Nonparametric Bayesian Inference in Biostatistics / edited by Riten Mitra, Peter Müller.

Format:

Book

Contributor:

Mitra, Riten., Editor.

Müller, Peter., Editor.

Series:

Frontiers in Probability and the Statistical Sciences, 2624-9995

Language:

English

Subjects (All):

Biometry.

Statistics.

Biostatistics.

Statistical Theory and Methods.

Local Subjects:

Biostatistics.

Statistical Theory and Methods.

Physical Description:

1 online resource (448 p.)

Edition:

1st ed. 2015.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2015.

Language Note:

English

Summary:

As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve. Riten Mitra is Assistant Professor in the Department of Bioinformatics and Biostatistics at University of Louisville. His research interests include Bayesian graphical models and nonparametric Bayesian methods with a special emphasis on applications in genomics and bioinformatics. Peter Mueller is Professor in the Department of Mathematics and the Department of Statistics & Data Science at the University of Texas at Austin. He has published widely on nonparametric Bayesian statistics, with an emphasis on applications in biostatistics and bioinformatics.

Contents:

Part I Introduction

Bayesian Nonparametric Models

Bayesian Nonparametric Biostatistics

Part II Genomics and Proteomics

Bayesian Shape Clustering

Estimating Latent Cell Subpopulations with Bayesian Feature Allocation Models

Species Sampling Priors for Modeling Dependence: An Application to the Detection of Chromosomal Aberrations

Modeling the Association Between Clusters of SNPs and Disease Responses

Bayesian Inference on Population Structure: from Parametric to Nonparametric Modeling

Bayesian Approaches for Large Biological Networks

Nonparametric Variable Selection, Clustering and Prediction for Large Biological Datasets

Part III Survival Analysis

Markov Processes in Survival Analysis

Bayesian Spatial Survival Models

Fully Nonparametric Regression Modelling of Misclassified Censored Time-to-Event Data

Part IV Random Functions and Response Surfaces

Neuronal Spike Train Analysis Using Gaussian Process Models

Bayesian Analysis of Curves Shape Variation through Registration and Regression

Biomarker-Driven Adaptive Design

Bayesian Nonparametric Approaches for ROC Curve Inference

Part V Spatial Data

Spatial Bayesian Nonparametric Methods

Spatial Species Sampling and Product Partition Models

Spatial Boundary Detection for Areal Counts

A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs

Bayesian Nonparametrics for Missing Data in Longitudinal Clinical Trials.

Notes:

Description based upon print version of record.

Includes bibliographical references at the end of each chapters and index.

ISBN:

3-319-19518-2