Download or read book Bayesian Semiparametric Models for Discrete Longitudinal Data written by Sylvie Tchumtchoua and published by . This book was released on 2010 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Beyesian Semiparametric Models for Discrete Longitudinal Data written by Sylvie Tchumtchoua and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Bayesian Semiparametric Joint Modeling of Longitudinal Predictors and Discrete Outcomes written by Woobeen Lim and published by . This book was released on 2021 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many prospective biomedical studies collect data on longitudinal variables that are predictive of a discrete outcome and oftentimes, primary interest lies in the association between the outcome and the values of the longitudinal measurements at a specific time point. A common problem in these longitudinal studies is inconsistency in timing of measurements and missing follow-ups since few subjects have values close to the time of interest. Another difficulty arises from the fact that numerous studies collect longitudinal measurements with different scales, as there is no known multivariate distribution that is capable of accommodating variables of mixed scale simultaneously. These challenges are well demonstrated in our motivating data example, the Life and Longevity After Cancer (LILAC), a cohort study of cancer survivors who participated in the Women's Health Initiative (WHI). One research area of interest in these studies is to determine the relationship between lifestyle or health measures recorded in the WHI with treatment-related outcomes measured in LILAC. For instance, a researcher may want to examine if sleep-related factors measured prior to initial cancer treatment, such as insomnia rating scale (a continuous variable), sleep duration (ordinal) and depression (binary) imputed at the time of cancer diagnosis can predict the incidence of adverse effects of cancer treatment. Despite the multitude of such applications in biostatistical areas, no previous methods exist that are able to tackle these challenges. In this work, we propose a new class of Bayesian joint models for a discrete outcome and longitudinal predictors of mixed scale. Our model consists of two submodels: 1) a longitudinal submodel which uses a latent normal random variable construction with regression splines to model time-dependent trends with a Dirichlet Process prior assigned to random effects to relax distribution assumptions and 2) an outcome submodel which standardizes timing of the predictors by relating the discrete outcome to the imputed longitudinal values at a set time point. We present two outcome models that will accommodate either a binary or count outcome, which will be used to model the incidence of insomnia and the number of symptoms after initial cancer treatment in LILAC, respectively. The proposed models will be evaluated via simulation studies to demonstrate their performance in comparison with other competing models.

Download or read book Models for Discrete Longitudinal Data written by Geert Molenberghs and published by Springer Science & Business Media. This book was released on 2006-08-30 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: The linear mixed model has become the main parametric tool for the analysis of continuous longitudinal data, as the authors discussed in their 2000 book. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The authors received the American Statistical Association's Excellence in Continuing Education Award based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004.

Download or read book Bayesian Semiparametric Modeling and Inference for Longitudinal functional Data and Parametric Modeling for the Evaluation of Diagnostic Screening Procedures written by Young-Ku Choi and published by . This book was released on 2005 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Nonparametric Regression Methods for Longitudinal Data Analysis written by Hulin Wu and published by John Wiley & Sons. This book was released on 2006-05-12 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporates mixed-effects modeling techniques for more powerful and efficient methods This book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques into various nonparametric regression models. The authors emphasize modeling ideas and inference methodologies, although some theoretical results for the justification of the proposed methods are presented. With its logical structure and organization, beginning with basic principles, the text develops the foundation needed to master advanced principles and applications. Following a brief overview, data examples from biomedical research studies are presented and point to the need for nonparametric regression analysis approaches. Next, the authors review mixed-effects models and nonparametric regression models, which are the two key building blocks of the proposed modeling techniques. The core section of the book consists of four chapters dedicated to the major nonparametric regression methods: local polynomial, regression spline, smoothing spline, and penalized spline. The next two chapters extend these modeling techniques to semiparametric and time varying coefficient models for longitudinal data analysis. The final chapter examines discrete longitudinal data modeling and analysis. Each chapter concludes with a summary that highlights key points and also provides bibliographic notes that point to additional sources for further study. Examples of data analysis from biomedical research are used to illustrate the methodologies contained throughout the book. Technical proofs are presented in separate appendices. With its focus on solving problems, this is an excellent textbook for upper-level undergraduate and graduate courses in longitudinal data analysis. It is also recommended as a reference for biostatisticians and other theoretical and applied research statisticians with an interest in longitudinal data analysis. Not only do readers gain an understanding of the principles of various nonparametric regression methods, but they also gain a practical understanding of how to use the methods to tackle real-world problems.

Download or read book Basic and Advanced Bayesian Structural Equation Modeling written by Sik-Yum Lee and published by John Wiley & Sons. This book was released on 2012-07-05 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides clear instructions to researchers on how to apply Structural Equation Models (SEMs) for analyzing the inter relationships between observed and latent variables. Basic and Advanced Bayesian Structural Equation Modeling introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered categorical data, multilevel data, mixture data, longitudinal data, highly non-normal data, as well as some of their combinations. In addition, Bayesian semiparametric SEMs to capture the true distribution of explanatory latent variables are introduced, whilst SEM with a nonparametric structural equation to assess unspecified functional relationships among latent variables are also explored. Statistical methodologies are developed using the Bayesian approach giving reliable results for small samples and allowing the use of prior information leading to better statistical results. Estimates of the parameters and model comparison statistics are obtained via powerful Markov Chain Monte Carlo methods in statistical computing. Introduces the Bayesian approach to SEMs, including discussion on the selection of prior distributions, and data augmentation. Demonstrates how to utilize the recent powerful tools in statistical computing including, but not limited to, the Gibbs sampler, the Metropolis-Hasting algorithm, and path sampling for producing various statistical results such as Bayesian estimates and Bayesian model comparison statistics in the analysis of basic and advanced SEMs. Discusses the Bayes factor, Deviance Information Criterion (DIC), and $L_\nu$-measure for Bayesian model comparison. Introduces a number of important generalizations of SEMs, including multilevel and mixture SEMs, latent curve models and longitudinal SEMs, semiparametric SEMs and those with various types of discrete data, and nonparametric structural equations. Illustrates how to use the freely available software WinBUGS to produce the results. Provides numerous real examples for illustrating the theoretical concepts and computational procedures that are presented throughout the book. Researchers and advanced level students in statistics, biostatistics, public health, business, education, psychology and social science will benefit from this book.

Download or read book Bayesian Semiparametric Regression for Censored and Incomplete Longitudinal Data written by Li Su and published by . This book was released on 2007 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bayesian Nonparametric and Semi parametric Methods for Incomplete Longitudinal Data written by Chenguang Wang and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In Chapter 4, we discuss pattern mixture models. Pattern mixture modeling is a popular approach for handling incomplete longitudinal data. Such models are not identifiable by construction. Identifying restrictions are one approach to mixture model identification (Daniels and Hogan, 2008; Kenward et al., 2003; Little, 1995; Little and Wang, 1996; Thijs et al., 2002) and are a natural starting point for missing not at random sensitivity analysis (Daniels and Hogan, 2008; Thijs et al., 2002). However, when the pattern specific models are multivariate normal (MVN), identifying restrictions corresponding to missing at random may not exist. Furthermore, identification strategies can be problematic in models with covariates (e.g. baseline covariates with time-invariant coefficients). In this paper, we explore conditions necessary for identifying restrictions that result in missing at random (MAR) to exist under a multivariate normality assumption and strategies for identifying sensitivity parameters for sensitivity analysis or for a fully Bayesian analysis with informative priors. A longitudinal clinical trial is used for illustration of sensitivity analysis. Problems caused by baseline covariates with time-invariant coefficients are investigated and an alternative identifying restriction based on residuals is proposed as a solution.

Download or read book Semiparametric Bayesian Estimation of Discrete Choice Models written by Sylvie Tchumtchoua and published by . This book was released on 2007 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mixed Effects Models for Complex Data written by Lang Wu and published by CRC Press. This book was released on 2009-11-11 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

Download or read book Bayesian Analysis of Discrete Longitudinal Data written by Nicholas A. Bernini and published by . This book was released on 2006 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bayesian Semiparametric Inference of Complex Longitudinal and Multiple Time Series Systems written by Jingjing Fan (Ph. D.) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series inference differs from traditional statistical analysis in that there is inherent dependence between observations in a time series. In the case of multiple time series, multivariate time series, or panel data, performing inference can become even more complex because of possible interactions between different subjects, variables, or both. We develop three new methodologies capable of performing inference on multiple time series, high dimensional multivariate time series, and panel data respectively. For multiple time series, we combine functional analysis with a Hidden Markov model to create a clustering algorithm that allows each time series to change its cluster membership over time. For high dimensional multivariate time series, we develop a tensor decomposition estimation method for the Vector Autoregressive (VAR) model which greatly reduces the parameter space without sacrificing accuracy. We extend the tensor decomposed VAR into a random effects model to allow for information sharing between subjects in multi-subject panels. For panels with many subjects, we employ a divide-and-conquer strategy with embarrassingly parallel samplers to lessen the computational burden on a single estimation process

Download or read book Missing Data in Longitudinal Studies written by Michael J. Daniels and published by CRC Press. This book was released on 2008-03-11 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing from the authors' own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ

Download or read book Bayesian Semiparametric Inference for Longitudinal Data with Applications written by Silvia Mongelluzzo and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bayesian Semiparametric Multi state Models written by and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-state models provide a unified framework for the description of the evolution of discrete phenomena in continuous time. One particular example are Markov processes which can be characterised by a set of time-constant transition intensities between the states. In this paper, we will extend such parametric approaches to semiparametric models with flexible transition intensities based on Bayesian versions of penalised splines. The transition intensities will be modelled as smooth functions of time and can further be related to parametric as well as nonparametric covariate effects. Covariates with time-varying effects and frailty terms can be included in addition. Inference will be conducted either fully Bayesian using Markov chain Monte Carlo simulation techniques or empirically Bayesian based on a mixed model representation. A counting process representation of semiparametric multi-state models provides the likelihood formula and also forms the basis for model validation via martingale residual processes. As an application, we will consider human sleep data with a discrete set of sleep states such as REM and Non-REM phases. In this case, simple parametric approaches are inappropriate since the dynamics underlying human sleep are strongly varying throughout the night and individual-specific variation has to be accounted for using covariate information and frailty terms. -- frailties ; martingale residuals ; multi-state models ; penalised splines ; time-varying effects ; transition intensities

Download or read book Bayesian Partition Models for Local Inference in Longitudinal and Survival Data written by Giorgio Paulon and published by . This book was released on 2021 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation proposes novel Bayesian semiparametric and nonparametric methods for complex, large and potentially high-dimensional longitudinal and survival data. The first part, comprising the bulk of this thesis, develops sophisticated dynamic partition models for longitudinal data that allow common features to be shared across some time segments while differing across others. These ideas are then specifically adapted to develop novel drift-diffusion models for the analysis of behavioral data on category learning in auditory neuroscience. The second part of this work proposes a bivariate survival regression method, borrowing information across two outcomes via common features in parts of the induced marginal partitions. In terms of flexibility and interpretability, the methods presented here provide significant improvements over many previously available tools and techniques, leading to interesting, novel and meaningful inference in many diverse application areas