Nonparametric Bayesian Inference in Biostatistics

  • Riten Mitra
  • Peter Müller

Part of the Frontiers in Probability and the Statistical Sciences book series (FROPROSTAS)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Peter Müller, Riten Mitra
      Pages 3-13
    3. Wesley O. Johnson, Miguel de Carvalho
      Pages 15-54
  3. Genomics and Proteomics

    1. Front Matter
      Pages 55-55
    2. Zhengwu Zhang, Debdeep Pati, Anuj Srivastava
      Pages 57-75
    3. Yuan Ji, Subhajit Sengupta, Juhee Lee, Peter Müller, Kamalakar Gulukota
      Pages 77-95
    4. Federico Bassetti, Fabrizio Leisen, Edoardo Airoldi, Michele Guindani
      Pages 97-114
    5. Raffaele Argiento, Alessandra Guglielmi, Chuhsing Kate Hsiao, Fabrizio Ruggeri, Charlotte Wang
      Pages 115-134
    6. Maria De Iorio, Stefano Favaro, Yee Whye Teh
      Pages 135-151
    7. Yang Ni, Giovanni M. Marchetti, Veerabhadran Baladandayuthapani, Francesco C. Stingo
      Pages 153-173
    8. Subharup Guha, Sayantan Banerjee, Chiyu Gu, Veerabhadran Baladandayuthapani
      Pages 175-192
  4. Survival Analysis

    1. Front Matter
      Pages 193-193
    2. Luis E. Nieto-Barajas
      Pages 195-213
    3. Haiming Zhou, Timothy Hanson
      Pages 215-246
    4. Alejandro Jara, María José García-Zattera, Arnošt Komárek
      Pages 247-267
  5. Random Functions and Response Surfaces

    1. Front Matter
      Pages 269-269
    2. Babak Shahbaba, Sam Behseta, Alexander Vandenberg-Rodes
      Pages 271-285
    3. Yanxun Xu, Yuan Ji, Peter Müller
      Pages 311-326

About this book

Introduction

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.

Keywords

Biostatistical inference Clinical sciences Genomics / proteomics Nonparametric Bayesian (BNP) approaches Survival analysis Survival regression

Editors and affiliations

  • Riten Mitra
    • 1
  • Peter Müller
    • 2
  1. 1.Department of Bioinformatics and BiostatisticsUniversity of LouisvilleLouisvilleUSA
  2. 2.Department of MathematicsUniversity of TexasAustinUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-19518-6
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-19517-9
  • Online ISBN 978-3-319-19518-6
  • About this book