ARED) is the resistive exercise device used by astronauts on the International Space Station (ISS) to mitigate bone loss and muscle atrophy due to extended exposure to microgravity (micro g). The Digital Astronaut Project (DAP) has developed a multi-body dynamics model of biomechanics models for use in spaceflight exercise physiology research and operations. In an effort to advance model maturity and credibility of the ARED model, the DAP performed verification, validation and credibility (VV and C) assessment of the analyses of the model in accordance to NASA-STD-7009 'Standards for Models and Simulations'.

Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects

Science.gov (United States)

Suhartono, Lee, Muhammad Hisyam; Prastyo, Dedy Dwi

2015-12-01

The aim of this research is to develop a calendar variation model for forecasting retail sales data with the Eid ul-Fitr effect. The proposed model is based on two methods, namely two levels ARIMAX and regression methods. Two levels ARIMAX and regression models are built by using ARIMAX for the first level and regression for the second level. Monthly men's jeans and women's trousers sales in a retail company for the period January 2002 to September 2009 are used as case study. In general, two levels of calendar variation model yields two models, namely the first model to reconstruct the sales pattern that already occurred, and the second model to forecast the effect of increasing sales due to Eid ul-Fitr that affected sales at the same and the previous months. The results show that the proposed two level calendar variation model based on ARIMAX and regression methods yields better forecast compared to the seasonal ARIMA model and Neural Networks.

Computer analysis of the exercise electrocardiogram and control of radionuclide ventriculography: an optimal statistical decision model for diagnosis of coronary artery disease

International Nuclear Information System (INIS)

Hsia, P.W.E.

1987-01-01

A new automated technique for the diagnosis of coronary artery disease (CAD) by stress electrocardiography and radionuclide ventriculography (RNV) has been developed. The method employs digital signal processing of the electrocardiogram (ECG) for recognition of ischemic and arrhythmic events. On-line detection of abnormal beats is used for control image acquisition by the gamma camera resulting in improved image quality since only identical beats are included in the composite images of the cardiac cycle. A combined stress ECG analysis which measures ST changes indicative of ischemia, and radionuclide results, which reveal corresponding ejection fraction abnormalities yields greater sensitivity and specificity than either test alone. Digitized data from the electrocardiogram are analyzed in a beat-by-beat mode and a contextual diagnosis of underlying rhythm is given. Template generation, R wave detection, QRS window size, baseline correction, and continuous updating of heart rate are completely automated. A statistical model base on computerized ST segment measurements combined with radionuclide ventriculography data has been developed by using a logistic model with stepwise regression fitting. A previously acquired database of similar measurements was used for designing the model. The most significant parameters were found to be (1) ejection fraction of exercise RNV; (2) difference of ST level between exercise and resting test (stdif); and (3) ejection fraction difference between exercise and resting test. The new parameter stdif, not previously used in ECG interpretation, was found to be of great diagnostic significance

Testing and Modeling Fuel Regression Rate in a Miniature Hybrid Burner

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Luciano Fanton

2012-01-01

Full Text Available Ballistic characterization of an extended group of innovative HTPB-based solid fuel formulations for hybrid rocket propulsion was performed in a lab-scale burner. An optical time-resolved technique was used to assess the quasisteady regression history of single perforation, cylindrical samples. The effects of metalized additives and radiant heat transfer on the regression rate of such formulations were assessed. Under the investigated operating conditions and based on phenomenological models from the literature, analyses of the collected experimental data show an appreciable influence of the radiant heat flux from burnt gases and soot for both unloaded and loaded fuel formulations. Pure HTPB regression rate data are satisfactorily reproduced, while the impressive initial regression rates of metalized formulations require further assessment.

An acute exercise session increases self-efficacy in sedentary endometrial cancer survivors and controls.

Science.gov (United States)

Hughes, Daniel; Baum, George; Jovanovic, Jennifer; Carmack, Cindy; Greisinger, Anthony; Basen-Engquist, Karen

2010-11-01

Self-efficacy can be affected by mastery experiences and somatic sensations. A novel exercise experience and associated sensations may impact self-efficacy and subsequent behaviors. We investigated the effect of a single exercise session on self-efficacy for sedentary endometrial cancer survivors compared with sedentary women of a similar age, but with no cancer history. Twenty survivors and 19 controls completed an exercise session performed as a submaximal cycle ergometry test. Sensations and efficacy were measured before and after exercise. Repeated measures analysis of variance (ANOVA) was performed. Regression models were used to determine predictors of self-efficacy and subsequent exercise. Self-efficacy increased for both survivors and controls, but survivors had a higher rate of increase, and the change predicted subsequent exercise. The association between exercise-related somatic sensations and self-efficacy differed between the 2 groups. A novel exercise experience had a larger effect on self-efficacy and subsequent exercise activity for endometrial cancer survivors than controls. Somatic sensations experienced during exercise may differ for survivors, which may be related to the experience of having cancer. Understanding factors affecting confidence in novel exercise experiences for populations with specific cancer histories is of the utmost importance in the adoption of exercise behaviors.

Two-step variable selection in quantile regression models

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FAN Yali

2015-06-01

Full Text Available We propose a two-step variable selection procedure for high dimensional quantile regressions, in which the dimension of the covariates, pn is much larger than the sample size n. In the first step, we perform ℓ1 penalty, and we demonstrate that the first step penalized estimator with the LASSO penalty can reduce the model from an ultra-high dimensional to a model whose size has the same order as that of the true model, and the selected model can cover the true model. The second step excludes the remained irrelevant covariates by applying the adaptive LASSO penalty to the reduced model obtained from the first step. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. We conduct a simulation study and a real data analysis to evaluate the finite sample performance of the proposed approach.

Regression Models for Predicting Force Coefficients of Aerofoils

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Mohammed ABDUL AKBAR

2015-09-01

Full Text Available Renewable sources of energy are attractive and advantageous in a lot of different ways. Among the renewable energy sources, wind energy is the fastest growing type. Among wind energy converters, Vertical axis wind turbines (VAWTs have received renewed interest in the past decade due to some of the advantages they possess over their horizontal axis counterparts. VAWTs have evolved into complex 3-D shapes. A key component in predicting the output of VAWTs through analytical studies is obtaining the values of lift and drag coefficients which is a function of shape of the aerofoil, ‘angle of attack’ of wind and Reynolds’s number of flow. Sandia National Laboratories have carried out extensive experiments on aerofoils for the Reynolds number in the range of those experienced by VAWTs. The volume of experimental data thus obtained is huge. The current paper discusses three Regression analysis models developed wherein lift and drag coefficients can be found out using simple formula without having to deal with the bulk of the data. Drag coefficients and Lift coefficients were being successfully estimated by regression models with R2 values as high as 0.98.

The Relationship between Economic Growth and Money Laundering – a Linear Regression Model

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Daniel Rece

2009-09-01

Full Text Available This study provides an overview of the relationship between economic growth and money laundering modeled by a least squares function. The report analyzes statistically data collected from USA, Russia, Romania and other eleven European countries, rendering a linear regression model. The study illustrates that 23.7% of the total variance in the regressand (level of money laundering is “explained” by the linear regression model. In our opinion, this model will provide critical auxiliary judgment and decision support for anti-money laundering service systems.

Regression analysis of informative current status data with the additive hazards model.

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Zhao, Shishun; Hu, Tao; Ma, Ling; Wang, Peijie; Sun, Jianguo

2015-04-01

This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided.

Poisson regression approach for modeling fatal injury rates amongst Malaysian workers

International Nuclear Information System (INIS)

Kamarulzaman Ibrahim; Heng Khai Theng

2005-01-01

Many safety studies are based on the analysis carried out on injury surveillance data. The injury surveillance data gathered for the analysis include information on number of employees at risk of injury in each of several strata where the strata are defined in terms of a series of important predictor variables. Further insight into the relationship between fatal injury rates and predictor variables may be obtained by the poisson regression approach. Poisson regression is widely used in analyzing count data. In this study, poisson regression is used to model the relationship between fatal injury rates and predictor variables which are year (1995-2002), gender, recording system and industry type. Data for the analysis were obtained from PERKESO and Jabatan Perangkaan Malaysia. It is found that the assumption that the data follow poisson distribution has been violated. After correction for the problem of over dispersion, the predictor variables that are found to be significant in the model are gender, system of recording, industry type, two interaction effects (interaction between recording system and industry type and between year and industry type). Introduction Regression analysis is one of the most popular

Accounting for Zero Inflation of Mussel Parasite Counts Using Discrete Regression Models

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Emel Çankaya

2017-06-01

Full Text Available In many ecological applications, the absences of species are inevitable due to either detection faults in samples or uninhabitable conditions for their existence, resulting in high number of zero counts or abundance. Usual practice for modelling such data is regression modelling of log(abundance+1 and it is well know that resulting model is inadequate for prediction purposes. New discrete models accounting for zero abundances, namely zero-inflated regression (ZIP and ZINB, Hurdle-Poisson (HP and Hurdle-Negative Binomial (HNB amongst others are widely preferred to the classical regression models. Due to the fact that mussels are one of the economically most important aquatic products of Turkey, the purpose of this study is therefore to examine the performances of these four models in determination of the significant biotic and abiotic factors on the occurrences of Nematopsis legeri parasite harming the existence of Mediterranean mussels (Mytilus galloprovincialis L.. The data collected from the three coastal regions of Sinop city in Turkey showed more than 50% of parasite counts on the average are zero-valued and model comparisons were based on information criterion. The results showed that the probability of the occurrence of this parasite is here best formulated by ZINB or HNB models and influential factors of models were found to be correspondent with ecological differences of the regions.

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

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Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Transpiration of glasshouse rose crops: evaluation of regression models

NARCIS (Netherlands)

Baas, R.; Rijssel, van E.

2006-01-01

Regression models of transpiration (T) based on global radiation inside the greenhouse (G), with or without energy input from heating pipes (Eh) and/or vapor pressure deficit (VPD) were parameterized. Therefore, data on T, G, temperatures from air, canopy and heating pipes, and VPD from both a

Patients' mental models and adherence to outpatient physical therapy home exercise programs.

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Rizzo, Jon

2015-05-01

Within physical therapy, patient adherence usually relates to attending appointments, following advice, and/or undertaking prescribed exercise. Similar to findings for general medical adherence, patient adherence to physical therapy home exercise programs (HEP) is estimated between 35 and 72%. Adherence to HEPs is a multifactorial and poorly understood phenomenon, with no consensus regarding a common theoretical framework that best guides empirical or clinical efforts. Mental models, a construct used to explain behavior and decision-making in the social sciences, may serve as this framework. Mental models comprise an individual's tacit thoughts about how the world works. They include assumptions about new experiences and expectations for the future based on implicit comparisons between current and past experiences. Mental models play an important role in decision-making and guiding actions. This professional theoretical article discusses empirical research demonstrating relationships among mental models, prior experience, and adherence decisions in medical and physical therapy contexts. Specific issues related to mental models and physical therapy patient adherence are discussed, including the importance of articulation of patients' mental models, assessment of patients' mental models that relate to exercise program adherence, discrepancy between patient and provider mental models, and revision of patients' mental models in ways that enhance adherence. The article concludes with practical implications for physical therapists and recommendations for further research to better understand the role of mental models in physical therapy patient adherence behavior.

Exercise motivation: a cross-sectional analysis examining its relationships with frequency, intensity, and duration of exercise

Directory of Open Access Journals (Sweden)

Wilson Philip M

2010-01-01

Full Text Available Abstract Background It is important to engage in regular physical activity in order to maintain a healthy lifestyle however a large portion of the population is insufficiently active. Understanding how different types of motivation contribute to exercise behavior is an important first step in identifying ways to increase exercise among individuals. The current study employs self-determination theory as a framework from which to examine how motivation contributes to various characteristics of exercise behavior. Methods Regular exercisers (N = 1079; n = 468 males; n = 612 females completed inventories which assessed the frequency, intensity, and duration with which they exercise, as well as the Behavioral Regulation in Exercise Questionnaire including four additional items assessing integrated regulation. Results Bivariate correlations revealed that all three behavioral indices (frequency, intensity, and duration of exercise were more highly correlated with more autonomous than controlling regulations. Regression analyses revealed that integrated and identified regulations predicted exercise frequency for males and females. Integrated regulation was found to be the only predictor of exercise duration across both genders. Finally, introjected regulation predicted exercise intensity for females only. Conclusions These findings suggest that exercise regulations that vary in their degree of internalization can differentially predict characteristics of exercise behavior. Furthermore, in the motivational profile of a regular exerciser, integrated regulation appears to be an important determinant of exercise behavior. These results highlight the importance of assessing integrated regulation in exercise settings where the goal of understanding motivated behavior has important health implications.

A Simple Exercise Reveals the Way Students Think about Scientific Modeling

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Ruebush, Laura; Sulikowski, Michelle; North, Simon

2009-01-01

Scientific modeling is an integral part of contemporary science, yet many students have little understanding of how models are developed, validated, and used to predict and explain phenomena. A simple modeling exercise led to significant gains in understanding key attributes of scientific modeling while revealing some stubborn misconceptions.…

Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks.

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Duda, Piotr; Jaworski, Maciej; Rutkowski, Leszek

2018-03-01

One of the greatest challenges in data mining is related to processing and analysis of massive data streams. Contrary to traditional static data mining problems, data streams require that each element is processed only once, the amount of allocated memory is constant and the models incorporate changes of investigated streams. A vast majority of available methods have been developed for data stream classification and only a few of them attempted to solve regression problems, using various heuristic approaches. In this paper, we develop mathematically justified regression models working in a time-varying environment. More specifically, we study incremental versions of generalized regression neural networks, called IGRNNs, and we prove their tracking properties - weak (in probability) and strong (with probability one) convergence assuming various concept drift scenarios. First, we present the IGRNNs, based on the Parzen kernels, for modeling stationary systems under nonstationary noise. Next, we extend our approach to modeling time-varying systems under nonstationary noise. We present several types of concept drifts to be handled by our approach in such a way that weak and strong convergence holds under certain conditions. Finally, in the series of simulations, we compare our method with commonly used heuristic approaches, based on forgetting mechanism or sliding windows, to deal with concept drift. Finally, we apply our concept in a real life scenario solving the problem of currency exchange rates prediction.

Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample

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Ivanka Jerić

2011-11-01

Full Text Available Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample.

A simulation study on Bayesian Ridge regression models for several collinearity levels

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Efendi, Achmad; Effrihan

2017-12-01

When analyzing data with multiple regression model if there are collinearities, then one or several predictor variables are usually omitted from the model. However, there sometimes some reasons, for instance medical or economic reasons, the predictors are all important and should be included in the model. Ridge regression model is not uncommon in some researches to use to cope with collinearity. Through this modeling, weights for predictor variables are used for estimating parameters. The next estimation process could follow the concept of likelihood. Furthermore, for the estimation nowadays the Bayesian version could be an alternative. This estimation method does not match likelihood one in terms of popularity due to some difficulties; computation and so forth. Nevertheless, with the growing improvement of computational methodology recently, this caveat should not at the moment become a problem. This paper discusses about simulation process for evaluating the characteristic of Bayesian Ridge regression parameter estimates. There are several simulation settings based on variety of collinearity levels and sample sizes. The results show that Bayesian method gives better performance for relatively small sample sizes, and for other settings the method does perform relatively similar to the likelihood method.

Predictors of exercise frequency in breast cancer survivors in Taiwan.

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Hsu, Hsin-Tien; Dodd, Marylin J; Guo, Su-Er; Lee, Kathryn A; Hwang, Shiow-Li; Lai, Yu-Hung

2011-07-01

To apply social cognitive theory to elucidate factors that motivate change in exercise frequency in breast cancer survivors during the six months after completing cancer treatment. Exercise is now a well-recognised quality-of-life intervention in breast cancer survivors. However, only regular exercise yields long-term benefits. Motivations for exercise have not been analysed in Taiwan patients with cancer. A prospective, longitudinal and repeated measures design was used. A convenience sample of 196 breast cancer survivors was recruited from hospitals in metropolitan areas of north and south Taiwan. Study participants were allowed to select their preferred exercised activities. Exercise behaviour and other factors were then recorded using various standardised instruments. Medical charts were also reviewed. Data were analysed by a linear mixed model and by hierarchical multiple regression equations. Exercise frequency significantly changed over time. Explained variance in exercise frequency change was modest. Baseline exercise frequency was the best significant predictor of exercise frequency during the six-month study. The study also identified possible age-related differences in the effect of social support on exercise. The effect of social support for exercise on exercise frequency was apparently larger in older subjects, especially those over 40 years old, than in younger subjects. Mental health, exercise barriers and exercise outcome expectancy significantly contributed to change in exercise frequency during the six-month study. The analytical results revealed several ways to increase exercise frequency in breast cancer survivors: (1) encourage exercise as early as possible; (2) improve health status and provide social support for exercise, especially in women aged 40 years or older; (3) reduce exercise barriers and promote mental health; (4) reinforce self-efficacy and positive expectations of exercise outcomes and (5) provide strategies for minimising fatigue

Photovoltaic Array Condition Monitoring Based on Online Regression of Performance Model

DEFF Research Database (Denmark)

Spataru, Sergiu; Sera, Dezso; Kerekes, Tamas

2013-01-01

regression modeling, from PV array production, plane-of-array irradiance, and module temperature measurements, acquired during an initial learning phase of the system. After the model has been parameterized automatically, the condition monitoring system enters the normal operation phase, where...

Vigorous exercise is associated with superior metabolic profiles in polycystic ovary syndrome independent of total exercise expenditure.

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Greenwood, Eleni A; Noel, Martha W; Kao, Chia-Ning; Shinkai, Kanade; Pasch, Lauri A; Cedars, Marcelle I; Huddleston, Heather G

2016-02-01

To characterize metabolic features of women with polycystic ovary syndrome (PCOS) by exercise behavior and determine relative health benefits of different exercise intensities. Cross-sectional study. Tertiary academic institution. Three hundred and twenty-six women aged 14-52 years-old with PCOS by Rotterdam criteria examined between 2006 and 2013. International Physical Activity Questionnaire (IPAQ) administered to classify patients into three groups based on Department of Health and Human Services (DHHS) Guidelines of vigorous, moderate, and inactive, along with physical examination and serum testing. Blood pressure, body mass index (BMI), waist circumference, fasting lipids, fasting glucose and insulin, 2-hour 75-gram oral glucose tolerance, homeostatic model assessment of insulin resistance (HOMA-IR). The DHHS guidelines for adequate physical activity were met by 182 (56%) women. Compared with moderate exercisers and inactive women, the vigorous exercisers had lower BMI and lower HOMA-IR; higher levels of high-density lipoprotein cholesterol and sex hormone-binding globulin; and a reduced prevalence of the metabolic syndrome. In a multivariate logistic regression analysis controlling for age, BMI, and total energy expenditure, every hour of vigorous exercise reduced a patient's odds of metabolic syndrome by 22% (odds ratio 0.78; 95% confidence interval, 0.62, 0.99). Women with PCOS who met DHHS guidelines for exercise demonstrated superior metabolic health parameters. Vigorous but not moderate activity is associated with reduced odds of the metabolic syndrome, independent of age, BMI, and total energy expenditure. PCOS patients should be encouraged to meet activity guidelines via vigorous physical activity. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Extended cox regression model: The choice of timefunction

Science.gov (United States)

Isik, Hatice; Tutkun, Nihal Ata; Karasoy, Durdu

2017-07-01

Cox regression model (CRM), which takes into account the effect of censored observations, is one the most applicative and usedmodels in survival analysis to evaluate the effects of covariates. Proportional hazard (PH), requires a constant hazard ratio over time, is the assumptionofCRM. Using extended CRM provides the test of including a time dependent covariate to assess the PH assumption or an alternative model in case of nonproportional hazards. In this study, the different types of real data sets are used to choose the time function and the differences between time functions are analyzed and discussed.

Determinants of exercise among children. II. A longitudinal analysis.

Science.gov (United States)

DiLorenzo, T M; Stucky-Ropp, R C; Vander Wal, J S; Gotham, H J

1998-01-01

Research has demonstrated that physical activity serves an important preventive function against the development of cardiovascular disease. The recognition that U.S. children are often sedentary, coupled with the observation that physical activity habits tend to persist into adulthood, has prompted the investigation of exercise determinants consistent with social learning theory. The purposes of the present study were to identify social learning variables relevant to children's exercise and to explore the longitudinal predictive value of the determinants. Data were collected from 111 families (N = 54 girls, N = 57 boys) who were interviewed in both Phase 1 (fifth and sixth grades) and Phase 2 (eight and ninth grades) of this study. Data from mothers (N = 111) were collected during both phases; data from 80 fathers were collected at Phase 2 only. The results of simultaneous stepwise regression analyses indicated that child's enjoyment of physical activity was the only consistent predictor of physical activity during Phase 1. At Phase 2, child's exercise knowledge, mother's physical activity, and child's and mother's friend modeling/support emerged as predictors for girls. For boys, child's self-efficacy for physical activity, exercise knowledge, parental modeling, and interest in sports media were important. Longitudinally, mother's self-efficacy, barriers to exercise, enjoyment of physical activity, and child's self-efficacy for physical activity were important for girls. Only child's exercise knowledge predicted boys' physical activity. The addition of information from fathers nearly doubled the explanatory power of the predictors for both genders. Socialization in the family unit exerts a tremendous influence on health-related behaviors such as exercise. The relative importance of determinants seems to differ for girls and boys and the pattern of these determinants appears to change over time.

INVESTIGATION OF E-MAIL TRAFFIC BY USING ZERO-INFLATED REGRESSION MODELS

Directory of Open Access Journals (Sweden)

Yılmaz KAYA

2012-06-01

Full Text Available Based on count data obtained with a value of zero may be greater than anticipated. These types of data sets should be used to analyze by regression methods taking into account zero values. Zero- Inflated Poisson (ZIP, Zero-Inflated negative binomial (ZINB, Poisson Hurdle (PH, negative binomial Hurdle (NBH are more common approaches in modeling more zero value possessing dependent variables than expected. In the present study, the e-mail traffic of Yüzüncü Yıl University in 2009 spring semester was investigated. ZIP and ZINB, PH and NBH regression methods were applied on the data set because more zeros counting (78.9% were found in data set than expected. ZINB and NBH regression considered zero dispersion and overdispersion were found to be more accurate results due to overdispersion and zero dispersion in sending e-mail. ZINB is determined to be best model accordingto Vuong statistics and information criteria.

Focused information criterion and model averaging based on weighted composite quantile regression

KAUST Repository

Xu, Ganggang; Wang, Suojin; Huang, Jianhua Z.

2013-01-01

We study the focused information criterion and frequentist model averaging and their application to post-model-selection inference for weighted composite quantile regression (WCQR) in the context of the additive partial linear models. With the non

Predicting older adults' maintenance in exercise participation using an integrated social psychological model

NARCIS (Netherlands)

Stiggelbout, M.; Hopman-Rock, M.; Crone, M.; Lechner, L.; Mechelen, W. van

2006-01-01

Little is known about the predictors of maintenance in organized exercise programmes. The aim of this study was to investigate the behavioral predictors of maintenance of exercise participation in older adults, using an integrated social psychological model. To this end, we carried out a prospective

Model-based bootstrapping when correcting for measurement error with application to logistic regression.

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Buonaccorsi, John P; Romeo, Giovanni; Thoresen, Magne

2018-03-01

When fitting regression models, measurement error in any of the predictors typically leads to biased coefficients and incorrect inferences. A plethora of methods have been proposed to correct for this. Obtaining standard errors and confidence intervals using the corrected estimators can be challenging and, in addition, there is concern about remaining bias in the corrected estimators. The bootstrap, which is one option to address these problems, has received limited attention in this context. It has usually been employed by simply resampling observations, which, while suitable in some situations, is not always formally justified. In addition, the simple bootstrap does not allow for estimating bias in non-linear models, including logistic regression. Model-based bootstrapping, which can potentially estimate bias in addition to being robust to the original sampling or whether the measurement error variance is constant or not, has received limited attention. However, it faces challenges that are not present in handling regression models with no measurement error. This article develops new methods for model-based bootstrapping when correcting for measurement error in logistic regression with replicate measures. The methodology is illustrated using two examples, and a series of simulations are carried out to assess and compare the simple and model-based bootstrap methods, as well as other standard methods. While not always perfect, the model-based approaches offer some distinct improvements over the other methods. © 2017, The International Biometric Society.

Ordinal regression models to describe tourist satisfaction with Sintra's world heritage

Science.gov (United States)

Mouriño, Helena

2013-10-01

In Tourism Research, ordinal regression models are becoming a very powerful tool in modelling the relationship between an ordinal response variable and a set of explanatory variables. In August and September 2010, we conducted a pioneering Tourist Survey in Sintra, Portugal. The data were obtained by face-to-face interviews at the entrances of the Palaces and Parks of Sintra. The work developed in this paper focus on two main points: tourists' perception of the entrance fees; overall level of satisfaction with this heritage site. For attaining these goals, ordinal regression models were developed. We concluded that tourist's nationality was the only significant variable to describe the perception of the admission fees. Also, Sintra's image among tourists depends not only on their nationality, but also on previous knowledge about Sintra's World Heritage status.

Combination of supervised and semi-supervised regression models for improved unbiased estimation

DEFF Research Database (Denmark)

Arenas-Garía, Jeronimo; Moriana-Varo, Carlos; Larsen, Jan

2010-01-01

In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised and semisupervi......In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised...

Relationships among adolescents' weight perceptions, exercise goals, exercise motivation, quality of life and leisure-time exercise behaviour: a self-determination theory approach.

Science.gov (United States)

Gillison, F B; Standage, M; Skevington, S M

2006-12-01

Exercise has an important role to play in the prevention of child and adolescent obesity. Recent school-based interventions have struggled to achieve meaningful and lasting changes to exercise levels. Theorists have suggested that this may, in part, be due to the failure to incorporate psychosocial mediators as they relate to behaviour change. Using a sample of 580 British schoolchildren, a model grounded in self-determination theory was explored to examine the effects of exercise goals on exercise motivation, leisure-time exercise behaviour and quality of life (QoL). Results of structural equation modelling revealed that adolescents perceiving themselves to be overweight and pressurized to lose weight, endorsed extrinsic weight-related goals for exercise. Extrinsic goals negatively predicted, whereas intrinsic goals positively predicted, self-determined motivation, which in turn positively predicted QoL and exercise behaviour. Furthermore, self-determined motivation partially mediated the effects of exercise goals on reported exercise behaviour and QoL. Multi-sample invariance testing revealed the proposed model to be largely invariant across gender. Results suggest that holding extrinsic exercise goals could compromise exercise participation levels and QoL. A role for teachers and parents is proposed with the aim of orienting young people towards intrinsic goals in an attempt to enhance future exercise behaviour and QoL.

Random regression models for daily feed intake in Danish Duroc pigs

DEFF Research Database (Denmark)

Strathe, Anders Bjerring; Mark, Thomas; Jensen, Just

The objective of this study was to develop random regression models and estimate covariance functions for daily feed intake (DFI) in Danish Duroc pigs. A total of 476201 DFI records were available on 6542 Duroc boars between 70 to 160 days of age. The data originated from the National test station......-year-season, permanent, and animal genetic effects. The functional form was based on Legendre polynomials. A total of 64 models for random regressions were initially ranked by BIC to identify the approximate order for the Legendre polynomials using AI-REML. The parsimonious model included Legendre polynomials of 2nd...... order for genetic and permanent environmental curves and a heterogeneous residual variance, allowing the daily residual variance to change along the age trajectory due to scale effects. The parameters of the model were estimated in a Bayesian framework, using the RJMC module of the DMU package, where...

Longitudinal beta regression models for analyzing health-related quality of life scores over time

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Hunger Matthias

2012-09-01

Full Text Available Abstract Background Health-related quality of life (HRQL has become an increasingly important outcome parameter in clinical trials and epidemiological research. HRQL scores are typically bounded at both ends of the scale and often highly skewed. Several regression techniques have been proposed to model such data in cross-sectional studies, however, methods applicable in longitudinal research are less well researched. This study examined the use of beta regression models for analyzing longitudinal HRQL data using two empirical examples with distributional features typically encountered in practice. Methods We used SF-6D utility data from a German older age cohort study and stroke-specific HRQL data from a randomized controlled trial. We described the conceptual differences between mixed and marginal beta regression models and compared both models to the commonly used linear mixed model in terms of overall fit and predictive accuracy. Results At any measurement time, the beta distribution fitted the SF-6D utility data and stroke-specific HRQL data better than the normal distribution. The mixed beta model showed better likelihood-based fit statistics than the linear mixed model and respected the boundedness of the outcome variable. However, it tended to underestimate the true mean at the upper part of the distribution. Adjusted group means from marginal beta model and linear mixed model were nearly identical but differences could be observed with respect to standard errors. Conclusions Understanding the conceptual differences between mixed and marginal beta regression models is important for their proper use in the analysis of longitudinal HRQL data. Beta regression fits the typical distribution of HRQL data better than linear mixed models, however, if focus is on estimating group mean scores rather than making individual predictions, the two methods might not differ substantially.

A Gompertz regression model for fern spores germination

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Gabriel y Galán, Jose María

2015-06-01

Full Text Available Germination is one of the most important biological processes for both seed and spore plants, also for fungi. At present, mathematical models of germination have been developed in fungi, bryophytes and several plant species. However, ferns are the only group whose germination has never been modelled. In this work we develop a regression model of the germination of fern spores. We have found that for Blechnum serrulatum, Blechnum yungense, Cheilanthes pilosa, Niphidium macbridei and Polypodium feuillei species the Gompertz growth model describe satisfactorily cumulative germination. An important result is that regression parameters are independent of fern species and the model is not affected by intraspecific variation. Our results show that the Gompertz curve represents a general germination model for all the non-green spore leptosporangiate ferns, including in the paper a discussion about the physiological and ecological meaning of the model.La germinación es uno de los procesos biológicos más relevantes tanto para las plantas con esporas, como para las plantas con semillas y los hongos. Hasta el momento, se han desarrollado modelos de germinación para hongos, briofitos y diversas especies de espermatófitos. Los helechos son el único grupo de plantas cuya germinación nunca ha sido modelizada. En este trabajo se desarrolla un modelo de regresión para explicar la germinación de las esporas de helechos. Observamos que para las especies Blechnum serrulatum, Blechnum yungense, Cheilanthes pilosa, Niphidium macbridei y Polypodium feuillei el modelo de crecimiento de Gompertz describe satisfactoriamente la germinación acumulativa. Un importante resultado es que los parámetros de la regresión son independientes de la especie y que el modelo no está afectado por variación intraespecífica. Por lo tanto, los resultados del trabajo muestran que la curva de Gompertz puede representar un modelo general para todos los helechos leptosporangiados

Generic global regression models for growth prediction of Salmonella in ground pork and pork cuts

DEFF Research Database (Denmark)

Buschhardt, Tasja; Hansen, Tina Beck; Bahl, Martin Iain

2017-01-01

Introduction and Objectives Models for the prediction of bacterial growth in fresh pork are primarily developed using two-step regression (i.e. primary models followed by secondary models). These models are also generally based on experiments in liquids or ground meat and neglect surface growth....... It has been shown that one-step global regressions can result in more accurate models and that bacterial growth on intact surfaces can substantially differ from growth in liquid culture. Material and Methods We used a global-regression approach to develop predictive models for the growth of Salmonella....... One part of obtained logtransformed cell counts was used for model development and another for model validation. The Ratkowsky square root model and the relative lag time (RLT) model were integrated into the logistic model with delay. Fitted parameter estimates were compared to investigate the effect...

Application of multilinear regression analysis in modeling of soil ...

African Journals Online (AJOL)

The application of Multi-Linear Regression Analysis (MLRA) model for predicting soil properties in Calabar South offers a technical guide and solution in foundation designs problems in the area. Forty-five soil samples were collected from fifteen different boreholes at a different depth and 270 tests were carried out for CBR, ...

EMD-regression for modelling multi-scale relationships, and application to weather-related cardiovascular mortality

Science.gov (United States)

Masselot, Pierre; Chebana, Fateh; Bélanger, Diane; St-Hilaire, André; Abdous, Belkacem; Gosselin, Pierre; Ouarda, Taha B. M. J.

2018-01-01

In a number of environmental studies, relationships between natural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the resulting regression models and therefore to results with moderate reliability. To deal with this issue, the present paper introduces the EMD-regression methodology consisting in applying the empirical mode decomposition (EMD) algorithm on data series and then using the resulting components in regression models. The proposed methodology presents a number of advantages. First, it accounts of the issues of non-stationarity associated to the data series. Second, this approach acts as a scan for the relationship between a response variable and the predictors at different time scales, providing new insights about this relationship. To illustrate the proposed methodology it is applied to study the relationship between weather and cardiovascular mortality in Montreal, Canada. The results shed new knowledge concerning the studied relationship. For instance, they show that the humidity can cause excess mortality at the monthly time scale, which is a scale not visible in classical models. A comparison is also conducted with state of the art methods which are the generalized additive models and distributed lag models, both widely used in weather-related health studies. The comparison shows that EMD-regression achieves better prediction performances and provides more details than classical models concerning the relationship.

The Application of Classical and Neural Regression Models for the Valuation of Residential Real Estate

Directory of Open Access Journals (Sweden)

Mach Łukasz

2017-06-01

Full Text Available The research process aimed at building regression models, which helps to valuate residential real estate, is presented in the following article. Two widely used computational tools i.e. the classical multiple regression and regression models of artificial neural networks were used in order to build models. An attempt to define the utilitarian usefulness of the above-mentioned tools and comparative analysis of them is the aim of the conducted research. Data used for conducting analyses refers to the secondary transactional residential real estate market.

A review of a priori regression models for warfarin maintenance dose prediction.

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Ben Francis

Full Text Available A number of a priori warfarin dosing algorithms, derived using linear regression methods, have been proposed. Although these dosing algorithms may have been validated using patients derived from the same centre, rarely have they been validated using a patient cohort recruited from another centre. In order to undertake external validation, two cohorts were utilised. One cohort formed by patients from a prospective trial and the second formed by patients in the control arm of the EU-PACT trial. Of these, 641 patients were identified as having attained stable dosing and formed the dataset used for validation. Predicted maintenance doses from six criterion fulfilling regression models were then compared to individual patient stable warfarin dose. Predictive ability was assessed with reference to several statistics including the R-square and mean absolute error. The six regression models explained different amounts of variability in the stable maintenance warfarin dose requirements of the patients in the two validation cohorts; adjusted R-squared values ranged from 24.2% to 68.6%. An overview of the summary statistics demonstrated that no one dosing algorithm could be considered optimal. The larger validation cohort from the prospective trial produced more consistent statistics across the six dosing algorithms. The study found that all the regression models performed worse in the validation cohort when compared to the derivation cohort. Further, there was little difference between regression models that contained pharmacogenetic coefficients and algorithms containing just non-pharmacogenetic coefficients. The inconsistency of results between the validation cohorts suggests that unaccounted population specific factors cause variability in dosing algorithm performance. Better methods for dosing that take into account inter- and intra-individual variability, at the initiation and maintenance phases of warfarin treatment, are needed.

A review of a priori regression models for warfarin maintenance dose prediction.

Science.gov (United States)

Francis, Ben; Lane, Steven; Pirmohamed, Munir; Jorgensen, Andrea

2014-01-01

A number of a priori warfarin dosing algorithms, derived using linear regression methods, have been proposed. Although these dosing algorithms may have been validated using patients derived from the same centre, rarely have they been validated using a patient cohort recruited from another centre. In order to undertake external validation, two cohorts were utilised. One cohort formed by patients from a prospective trial and the second formed by patients in the control arm of the EU-PACT trial. Of these, 641 patients were identified as having attained stable dosing and formed the dataset used for validation. Predicted maintenance doses from six criterion fulfilling regression models were then compared to individual patient stable warfarin dose. Predictive ability was assessed with reference to several statistics including the R-square and mean absolute error. The six regression models explained different amounts of variability in the stable maintenance warfarin dose requirements of the patients in the two validation cohorts; adjusted R-squared values ranged from 24.2% to 68.6%. An overview of the summary statistics demonstrated that no one dosing algorithm could be considered optimal. The larger validation cohort from the prospective trial produced more consistent statistics across the six dosing algorithms. The study found that all the regression models performed worse in the validation cohort when compared to the derivation cohort. Further, there was little difference between regression models that contained pharmacogenetic coefficients and algorithms containing just non-pharmacogenetic coefficients. The inconsistency of results between the validation cohorts suggests that unaccounted population specific factors cause variability in dosing algorithm performance. Better methods for dosing that take into account inter- and intra-individual variability, at the initiation and maintenance phases of warfarin treatment, are needed.

Modeling of chemical exergy of agricultural biomass using improved general regression neural network

International Nuclear Information System (INIS)

Huang, Y.W.; Chen, M.Q.; Li, Y.; Guo, J.

2016-01-01

A comprehensive evaluation for energy potential contained in agricultural biomass was a vital step for energy utilization of agricultural biomass. The chemical exergy of typical agricultural biomass was evaluated based on the second law of thermodynamics. The chemical exergy was significantly influenced by C and O elements rather than H element. The standard entropy of the samples also was examined based on their element compositions. Two predicted models of the chemical exergy were developed, which referred to a general regression neural network model based upon the element composition, and a linear model based upon the high heat value. An auto-refinement algorithm was firstly developed to improve the performance of regression neural network model. The developed general regression neural network model with K-fold cross-validation had a better ability for predicting the chemical exergy than the linear model, which had lower predicted errors (±1.5%). - Highlights: • Chemical exergies of agricultural biomass were evaluated based upon fifty samples. • Values for the standard entropy of agricultural biomass samples were calculated. • A linear relationship between chemical exergy and HHV of samples was detected. • An improved GRNN prediction model for the chemical exergy of biomass was developed.

Ameliorated Austenite Carbon Content Control in Austempered Ductile Irons by Support Vector Regression

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Chan-Yun Yang

2013-01-01

Full Text Available Austempered ductile iron has emerged as a notable material in several engineering fields, including marine applications. The initial austenite carbon content after austenization transform but before austempering process for generating bainite matrix proved critical in controlling the resulted microstructure and thus mechanical properties. In this paper, support vector regression is employed in order to establish a relationship between the initial carbon concentration in the austenite with austenization temperature and alloy contents, thereby exercising improved control in the mechanical properties of the austempered ductile irons. Particularly, the paper emphasizes a methodology tailored to deal with a limited amount of available data with intrinsically contracted and skewed distribution. The collected information from a variety of data sources presents another challenge of highly uncertain variance. The authors present a hybrid model consisting of a procedure of a histogram equalizer and a procedure of a support-vector-machine (SVM- based regression to gain a more robust relationship to respond to the challenges. The results show greatly improved accuracy of the proposed model in comparison to two former established methodologies. The sum squared error of the present model is less than one fifth of that of the two previous models.

Gender and developmental differences in exercise beliefs among youth and prediction of their exercise behavior.

Science.gov (United States)

Garcia, A W; Broda, M A; Frenn, M; Coviak, C; Pender, N J; Ronis, D L

1995-08-01

This study examined gender and developmental differences in exercise-related beliefs and exercise behaviors of 286 racially diverse youth and explored factors predictive of exercise. Compared to males, females reported less prior and current exercise, lower self-esteem, poorer health status, and lower exercise self-schema. Adolescents, in contrast to pre-adolescents, reported less social support for exercise and fewer exercise role models. In a path model, gender, the benefits/barriers differential, and access to exercise facilities and programs directly predicted exercise. Effects of grade, perceived health status, exercise self-efficacy, social support for exercise, and social norms for exercise on exercise behavior, were mediated through the benefits/barriers differential. Effect of race on exercise was mediated by access to exercise facilities and programs. Continued exploration of gender and developmental differences in variables influencing physical activity can yield valuable information for tailoring exercise promotion interventions to the unique needs of youth.

Spreadsheet Decision Support Model for Training Exercise Material Requirements Planning

National Research Council Canada - National Science Library

Tringali, Arthur

1997-01-01

... associated with military training exercises. The model combines the business practice of Material Requirements Planning and the commercial spreadsheet software capabilities of Lotus 1-2-3 to calculate the requirements for food, consumable...

Exercise ameliorates neurocognitive impairments in a translational model of pediatric radiotherapy.

Science.gov (United States)

Sahnoune, Iman; Inoue, Taeko; Kesler, Shelli R; Rodgers, Shaefali P; Sabek, Omaima M; Pedersen, Steen E; Zawaski, Janice A; Nelson, Katharine H; Ris, M Douglas; Leasure, J Leigh; Gaber, M Waleed

2018-04-09

While cranial radiation therapy (CRT) is an effective treatment, healthy areas surrounding irradiation sites are negatively affected. Frontal lobe functions involving attention, processing speed, and inhibition control are impaired. These deficits appear months to years after CRT and impair quality of life. Exercise has been shown to rejuvenate the brain and aid in recovery post-injury through its effects on neurogenesis and cognition. We developed a juvenile rodent CRT model that reproduces neurocognitive deficits. Next, we utilized the model to test whether exercise ameliorates these deficits. Fischer rats (31 days old) were irradiated with a fractionated dose of 4 Gy × 5 days, trained and tested at 6, 9, and 12 months post-CRT using 5-choice serial reaction time task. After testing, fixed rat brains were imaged using diffusion tensor imaging and immunohistochemistry. CRT caused early and lasting impairments in task acquisition, accuracy, and latency to correct response, as well as causing stunting of growth and changes in brain volume and diffusion. Exercising after irradiation improved acquisition, behavioral control, and processing speed, mitigated the stunting of brain size, and increased brain fiber numbers compared with sedentary CRT values. Further, exercise partially restored global connectome organization, including assortativity and characteristic path length, and while it did not improve the specific regional connections that were lowered by CRT, it appeared to remodel these connections by increasing connectivity between alternate regional pairs. Our data strongly suggest that exercise may be useful in combination with interventions aimed at improving cognitive outcome following pediatric CRT.

New robust statistical procedures for the polytomous logistic regression models.

Science.gov (United States)

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

Energy Technology Data Exchange (ETDEWEB)

Pappas, S.S. [Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, 83 200 Samos (Greece); Ekonomou, L.; Chatzarakis, G.E. [Department of Electrical Engineering Educators, ASPETE - School of Pedagogical and Technological Education, N. Heraklion, 141 21 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24100 Kalamata (Greece); Katsikas, S.K. [Department of Technology Education and Digital Systems, University of Piraeus, 150 Androutsou Srt., 18 532 Piraeus (Greece); Liatsis, P. [Division of Electrical Electronic and Information Engineering, School of Engineering and Mathematical Sciences, Information and Biomedical Engineering Centre, City University, Northampton Square, London EC1V 0HB (United Kingdom)

2008-09-15

This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts. (author)

The characterization of obese polycystic ovary syndrome rat model suitable for exercise intervention.

Directory of Open Access Journals (Sweden)

Chuyan Wu

Full Text Available To develop a new polycystic ovary syndrome (PCOS rat model suitable for exercise intervention.Thirty six rats were randomly divided into three experimental groups: PCOS rats with high-fat diet (PF, n = 24, PCOS rats with ordinary diet (PO, n = 6, and control rats with ordinary diet (CO, n = 6. Two kinds of PCOS rat model were made by adjustment diet structure and testosterone injection for 28 days. After a successful animal model, PF model rats were randomly assigned to three groups: exercise with a continuation of high-fat diet (PF-EF, n = 6, sedentary with a continuation of high-fat diet (PF-SF, n = 6, exercise with an ordinary diet (PF-EO, n = 6. Fasting blood glucose (FBG and insulin (FINS, estrogen (E2, progesterone (P, and testosterone (T in serum were determined by RIA, and ovarian morphology was evaluated by Image-Pro plus 6.0.Body weight, Lee index, FINS increased significantly in PF rat model. Serum levels of E2 and T were significantly higher in PF and PO than in CO. Ovary organ index and ovarian areas were significant lower in PF than in CO. After intervention for 2 weeks, the levels of 1 h postprandial blood glucose (PBG1, 2 h postprandial blood glucose (PBG2, FINS and the serum levels of T decreased significantly in PF-EF rats and PF-EO rats. The ratio of FBG/FINS was significant higher in PF-EO rats than in PF-SF rats. Ovarian morphology showed that the numbers of preantral follicles and atretic follicles decreased significantly, and the numbers of antral follicles and corpora lutea increased significantly in the rats of PF-EF and PF-EO.By combination of high-fat diet and testosterone injection, the obese PCOS rat model is conformable with the lifestyle habits of fatty foods and insufficient exercise, and has metabolic and reproductive characteristics of human PCOS. This model can be applied to study exercise intervention.

Analysis of the influence of quantile regression model on mainland tourists' service satisfaction performance.

Science.gov (United States)

Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen

2014-01-01

It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.

Analysis of the Influence of Quantile Regression Model on Mainland Tourists' Service Satisfaction Performance

Science.gov (United States)

Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen

2014-01-01

It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models. PMID:24574916

Analysis of the Influence of Quantile Regression Model on Mainland Tourists’ Service Satisfaction Performance

Directory of Open Access Journals (Sweden)

Wen-Cheng Wang

2014-01-01

Full Text Available It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.

A binary logistic regression model with complex sampling design of ...

African Journals Online (AJOL)

2017-09-03

Sep 3, 2017 ... Bi-variable and multi-variable binary logistic regression model with complex sampling design was fitted. .... Data was entered into STATA-12 and analyzed using. SPSS-21. .... lack of access/too far or costs too much. 35. 1.2.

truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models

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Maria Karlsson

2014-05-01

Full Text Available Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and ?nite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.

Exercise training on cardiovascular diseases: Role of animal models in the elucidation of the mechanisms

Directory of Open Access Journals (Sweden)

Bruno Rodrigues

2017-05-01

Full Text Available Abstract Cardiovascular diseases, which include hypertension, coronary artery disease/myocardial infarction and heart failure, are one of the major causes of disability and death worldwide. On the other hand, physical exercise acts in the preventionand treatment of these conditions. In fact, several experiments performed in human beings have demonstrated the efficiency of physical exercise to alter clinical signals observed in these diseases, such as high blood pressure and exercise intolerance. However, even if human studies demonstrated the clinical efficiency of physical exercise, most extensive mechanisms responsible for this phenomenon still have to be elucidated. In this sense, studies using animal models seem to be a good option to demonstrate such mechanisms. Therefore, the aims of the present study are describing the main pathophysiological characteristics of the animal models used in the study of cardiovascular diseases, as well as the main mechanismsassociated with the benefits of physical exercise.

Regression estimators for generic health-related quality of life and quality-adjusted life years.

Science.gov (United States)

Basu, Anirban; Manca, Andrea

2012-01-01

To develop regression models for outcomes with truncated supports, such as health-related quality of life (HRQoL) data, and account for features typical of such data such as a skewed distribution, spikes at 1 or 0, and heteroskedasticity. Regression estimators based on features of the Beta distribution. First, both a single equation and a 2-part model are presented, along with estimation algorithms based on maximum-likelihood, quasi-likelihood, and Bayesian Markov-chain Monte Carlo methods. A novel Bayesian quasi-likelihood estimator is proposed. Second, a simulation exercise is presented to assess the performance of the proposed estimators against ordinary least squares (OLS) regression for a variety of HRQoL distributions that are encountered in practice. Finally, the performance of the proposed estimators is assessed by using them to quantify the treatment effect on QALYs in the EVALUATE hysterectomy trial. Overall model fit is studied using several goodness-of-fit tests such as Pearson's correlation test, link and reset tests, and a modified Hosmer-Lemeshow test. The simulation results indicate that the proposed methods are more robust in estimating covariate effects than OLS, especially when the effects are large or the HRQoL distribution has a large spike at 1. Quasi-likelihood techniques are more robust than maximum likelihood estimators. When applied to the EVALUATE trial, all but the maximum likelihood estimators produce unbiased estimates of the treatment effect. One and 2-part Beta regression models provide flexible approaches to regress the outcomes with truncated supports, such as HRQoL, on covariates, after accounting for many idiosyncratic features of the outcomes distribution. This work will provide applied researchers with a practical set of tools to model outcomes in cost-effectiveness analysis.

Testing for constant nonparametric effects in general semiparametric regression models with interactions

KAUST Repository

Wei, Jiawei; Carroll, Raymond J.; Maity, Arnab

2011-01-01

We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work

Factors that influence exercise activity among women post hip fracture participating in the Exercise Plus Program.

Science.gov (United States)

Resnick, Barbara; Orwig, Denise; D'Adamo, Christopher; Yu-Yahiro, Janet; Hawkes, William; Shardell, Michelle; Golden, Justine; Zimmerman, Sheryl; Magaziner, Jay

2007-01-01

Using a social ecological model, this paper describes selected intra- and interpersonal factors that influence exercise behavior in women post hip fracture who participated in the Exercise Plus Program. Model testing of factors that influence exercise behavior at 2, 6 and 12 months post hip fracture was done. The full model hypothesized that demographic variables; cognitive, affective, physical and functional status; pain; fear of falling; social support for exercise, and exposure to the Exercise Plus Program would influence self-efficacy, outcome expectations, and stage of change both directly and indirectly influencing total time spent exercising. Two hundred and nine female hip fracture patients (age 81.0 +/- 6.9), the majority of whom were Caucasian (97%), participated in this study. The three predictive models tested across the 12 month recovery trajectory suggest that somewhat different factors may influence exercise over the recovery period and the models explained 8 to 21% of the variance in time spent exercising. To optimize exercise activity post hip fracture, older adults should be helped to realistically assess their self-efficacy and outcome expectations related to exercise, health care providers and friends/peers should be encouraged to reinforce the positive benefits of exercise post hip fracture, and fear of falling should be addressed throughout the entire hip fracture recovery trajectory.

Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.

Science.gov (United States)

Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris

2016-09-01

Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have

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

Science.gov (United States)

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

2015-04-01

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

Theoretical Constructs that Predict Women's Exercise

OpenAIRE

Whiteley, Jessica A.

1998-01-01

Although research has examined the determinants of physical activity, this research has focused primarily on men and few efforts have been made to explain the interrelationships between commonly used predictors of physical activity. Descriptive data and regression analyses were conducted with 193 female students, faculty, staff and community members of a southwestern Virginia university town. Variables that were entered into the regression included age, body mass index, exercise knowledge, se...

Better Autologistic Regression

Directory of Open Access Journals (Sweden)

Mark A. Wolters

2017-11-01

Full Text Available Autologistic regression is an important probability model for dichotomous random variables observed along with covariate information. It has been used in various fields for analyzing binary data possessing spatial or network structure. The model can be viewed as an extension of the autologistic model (also known as the Ising model, quadratic exponential binary distribution, or Boltzmann machine to include covariates. It can also be viewed as an extension of logistic regression to handle responses that are not independent. Not all authors use exactly the same form of the autologistic regression model. Variations of the model differ in two respects. First, the variable coding—the two numbers used to represent the two possible states of the variables—might differ. Common coding choices are (zero, one and (minus one, plus one. Second, the model might appear in either of two algebraic forms: a standard form, or a recently proposed centered form. Little attention has been paid to the effect of these differences, and the literature shows ambiguity about their importance. It is shown here that changes to either coding or centering in fact produce distinct, non-nested probability models. Theoretical results, numerical studies, and analysis of an ecological data set all show that the differences among the models can be large and practically significant. Understanding the nature of the differences and making appropriate modeling choices can lead to significantly improved autologistic regression analyses. The results strongly suggest that the standard model with plus/minus coding, which we call the symmetric autologistic model, is the most natural choice among the autologistic variants.

A twin-sibling study on the relationship between exercise attitudes and exercise behavior.

Science.gov (United States)

Huppertz, Charlotte; Bartels, Meike; Jansen, Iris E; Boomsma, Dorret I; Willemsen, Gonneke; de Moor, Marleen H M; de Geus, Eco J C

2014-01-01

Social cognitive models of health behavior propose that individual differences in leisure time exercise behavior are influenced by the attitudes towards exercise. At the same time, large scale twin-family studies show a significant influence of genetic factors on regular exercise behavior. This twin-sibling study aimed to unite these findings by demonstrating that exercise attitudes can be heritable themselves. Secondly, the genetic and environmental cross-trait correlations and the monozygotic (MZ) twin intrapair differences model were used to test whether the association between exercise attitudes and exercise behavior can be causal. Survey data were obtained from 5,095 twins and siblings (18-50 years). A genetic contribution was found for exercise behavior (50 % in males, 43 % in females) and for the six exercise attitude components derived from principal component analysis: perceived benefits (21, 27 %), lack of skills, support and/or resources (45, 48 %), time constraints (25, 30 %), lack of energy (34, 44 %), lack of enjoyment (47, 44 %), and embarrassment (42, 49 %). These components were predictive of leisure time exercise behavior (R(2) = 28 %). Bivariate modeling further showed that all the genetic (0.36 exercise attitudes and exercise behavior were significantly different from zero, which is a necessary condition for the existence of a causal effect driving the association. The correlations between the MZ twins' difference scores were in line with this finding. It is concluded that exercise attitudes and exercise behavior are heritable, that attitudes and behavior are partly correlated through pleiotropic genetic effects, but that the data are compatible with a causal association between exercise attitudes and behavior.

Can the measurement of brachial artery flow-mediated dilation be applied to the acute exercise model?

Directory of Open Access Journals (Sweden)

Harris Ryan A

2007-11-01

Full Text Available Abstract The measurement of flow-mediated dilation using high-resolution ultrasound has been utilized extensively in interventional trials evaluating the salutary effect of drugs and lifestyle modifications (i.e. diet or exercise training on endothelial function; however, until recently researchers have not used flow-mediated dilation to examine the role of a single bout of exercise on vascular function. Utilizing the acute exercise model can be advantageous as it allows for an efficient manipulation of exercise variables (i.e. mode, intensity, duration, etc. and permits greater experimental control of confounding variables. Given that the application of flow-mediated dilation in the acute exercise paradigm is expanding, the purpose of this review is to discuss methodological and physiological factors pertinent to flow-mediated dilation in the context of acute exercise. Although the scientific rationale for evaluating endothelial function in response to acute exercise is sound, few concerns warrant attention when interpreting flow-mediated dilation data following acute exercise. The following questions will be addressed in the present review: Does the measurement of flow-mediated dilation influence subsequent serial measures of flow-mediated dilation? Do we need to account for diurnal variation? Is there an optimal time to measure post-exercise flow-mediated dilation? Is the post-exercise flow-mediated dilation reproducible? How is flow-mediated dilation interpreted considering the hemodynamic and sympathetic changes associated with acute exercise? Can the measurement of endothelial-independent dilation affect the exercise? Evidence exists to support the methodological appropriateness for employing flow-mediated dilation in the acute exercise model; however, further research is warranted to clarify its interpretation following acute exercise.

Alternative Methods of Regression

CERN Document Server

Birkes, David

2011-01-01

Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s

Time-adaptive quantile regression

DEFF Research Database (Denmark)

Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik

2008-01-01

and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....

Exercise Effects for Children With Autism Spectrum Disorder: Metabolic Health, Autistic Traits, and Quality of Life.

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Toscano, Chrystiane V A; Carvalho, Humberto M; Ferreira, José P

2018-02-01

This study examined the effects of a 48-week exercise-based intervention on the metabolic profile, autism traits, and perceived quality of life in children with autism spectrum disorder (ASD). We randomly allocated 64 children with ASD (aged 6-12 years) to experimental ( n = 46) and control groups ( n = 18) and used multilevel regression modeling to examine responses to receiving or not receiving the intervention. The experimental group showed beneficial effects on metabolic indicators (high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and total cholesterol), autism traits, and parent-perceived quality of life. Our results provide support for exercise and physical activity, including basic coordination and strength exercises, as important therapeutic interventions for children with ASD.

Construction of risk prediction model of type 2 diabetes mellitus based on logistic regression

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Li Jian

2017-01-01

Full Text Available Objective: to construct multi factor prediction model for the individual risk of T2DM, and to explore new ideas for early warning, prevention and personalized health services for T2DM. Methods: using logistic regression techniques to screen the risk factors for T2DM and construct the risk prediction model of T2DM. Results: Male’s risk prediction model logistic regression equation: logit(P=BMI × 0.735+ vegetables × (−0.671 + age × 0.838+ diastolic pressure × 0.296+ physical activity× (−2.287 + sleep ×(−0.009 +smoking ×0.214; Female’s risk prediction model logistic regression equation: logit(P=BMI ×1.979+ vegetables× (−0.292 + age × 1.355+ diastolic pressure× 0.522+ physical activity × (−2.287 + sleep × (−0.010.The area under the ROC curve of male was 0.83, the sensitivity was 0.72, the specificity was 0.86, the area under the ROC curve of female was 0.84, the sensitivity was 0.75, the specificity was 0.90. Conclusion: This study model data is from a compared study of nested case, the risk prediction model has been established by using the more mature logistic regression techniques, and the model is higher predictive sensitivity, specificity and stability.

THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE

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

2015-01-01

Background: The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran?s universities. Methods: This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran?s public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For pr...

Exercising alone versus with others and associations with subjective health status in older Japanese: The JAGES Cohort Study.

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Kanamori, Satoru; Takamiya, Tomoko; Inoue, Shigeru; Kai, Yuko; Kawachi, Ichiro; Kondo, Katsunori

2016-12-15

Although exercising with others may have extra health benefits compared to exercising alone, few studies have examined the differences. We sought to examine whether the association of regular exercise to subjective health status differs according to whether people exercise alone and/or with others, adjusting for frequency of exercise. The study was based on the Japan Gerontological Evaluation Study (JAGES) Cohort Study data. Participants were 21,684 subjects aged 65 or older. Multivariable logistic regression models were used to examine the association. The adjusted odds ratios (ORs) for poor self-rated health were significantly lower for people who exercised compared to non-exercisers. In analyses restricted to regular exercisers the ORs for poor health were 0.69 (95% confidence intervals: 0.60-0.79) for individuals exercising alone more often than with others, 0.74 (0.64-0.84) for people who were equally likely to exercise alone as with others, 0.57 (0.43-0.75) for individuals exercising with others more frequently than alone, and 0.79 (0.64-0.97) for individuals only exercising with others compared to individuals only exercising alone. Although exercising alone and exercising with others both seem to have health benefits, increased frequency of exercise with others has important health benefits regardless of the total frequency of exercise.

Harmonic regression of Landsat time series for modeling attributes from national forest inventory data

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Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E.

2018-03-01

Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009-2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10-20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher.

An appraisal of convergence failures in the application of logistic regression model in published manuscripts.

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Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A

2014-09-01

Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.

Modeling of the Monthly Rainfall-Runoff Process Through Regressions

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Campos-Aranda Daniel Francisco

2014-10-01

Full Text Available To solve the problems associated with the assessment of water resources of a river, the modeling of the rainfall-runoff process (RRP allows the deduction of runoff missing data and to extend its record, since generally the information available on precipitation is larger. It also enables the estimation of inputs to reservoirs, when their building led to the suppression of the gauging station. The simplest mathematical model that can be set for the RRP is the linear regression or curve on a monthly basis. Such a model is described in detail and is calibrated with the simultaneous record of monthly rainfall and runoff in Ballesmi hydrometric station, which covers 35 years. Since the runoff of this station has an important contribution from the spring discharge, the record is corrected first by removing that contribution. In order to do this a procedure was developed based either on the monthly average regional runoff coefficients or on nearby and similar watershed; in this case the Tancuilín gauging station was used. Both stations belong to the Partial Hydrologic Region No. 26 (Lower Rio Panuco and are located within the state of San Luis Potosi, México. The study performed indicates that the monthly regression model, due to its conceptual approach, faithfully reproduces monthly average runoff volumes and achieves an excellent approximation in relation to the dispersion, proved by calculation of the means and standard deviations.

Genetic evaluation of European quails by random regression models

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Flaviana Miranda Gonçalves

2012-09-01

Full Text Available The objective of this study was to compare different random regression models, defined from different classes of heterogeneity of variance combined with different Legendre polynomial orders for the estimate of (covariance of quails. The data came from 28,076 observations of 4,507 female meat quails of the LF1 lineage. Quail body weights were determined at birth and 1, 14, 21, 28, 35 and 42 days of age. Six different classes of residual variance were fitted to Legendre polynomial functions (orders ranging from 2 to 6 to determine which model had the best fit to describe the (covariance structures as a function of time. According to the evaluated criteria (AIC, BIC and LRT, the model with six classes of residual variances and of sixth-order Legendre polynomial was the best fit. The estimated additive genetic variance increased from birth to 28 days of age, and dropped slightly from 35 to 42 days. The heritability estimates decreased along the growth curve and changed from 0.51 (1 day to 0.16 (42 days. Animal genetic and permanent environmental correlation estimates between weights and age classes were always high and positive, except for birth weight. The sixth order Legendre polynomial, along with the residual variance divided into six classes was the best fit for the growth rate curve of meat quails; therefore, they should be considered for breeding evaluation processes by random regression models.

Significance tests to determine the direction of effects in linear regression models.

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Wiedermann, Wolfgang; Hagmann, Michael; von Eye, Alexander

2015-02-01

Previous studies have discussed asymmetric interpretations of the Pearson correlation coefficient and have shown that higher moments can be used to decide on the direction of dependence in the bivariate linear regression setting. The current study extends this approach by illustrating that the third moment of regression residuals may also be used to derive conclusions concerning the direction of effects. Assuming non-normally distributed variables, it is shown that the distribution of residuals of the correctly specified regression model (e.g., Y is regressed on X) is more symmetric than the distribution of residuals of the competing model (i.e., X is regressed on Y). Based on this result, 4 one-sample tests are discussed which can be used to decide which variable is more likely to be the response and which one is more likely to be the explanatory variable. A fifth significance test is proposed based on the differences of skewness estimates, which leads to a more direct test of a hypothesis that is compatible with direction of dependence. A Monte Carlo simulation study was performed to examine the behaviour of the procedures under various degrees of associations, sample sizes, and distributional properties of the underlying population. An empirical example is given which illustrates the application of the tests in practice. © 2014 The British Psychological Society.

Exercise 5+6 - Introduction to Control and Lab Exercises