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Bayesian Semiparametric Modeling And Inference For Longitudinal Functional Data And Parametric Modeling For The Evaluation Of Diagnostic Screening Procedures
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Book Synopsis Bayesian Semiparametric Modeling and Inference for Longitudinal/functional Data and Parametric Modeling for the Evaluation of Diagnostic Screening Procedures by : Young-Ku Choi
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:
Book Synopsis Bayesian Semiparametric Inference of Complex Longitudinal and Multiple Time Series Systems by : Jingjing Fan (Ph. D.)
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
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2006 with total page 942 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A New Class of Bayesian Semi-Parametric Joint Longitudinal-Survival Models for Biomarker Discovery by : Sepehr Akhavan Masouleh
Download or read book A New Class of Bayesian Semi-Parametric Joint Longitudinal-Survival Models for Biomarker Discovery written by Sepehr Akhavan Masouleh and published by . This book was released on 2016 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: In studying the progression of a disease and to better predict time to death (survival data), investigators often collect repeated measures over time (longitudinal data) and are interested in testing the association between risk factors, including collected repeated measures, and time to death. One such example is testing the association between the biomarker serum albumin that is measured repeatedly on end-stage renal disease (ESRD) patients. A modeling framework that is capable of modeling longitudinal and survival outcomes simultaneously is called a joint longitudinal-survival model.Joint longitudinal-survival models have received a great deal of attention over the past years where many different joint models have been proposed. Joint models commonly make parametric assumptions on either the functional form of the repeated measures or on the distribution of survival times. In this dissertation we are interested in joint models that are robust to common parametric and semi-parameteric survival and longitudinal assumptions. We propose a flexible Bayesian joint longitudinal-survival framework that avoids common parametric and semi-parameteric assumptions. More specifically, our modeling framework incorporates a flexible longitudinal component by utilizing Gaussian process (GP) technique. This technique avoids any explicit functional assumption on the trajectory of the repeated measures. Our modeling framework also uses Dirichlet process (DP) prior to avoid explicit distributional assumptions on survival times.We further extend our framework to modeling multiple longitudinal processes simultaneously. We propose a multivariate joint longitudinal-survival technique to jointly model the association between multiple longitudinal processes with survival outcomes. Our proposed technique is capable of taking correlation between longitudinal processes into account. This is particularly useful when observed measures from different longitudinal processes are taken at different frequencies. That means, some longitudinal processes are observed less frequently compared to other longitudinal processes. By jointly modeling these processes, one can take the correlation between the processes into account, and hence, better estimate the trajectory of the processes including those less frequent ones.Our proposed joint modeling frameworks use Dirichlet process techniques. Therefore, understanding parameter estimation in these models is vital. Using synthetic longitudinal and survival data, we compare parameter estimation under DPM models as opposed to commonly used parametric techniques. We are particularly interested in evaluation of the performance of the model in parameter estimation when a population consists of sub-populations with latent features that are different across subgroups. We propose a Dirichlet process mixture survival model that is capable of detecting latent subpopulations characterized by differing baseline risks for mortality. Our proposed technique is particularly useful when interest lies in estimation of the conditional effect of covariates as opposed to estimates that are marginalized across all subpopulations.Throughout, our work is motivated by data on patients with end stage renal disease (ESRD), a condition where the kidneys are no longer capable of cleaning blood sufficiently enough to sustain life. In this context a modeling framework capable of finding mortality-related biomarkers, which are measured longitudinally over time, can significantly help physicians and practitioners to lower mortality among these patients.
Book Synopsis Bayesian Psychometric Modeling by : Roy Levy
Download or read book Bayesian Psychometric Modeling written by Roy Levy and published by CRC Press. This book was released on 2017-07-28 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Single Cohesive Framework of Tools and Procedures for Psychometrics and Assessment Bayesian Psychometric Modeling presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid in unifying seemingly disparate—and sometimes conflicting—ideas and activities in psychometrics. This book explains both how to perform psychometrics using Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking. The first part of the book introduces foundational principles and statistical models, including conceptual issues, normal distribution models, Markov chain Monte Carlo estimation, and regression. Focusing more directly on psychometrics, the second part covers popular psychometric models, including classical test theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples primarily from educational assessments. A supplementary website provides the datasets, WinBUGS code, R code, and Netica files used in the examples.
Book Synopsis Missing Data in Longitudinal Studies by : Michael J. Daniels
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
Book Synopsis Bayesian Nonparametric and Semiparametric Modeling Using Dirichlet Process Mixing by : Athanasios Kottas
Download or read book Bayesian Nonparametric and Semiparametric Modeling Using Dirichlet Process Mixing written by Athanasios Kottas and published by . This book was released on 2000 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Modeling in Bioinformatics by : Dipak K. Dey
Download or read book Bayesian Modeling in Bioinformatics written by Dipak K. Dey and published by CRC Press. This book was released on 2010-09-03 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and c
Book Synopsis Bayesian Semiparametric Joint Modeling of Longitudinal Predictors and Discrete Outcomes by : Woobeen Lim
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.
Book Synopsis Parametric and Semiparametric Models for Longitudinal Data by : Ann Michelle Norris
Download or read book Parametric and Semiparametric Models for Longitudinal Data written by Ann Michelle Norris and published by . This book was released on 2008 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Semiparametric Models for Discrete Longitudinal Data by : Sylvie Tchumtchoua
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:
Book Synopsis Bayesian Inference for Probabilistic Risk Assessment by : Dana Kelly
Download or read book Bayesian Inference for Probabilistic Risk Assessment written by Dana Kelly and published by Springer Science & Business Media. This book was released on 2011-08-30 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems. Inference in the book employs a modern computational approach known as Markov chain Monte Carlo (MCMC). The MCMC approach may be implemented using custom-written routines or existing general purpose commercial or open-source software. This book uses an open-source program called OpenBUGS (commonly referred to as WinBUGS) to solve the inference problems that are described. A powerful feature of OpenBUGS is its automatic selection of an appropriate MCMC sampling scheme for a given problem. The authors provide analysis “building blocks” that can be modified, combined, or used as-is to solve a variety of challenging problems. The MCMC approach used is implemented via textual scripts similar to a macro-type programming language. Accompanying most scripts is a graphical Bayesian network illustrating the elements of the script and the overall inference problem being solved. Bayesian Inference for Probabilistic Risk Assessment also covers the important topics of MCMC convergence and Bayesian model checking. Bayesian Inference for Probabilistic Risk Assessment is aimed at scientists and engineers who perform or review risk analyses. It provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models.
Book Synopsis Bayesian Semiparametric Inference for Longitudinal Data with Applications by : Silvia Mongelluzzo
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:
Book Synopsis Bayesian Semiparametric Models for Heterogeneous Cross-platform Differential Gene Expression by : Soma Sekhar Dhavala
Download or read book Bayesian Semiparametric Models for Heterogeneous Cross-platform Differential Gene Expression written by Soma Sekhar Dhavala and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We are concerned with testing for differential expression and consider three different aspects of such testing procedures. First, we develop an exact ANOVA type model for discrete gene expression data, produced by technologies such as a Massively Parallel Signature Sequencing (MPSS), Serial Analysis of Gene Expression (SAGE) or other next generation sequencing technologies. We adopt two Bayesian hierarchical models -- one parametric and the other semiparametric with a Dirichlet process prior that has the ability to borrow strength across related signatures, where a signature is a specific arrangement of the nucleotides. We utilize the discreteness of the Dirichlet process prior to cluster signatures that exhibit similar differential expression profiles. Tests for differential expression are carried out using non-parametric approaches, while controlling the false discovery rate. Next, we consider ways to combine expression data from different studies, possibly produced by different technologies resulting in mixed type responses, such as Microarrays and MPSS. Depending on the technology, the expression data can be continuous or discrete and can have different technology dependent noise characteristics. Adding to the difficulty, genes can have an arbitrary correlation structure both within and across studies. Performing several hypothesis tests for differential expression could also lead to false discoveries. We propose to address all the above challenges using a Hierarchical Dirichlet process with a spike-and-slab base prior on the random effects, while smoothing splines model the unknown link functions that map different technology dependent manifestations to latent processes upon which inference is based. Finally, we propose an algorithm for controlling different error measures in a Bayesian multiple testing under generic loss functions, including the widely used uniform loss function. We do not make any specific assumptions about the underlying probability model but require that indicator variables for the individual hypotheses are available as a component of the inference. Given this information, we recast multiple hypothesis testing as a combinatorial optimization problem and in particular, the 0-1 knapsack problem which can be solved efficiently using a variety of algorithms, both approximate and exact in nature.
Book Synopsis Semiparametric Bayesian Inference in Autoregressive Panel Data Models by : Keisuke Hirano
Download or read book Semiparametric Bayesian Inference in Autoregressive Panel Data Models written by Keisuke Hirano and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper develops semiparametric Bayesian methods for inference in dynamic linear panel data models, and applies them to longitudinal data on labor earnings from the Panel Study of Income Dynamics. We focus on characterizing not only parameters related to conditional means and variances, but the entire joint distribution of earnings. Full distributional inference in semiparametric panel data models must solve a nonparametric deconvolution problem arising from the existence of unobserved additive individual-specific terms. A computational Bayesian approach based on variable augmentation can deal with such latent-variable models effectively, and can allow considerable flexibility in distributional assumptions. We use relatively simple, low-order linear models with individual heterogeneity, but estimate the distribution of the disturbances without requiring that it belong to a restrictive parametric class, such as the class of normal distributions. A prior distribution on the space of probability distributions is used to impose smoothness on an otherwise general specification for the disturbance terms. This nonparametric prior is constructed using a countable mixture of normals representation, where the mixing distribution is given a Dirichlet process prior.
Book Synopsis Joint Models for Longitudinal and Time-to-Event Data by : Dimitris Rizopoulos
Download or read book Joint Models for Longitudinal and Time-to-Event Data written by Dimitris Rizopoulos and published by CRC Press. This book was released on 2012-06-22 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author. All the R code used in the book is available at: http://jmr.r-forge.r-project.org/
Book Synopsis Semiparametric Bayesian Model for Response Time Distribution Evaluation by : Yiyang Chen
Download or read book Semiparametric Bayesian Model for Response Time Distribution Evaluation written by Yiyang Chen and published by . This book was released on 2018 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt: Houpt et al. (2016) developed a semiparametric Bayesian model to estimate the hazard distributions of response time data. In this thesis, I update the semiparametric model with a more flexible structure which fits better to RT data with respect to MSE and WAIC criteria. I apply the updated model to published data sets (Schwarz, 2001; Palmer et al., 2011) to evaluate the plausibility of some commonly used parametric RT distribution proposals, including ex-Gaussian, Weibull, Gamma, Log-normal, Wald and ex-Wald distributions. Results show a considerable amount of inconsistency between the estimated hazard distributions from the semiparametric Bayesian model and parametric models, indicating that these parametric distribution proposals may not be flexible enough to characterize the traits of RT data. I also use the updated model to obtain a detailed hazard distribution estimation of RT data from Lappin et al. (2016), which has proposed a hazard function model but lacks satisfying hazard estimation tools. The updated semiparametric model is able to capture some critical hazard distribution traits which provide evidence to evaluate the underlying hypotheses of Lappin et al. (2016)'s model.
