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Author : Silvia Mongelluzzo
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (955 download)
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:
Author : Jingjing Fan (Ph. D.)
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)
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
Author : Young-Ku Choi
Publisher :
ISBN 13 :
Total Pages : 300 pages
Book Rating : 4.:/5 (7 download)
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:
Author : Sylvie Tchumtchoua
Publisher :
ISBN 13 :
Total Pages : 226 pages
Book Rating : 4.:/5 (78 download)
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:
Author : Ann Michelle Norris
Publisher :
ISBN 13 :
Total Pages : 306 pages
Book Rating : 4.:/5 (79 download)
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:
Author : Giorgio Paulon
Publisher :
ISBN 13 :
Total Pages : 392 pages
Book Rating : 4.:/5 (126 download)
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
Author : Michael J. Daniels
Publisher : CRC Press
ISBN 13 : 1420011189
Total Pages : 324 pages
Book Rating : 4.4/5 (2 download)
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
Author : Li Su
Publisher :
ISBN 13 :
Total Pages : 280 pages
Book Rating : 4.:/5 (247 download)
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:
Author : Niansheng Tang
Publisher : BoD – Books on Demand
ISBN 13 : 1838803858
Total Pages : 120 pages
Book Rating : 4.8/5 (388 download)
Download or read book Bayesian Inference on Complicated Data written by Niansheng Tang and published by BoD – Books on Demand. This book was released on 2020-07-15 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to great applications in various fields, such as social science, biomedicine, genomics, and signal processing, and the improvement of computing ability, Bayesian inference has made substantial developments for analyzing complicated data. This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. It is structured around topics on the impact of the choice of the prior on Bayesian statistics, some advances on Bayesian sampling methods, and Bayesian inference for complicated data including breast cancer data, cloud-based healthcare data, gene network data, and longitudinal data. This volume is designed for statisticians, engineers, doctors, and machine learning researchers.
Author : Roberto Carta
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (144 download)
Download or read book Bayesian Semiparametric Inference for Statistical Models Using Mixtures written by Roberto Carta and published by . This book was released on 2004 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Gary Chamberlain
Publisher :
ISBN 13 :
Total Pages : 53 pages
Book Rating : 4.:/5 (246 download)
Download or read book Semiparametric Applications of Bayesian Inference written by Gary Chamberlain and published by . This book was released on 1995 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Ludwig Fahrmeir
Publisher : OUP Oxford
ISBN 13 : 9780199533022
Total Pages : 0 pages
Book Rating : 4.5/5 (33 download)
Download or read book Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data written by Ludwig Fahrmeir and published by OUP Oxford. This book was released on 2011-04-28 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bringing together recent advances in smoothing and semiparametric regression from a Bayesian perspective, this book demonstrates, with worked examples, the application of these statistical methods to a variety of fields including forestry, development economics, medicine and marketing.
Author : Jianqing Fan
Publisher : World Scientific
ISBN 13 : 1908979763
Total Pages : 552 pages
Book Rating : 4.9/5 (89 download)
Download or read book Frontiers In Statistics written by Jianqing Fan and published by World Scientific. This book was released on 2006-07-17 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last two decades, many areas of statistical inference have experienced phenomenal growth. This book presents a timely analysis and overview of some of these new developments and a contemporary outlook on the various frontiers of statistics.Eminent leaders in the field have contributed 16 review articles and 6 research articles covering areas including semi-parametric models, data analytical nonparametric methods, statistical learning, network tomography, longitudinal data analysis, financial econometrics, time series, bootstrap and other re-sampling methodologies, statistical computing, generalized nonlinear regression and mixed effects models, martingale transform tests for model diagnostics, robust multivariate analysis, single index models and wavelets.This volume is dedicated to Prof. Peter J Bickel in honor of his 65th birthday. The first article of this volume summarizes some of Prof. Bickel's distinguished contributions.
Author : Keisuke Hirano
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
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.
Author : Christopher K. Carter
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Semiparametric Bayesian Inference for Time Series with Mixed Spectra written by Christopher K. Carter and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Bayesian analysis is presented of a time series which is the sum of a stationary component with a smooth spectral density and a deterministic component consisting of a linear combination of a trend and periodic terms. The periodic terms may have known or unknown frequencies. The advantage of our approach is that different features of the data such as the regression parameters, the spectral density, unknown frequencies, and missing observations are combined in a hierarchical Bayesian framework and estimated simultaneously. A Bayesian test to detect the presence of deterministic components in the data is also constructed. By using an asymptotic approximation to the likelihood, the computation is carried out efficiently using Markov chain Monte Carlo in O(Mn) operations, where n is the sample size and M and is the number of iterations. We show empirically that our approach works well on real and simulated examples.
Author : Dimitris Rizopoulos
Publisher : CRC Press
ISBN 13 : 1439872864
Total Pages : 279 pages
Book Rating : 4.4/5 (398 download)
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/
Author : Geert Molenberghs
Publisher : CRC Press
ISBN 13 : 1439854610
Total Pages : 600 pages
Book Rating : 4.4/5 (398 download)
Download or read book Handbook of Missing Data Methodology written by Geert Molenberghs and published by CRC Press. This book was released on 2014-11-06 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research. Divided into six parts, the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods. The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters. Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.
