Multivariate Semiparametric Regression Models For Longitudinal Data PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Multivariate Semiparametric Regression Models For Longitudinal Data PDF full book. Access full book title Multivariate Semiparametric Regression Models For Longitudinal Data.
| Author | : Zhuokai Li |
| Publisher | : |
| Total Pages | : 168 |
| Release | : 2014 |
| Genre | : Binary system (Mathematics) |
| ISBN | : |
Download Multivariate Semiparametric Regression Models for Longitudinal Data Book in PDF, ePub and Kindle
Multiple-outcome longitudinal data are abundant in clinical investigations. For example, infections with different pathogenic organisms are often tested concurrently, and assessments are usually taken repeatedly over time. It is therefore natural to consider a multivariate modeling approach to accommodate the underlying interrelationship among the multiple longitudinally measured outcomes. This dissertation proposes a multivariate semiparametric modeling framework for such data. Relevant estimation and inference procedures as well as model selection tools are discussed within this modeling framework. The first part of this research focuses on the analytical issues concerning binary data. The second part extends the binary model to a more general situation for data from the exponential family of distributions. The proposed model accounts for the correlations across the outcomes as well as the temporal dependency among the repeated measures of each outcome within an individual. An important feature of the proposed model is the addition of a bivariate smooth function for the depiction of concurrent nonlinear and possibly interacting influences of two independent variables on each outcome. For model implementation, a general approach for parameter estimation is developed by using the maximum penalized likelihood method. For statistical inference, a likelihood-based resampling procedure is proposed to compare the bivariate nonlinear effect surfaces across the outcomes. The final part of the dissertation presents a variable selection tool to facilitate model development in practical data analysis. Using the adaptive least absolute shrinkage and selection operator (LASSO) penalty, the variable selection tool simultaneously identifies important fixed effects and random effects, determines the correlation structure of the outcomes, and selects the interaction effects in the bivariate smooth functions. Model selection and estimation are performed through a two-stage procedure based on an expectation-maximization (EM) algorithm. Simulation studies are conducted to evaluate the performance of the proposed methods. The utility of the methods is demonstrated through several clinical applications.
| Author | : Garrett Fitzmaurice |
| Publisher | : CRC Press |
| Total Pages | : 633 |
| Release | : 2008-08-11 |
| Genre | : Mathematics |
| ISBN | : 142001157X |
Download Longitudinal Data Analysis Book in PDF, ePub and Kindle
Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory
| Author | : Liang Zhu |
| Publisher | : |
| Total Pages | : |
| Release | : 2008 |
| Genre | : Electronic dissertations |
| ISBN | : |
Download Semiparametric Analysis of Multivariate Longitudinal Data Book in PDF, ePub and Kindle
Longitudinal studies are conducted widely in fields such as agriculture and life sciences, business and industry, demography and other social sciences, medicine and public health. In longitudinal studies, individuals are measured repeatedly over time and multivariate longitudinal data occur when subjects are measured repeatedly with regard to multiple response variables. Analysis of multivariate longitudinal data can be challenging since it requires accounting for not only correlations between repeated measures of the same subject but also correlations among different response variables. One special type of longitudinal study involves monitoring subjects continuously to record occurrences of events and thus generates so-called recurrent event data. In the first part of this dissertation, we will discuss analysis of a set of multivariate longitudinal data arising from a prospective study of alcohol and drug use in college freshmen. Several statistical models and estimation approaches are presented for joint analysis of conducting alcohol and drug use. In particular, a marginal means model is proposed that leaves the correlation between response outcomes arbitrary. In the second part, regression analysis of multivariate recurrent event data with time-varying covariate effects will be considered. For the problem, we present some marginal models for the underlying counting processes and develop estimating equation based inference approaches. The asymptotic properties of the proposed estimates are established and their finite sample properties are evaluated through simulation studies. Additionally, some procedures are presented for testing the time-dependence of covariate effects and the proposed methodology is applied to sets of univariate recurrent event data and bivariate recurrent event data. The third part of this dissertation will consider variable selection for univariate and multivariate recurrent event data in the context of regression analysis. For the problem, we adopt the idea behind the nonconcave penalized likelihood approach proposed in Fan and Li (2001) and develop a nonconcave penalized estimating function approach. The proposed approach selects variables and estimates regression coefficients simultaneously and an algorithm is presented for this process. We show that the proposed approach performs as well as the oracle procedure, yielding estimates as if the correct submodel were known. Simulation studies conducted for assessing the performance of the proposed approach suggest that it works well for practical situations. The methodology is illustrated using a set of bivariate recurrent event data.
| Author | : David J. Hand |
| Publisher | : Routledge |
| Total Pages | : 248 |
| Release | : 2017-10-06 |
| Genre | : Mathematics |
| ISBN | : 1351422650 |
Download Practical Longitudinal Data Analysis Book in PDF, ePub and Kindle
This text describes regression-based approaches to analyzing longitudinal and repeated measures data. It emphasizes statistical models, discusses the relationships between different approaches, and uses real data to illustrate practical applications. It uses commercially available software when it exists and illustrates the program code and output. The data appendix provides many real data sets-beyond those used for the examples-which can serve as the basis for exercises.
| Author | : Peter Diggle |
| Publisher | : Oxford University Press, USA |
| Total Pages | : 397 |
| Release | : 2013-03-14 |
| Genre | : Language Arts & Disciplines |
| ISBN | : 0199676755 |
Download Analysis of Longitudinal Data Book in PDF, ePub and Kindle
This second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.
| Author | : Hans-Georg Müller |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 208 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1461239265 |
Download Nonparametric Regression Analysis of Longitudinal Data Book in PDF, ePub and Kindle
This monograph reviews some of the work that has been done for longitudi nal data in the rapidly expanding field of nonparametric regression. The aim is to give the reader an impression of the basic mathematical tools that have been applied, and also to provide intuition about the methods and applications. Applications to the analysis of longitudinal studies are emphasized to encourage the non-specialist and applied statistician to try these methods out. To facilitate this, FORTRAN programs are provided which carry out some of the procedures described in the text. The emphasis of most research work so far has been on the theoretical aspects of nonparametric regression. It is my hope that these techniques will gain a firm place in the repertoire of applied statisticians who realize the large potential for convincing applications and the need to use these techniques concurrently with parametric regression. This text evolved during a set of lectures given by the author at the Division of Statistics at the University of California, Davis in Fall 1986 and is based on the author's Habilitationsschrift submitted to the University of Marburg in Spring 1985 as well as on published and unpublished work. Completeness is not attempted, neither in the text nor in the references. The following persons have been particularly generous in sharing research or giving advice: Th. Gasser, P. Ihm, Y. P. Mack, V. Mammi tzsch, G . G. Roussas, U. Stadtmuller, W. Stute and R.
| Author | : Shirong Deng |
| Publisher | : |
| Total Pages | : 159 |
| Release | : 2012 |
| Genre | : Longitudinal method |
| ISBN | : |
Download Semiparametric Regression Analysis of Longitudinal Data with Informative Observation Times Book in PDF, ePub and Kindle
| Author | : Donald Hedeker |
| Publisher | : John Wiley & Sons |
| Total Pages | : 360 |
| Release | : 2006-05-12 |
| Genre | : Mathematics |
| ISBN | : 0470036478 |
Download Longitudinal Data Analysis Book in PDF, ePub and Kindle
Longitudinal data analysis for biomedical and behavioral sciences This innovative book sets forth and describes methods for the analysis of longitudinaldata, emphasizing applications to problems in the biomedical and behavioral sciences. Reflecting the growing importance and use of longitudinal data across many areas of research, the text is designed to help users of statistics better analyze and understand this type of data. Much of the material from the book grew out of a course taught by Dr. Hedeker on longitudinal data analysis. The material is, therefore, thoroughly classroom tested and includes a number of features designed to help readers better understand and apply the material. Statistical procedures featured within the text include: * Repeated measures analysis of variance * Multivariate analysis of variance for repeated measures * Random-effects regression models (RRM) * Covariance-pattern models * Generalized-estimating equations (GEE) models * Generalizations of RRM and GEE for categorical outcomes Practical in their approach, the authors emphasize the applications of the methods, using real-world examples for illustration. Some syntax examples are provided, although the authors do not generally focus on software in this book. Several datasets and computer syntax examples are posted on this title's companion Web site. The authors intend to keep the syntax examples current as new versions of the software programs emerge. This text is designed for both undergraduate and graduate courses in longitudinal data analysis. Instructors can take advantage of overheads and additional course materials available online for adopters. Applied statisticians in biomedicine and the social sciences can also use the book as a convenient reference.
| Author | : Robert E. Weiss |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 445 |
| Release | : 2006-12-06 |
| Genre | : Medical |
| ISBN | : 0387283145 |
Download Modeling Longitudinal Data Book in PDF, ePub and Kindle
The book features many figures and tables illustrating longitudinal data and numerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce the material. Weiss emphasizes continuous data rather than discrete data, graphical and covariance methods, and generalizations of regression rather than generalizations of analysis of variance.
| Author | : Sylvie Tchumtchoua |
| Publisher | : |
| Total Pages | : |
| Release | : 2010 |
| Genre | : Electronic dissertations |
| ISBN | : |
Download Beyesian Semiparametric Models for Discrete Longitudinal Data Book in PDF, ePub and Kindle
Discrete longitudinal data are common in various disciplines and are often used to assess the change over time of one or several outcomes, and/or what covariates might be associated with the outcomes. Existing parametric and nonparametric/semiparametric models typically attribute the heterogeneity across subjects and/or through time to the effects of included explanatory variables or the effect of omitted variables that do not vary across subjects and over time. This dissertation focuses on developing new flexible semiparametric models for discrete longitudinal data using Dirichlet processes. It consists of three parts. In chapter 2 we propose a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the distributions of the factors are modeled nonparametrically through a dynamic Dirichlet process. A Markov chain Monte Carlo algorithm is developed for fitting the model, and the methodology is applied to study the dynamics of public attitudes toward science and technology in the United States over the period 1992-2001. In chapter 3 we consider the estimation of nonparametric regression for binary longitudinal data. Instead of assuming a parametric link function, we specify the joint distribution of the covariates and the latent variable underlying the binary outcome as a multivariate normal with subject and time-specific mean vector and covariance matrix. We then modeled the distribution of these parameters nonparametrically using a dynamic Dirichlet process. The resulting binary regression model is a finite mixture of probit regressions and a nonlinear regression. The proposed model is more flexible than the existing models in that it models the relationship between the binary response and the covariates nonparametrically while at the same time allowing the shape of the relationship to change over time. The methodology is illustrated using simulated data and a real dataset, the data on labor force participation of married women in the US over the period 1979 to 1992. Finally, chapter 4 proposes two functional generalized linear models where the response variables are discrete functional data and one of the covariates is also functional. Functional regression is combined with penalized B-splines in a semiparametric Bayesian framework to jointly estimate the response model and the predictor curves, clustering curves with similar shapes. The methodology is applied to study the price and bids arrivals dynamics in online auctions using data for the palm M515 Personal Digital Assistant (PDA) units from eBay.com.
