Download or read book Semiparametric Regression Models for Interacting Covariates written by Clemontina Alexander Davenport and published by . This book was released on 2013 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Regression written by David Ruppert and published by Cambridge University Press. This book was released on 2003-07-14 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Even experts on semiparametric regression should find something new here.

Download or read book Efficient Inference in General Semiparametric Regression Models written by Arnab Maity and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Semiparametric regression has become very popular in the field of Statistics over the years. While on one hand more and more sophisticated models are being developed, on the other hand the resulting theory and estimation process has become more and more involved. The main problems that are addressed in this work are related to efficient inferential procedures in general semiparametric regression problems. We first discuss efficient estimation of population-level summaries in general semiparametric regression models. Here our focus is on estimating general population-level quantities that combine the parametric and nonparametric parts of the model (e.g., population mean, probabilities, etc.). We place this problem in a general context, provide a general kernel-based methodology, and derive the asymptotic distributions of estimates of these population-level quantities, showing that in many cases the estimates are semiparametric efficient. Next, motivated from the problem of testing for genetic effects on complex traits in the presence of gene-environment interaction, we consider developing score test in general semiparametric regression problems that involves Tukey style 1 d.f form of interaction between parametrically and non-parametrically modeled covariates. We develop adjusted score statistics which are unbiased and asymptotically efficient and can be performed using standard bandwidth selection methods. In addition, to over come the difficulty of solving functional equations, we give easy interpretations of the target functions, which in turn allow us to develop estimation procedures that can be easily implemented using standard computational methods. Finally, we take up the important problem of estimation in a general semiparametric regression model when covariates are measured with an additive measurement error structure having normally distributed measurement errors. In contrast to methods that require solving integral equation of dimension the size of the covariate measured with error, we propose methodology based on Monte Carlo corrected scores to estimate the model components and investigate the asymptotic behavior of the estimates. For each of the problems, we present simulation studies to observe the performance of the proposed inferential procedures. In addition, we apply our proposed methodology to analyze nontrivial real life data sets and present the results.

Download or read book Regression written by Ludwig Fahrmeir and published by Springer Science & Business Media. This book was released on 2013-05-09 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.

Download or read book Regression written by Ludwig Fahrmeir and published by Springer Nature. This book was released on 2022-03-15 with total page 759 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this textbook provides an applied and unified introduction to parametric, nonparametric and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through numerous examples and case studies. The most important definitions and statements are concisely summarized in boxes, and the underlying data sets and code are available online on the book’s dedicated website. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. The chapters address the classical linear model and its extensions, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression, quantile regression and distributional regression models. Two appendices describe the required matrix algebra, as well as elements of probability calculus and statistical inference. In this substantially revised and updated new edition the overview on regression models has been extended, and now includes the relation between regression models and machine learning, additional details on statistical inference in structured additive regression models have been added and a completely reworked chapter augments the presentation of quantile regression with a comprehensive introduction to distributional regression models. Regularization approaches are now more extensively discussed in most chapters of the book. The book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written at an intermediate mathematical level and assumes only knowledge of basic probability, calculus, matrix algebra and statistics.

Download or read book Semiparametric Regression for the Social Sciences written by Luke John Keele and published by John Wiley & Sons. This book was released on 2008-04-15 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and data sets. It also demonstrates the potential of these techniques using detailed empirical examples drawn from the social and political sciences. Each chapter includes exercises and examples and there is a supplementary website containing all the datasets used, as well as computer code, allowing readers to replicate every analysis reported in the book. Includes software for implementing the methods in S-Plus and R.

Download or read book Semiparametric Regression Models for Between and Within subject Attributes written by Jinyuan Liu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breakthroughs such as high-throughput sequencing are generating flourishing high-dimensional data that provoke challenges in both statistical analyses and interpretations. Since directly modeling such data often suffers from multiple testing and low power, an emerging alternative is to first reduce the dimension at the outset, by comparing two subjects' genome sequences using dissimilarity metrics, yielding "between-subject attributes." In the first half of this talk, I will extend the classical generalized linear models (GLM) to establish a new regression paradigm for between-subject attributes, using a class of semiparametric functional response models (FRM). Despite its growing applications, the efficiency of estimators for the FRM has not yet been carefully studied. This is of fundamental importance for semiparametric models due to the efficiency loss at the price of minimum model assumptions. For the next half of the talk, we leverage the Hilbert-Space-based semiparametric efficiency theory to show that estimators from a class of U-statistics-based generalized estimating equation (UGEE) achieve the semiparametric efficiency bound. Thus, like GEE for semiparametric GLM, UGEE estimators also harmonize efficiency and robustness, propelling growing applications in biomedical, psychosocial, and related research.

Download or read book robust estimations of semiparametric regression models written by martin r. young and published by . This book was released on 1997 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Regression And Time Series Model Selection written by Allan D R Mcquarrie and published by World Scientific. This book was released on 1998-05-30 with total page 479 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important book describes procedures for selecting a model from a large set of competing statistical models. It includes model selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including wavelets) and semiparametric regression models, and quasi-likelihood and robust regression models. Information-based model selection criteria are discussed, and small sample and asymptotic properties are presented. The book also provides examples and large scale simulation studies comparing the performances of information-based model selection criteria, bootstrapping, and cross-validation selection methods over a wide range of models.

Download or read book Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates written by Jeffrey R. Wilson and published by Springer Nature. This book was released on 2020-09-28 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides a concise point of research topics and reference for modeling correlated response data with time-dependent covariates, and longitudinal data for the analysis of population-averaged models, highlighting methods by a variety of pioneering scholars. While the models presented in the volume are applied to health and health-related data, they can be used to analyze any kind of data that contain covariates that change over time. The included data are analyzed with the use of both R and SAS, and the data and computing programs are provided to readers so that they can replicate and implement covered methods. It is an excellent resource for scholars of both computational and methodological statistics and biostatistics, particularly in the applied areas of health. ​

Download or read book Semiparametric Regression in Likelihood Based Models written by Sally A. Hunsberger and published by . This book was released on 1990 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Applied Multiple Regression correlation Analysis for the Behavioral Sciences written by Jacob Cohen and published by Taylor & Francis. This book was released on 1983 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying CD with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT. Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.

Download or read book Empirical Likelihood for a Class of Semiparametric Regression Models written by Nan Bi and published by . This book was released on 2016 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays on Nonparametric and Semiparametric Regression Models written by Feng Yao and published by . This book was released on 2004 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Nonparametric and Semiparametric Modeling written by Bo Kai and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, several new statistical procedures in nonparametric and semiparametric models are proposed. The concerns of the research are efficiency, robustness and sparsity. In Chapter 3, we propose complete composite quantile regression (CQR) procedures for estimating both the regression function and its derivatives in fully nonparametric regression models by using local smoothing techniques. The CQR estimator was recently proposed by Zou and Yuan (2008) for estimating the regression coefficients in the classical linear regression model. The asymptotic theory of the proposed estimator was established. We show that, compared with the classical local linear least squares estimator, the new method can significantly improve the estimation efficiency of the local linear least squares estimator for commonly used non-normal error distributions, and at the same time, the loss in efficiency is at most 8.01% in the worst case scenario. In Chapter 4, we further consider semiparametric models. The complexity of semiparametric models poses new challenges to parametric inferences and model selection that frequently arise from real applications. We propose new robust inference procedures for the semiparametric varying-coefficient partially linear model. We first study a quantile regression estimate for the nonparametric varying-coefficient functions and the parametric regression coefficients. To improve efficiency, we further develop a composite quantile regression procedure for both parametric and nonparametric components. To achieve sparsity, we develop a variable selection procedure for this model to select significant variables. We study the sampling properties of the resulting quantile regression estimate and composite quantile regression estimate. With proper choices of penalty functions and regularization parameters, we show the proposed variable selection procedure possesses the oracle property in the terminology of Fan and Li (2001). In Chapter 5, we propose a novel estimation procedure for varying coefficient models based on local ranks. By allowing the regression coefficients to change with certain covariates, the class of varying coefficient models offers a flexible semiparametric approach to modeling nonlinearity and interactions between covariates. Varying coefficient models are useful nonparametric regression models and have been well studied in the literature. However, the performance of existing procedures can be adversely influenced by outliers. The new procedure provides a highly efficient and robust alternative to the local linear least squares method and can be conveniently implemented using existing R software packages. We study the sample properties of the proposed procedure and establish the asymptotic normality of the resulting estimate. We also derive the asymptotic relative efficiency of the proposed local rank estimate to the local linear estimate for the varying coefficient model. The gain of the local rank regression estimate over the local linear regression estimate can be substantial. We further develop nonparametric inferences for the rank-based method. Monte Carlo simulations are conducted to access the finite sample performance of the proposed estimation procedure. The simulation results are promising and consistent with our theoretical findings. All the proposed procedures are supported by intensive finite sample simulation studies and most are illustrated with real data examples.

Download or read book Semiparametric Regression with Random Effects written by Sungwook Lee and published by . This book was released on 1997 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we present a semiparametric regression model with random effects. Mixed models with fixed effects and random subject effects are popular in longitudinal studies where observations within each subject have a serial correlation and each subject has random variation. Recently, Moyeed and Diggle (1994) studied an additive model for longitudinal data with parametric and nonparametric terms without explicit random subject effects. A new method based on the partial linear model is presented, with explicit random effects. In this study, we will present theoretical asymptotic properties of the estimators of the parametric and nonparametric parts for balanced designs. Simulation results along with an analysis of real data are also considered for balanced and unbalanced design. Numerical results suggest that the new method performs better with unbalanced designs.

Download or read book Efficient Nonparametric and Semiparametric Regression Methods with Application in Case Control Studies written by Shahina Rahman and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Regression Analysis is one of the most important tools of statistics which is widely used in other scientific fields for projection and modeling of association between two variables. Nowadays with modern computing techniques and super high performance devices, regression analysis on multiple dimensions has become an important issue. Our task is to address the issue of modeling with no assumption on the mean and the variance structure and further with no assumption on the error distribution. In other words, we focus on developing robust semiparametric and nonparamteric regression problems. In modern genetic epidemiological association studies, it is often important to investigate the relationships among the potential covariates related to disease in case-control data, a study known as "Secondary Analysis". First we focus to model the association between the potential covariates in univariate dimension nonparametrically. Then we focus to model the association in mulivariate set up by assuming a convenient and popular multivariate semiparametric model, known as Single-Index Model. The secondary analysis of case-control studies is particularly challenging due to multiple reasons (a) the case-control sample is not a random sample, (b) the logistic intercept is practically not identifiable and (c) misspecification of error distribution leads to inconsistent results. For rare disease, controls (individual free of disease) are typically used for valid estimation. However, numerous publication are done to utilize the entire case-control sample (including the diseased individual) to increase the efficiency. Previous work in this context has either specified a fully parametric distribution for regression errors or specified a homoscedastic distribution for the regression errors or have assumed parametric forms on the regression mean. In the first chapter we focus on to predict an univariate covariate Y by another potential univariate covariate X neither by any parametric form on the mean function nor by any distributional assumption on error, hence addressing potential heteroscedasticity, a problem which has not been studied before. We develop a tilted Kernel based estimator which is a first attempt to model the mean function nonparametrically in secondary analysis. In the following chapters, we focus on i.i.d samples to model both the mean and variance function for predicting Y by multiple covariates X without assuming any form on the regression mean. In particular we model Y by a single-index model m(X^T [Lowercase theta symbol]), where [Lowercase theta symbol] is a single-index vector and m is unspecified. We also model the variance function by another flexible single index model. We develop a practical and readily applicable Bayesian methodology based on penalized spline and Markov Chain Monte Carlo (MCMC) both in i.i.d set up and in case-control set up. For efficient estimation, we model the error distribution by a Dirichlet process mixture models of Normals (DPMM). In numerical examples, we illustrate the finite sample performance of the posterior estimates for both i.i.d and for case-control set up. For single-index set up, in i.i.d case only one existing work based on local linear kernel method addresses modeling of the variance function. We found that our method based on DPMM vastly outperforms the other existing method in terms of mean square efficiency and computation stability. We develop the single-index modeling in secondary analysis to introduce flexible mean and variance function modeling in case-control studies, a problem which has not been studies before. We showed that our method is almost 2 times efficient than using only controls, which is typically used for many cases. We use the real data example from NIH-AARP study on breast cancer, from Colon Cancer Study on red meat consumption and from National Morbidity Air Pollution Study to illustrate the computational efficiency and stability of our methods. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/155719