<電子ブック>
Nonparametric Bayesian Inference in Biostatistics / edited by Riten Mitra, Peter Müller
(Frontiers in Probability and the Statistical Sciences. ISSN:26249995)
版 | 1st ed. 2015. |
---|---|
出版者 | Cham : Springer International Publishing : Imprint: Springer |
出版年 | 2015 |
本文言語 | 英語 |
大きさ | XVII, 448 p. 96 illus., 47 illus. in color : online resource |
著者標目 | Mitra, Riten editor Müller, Peter editor SpringerLink (Online service) |
件 名 | LCSH:Biometry LCSH:Statistics FREE:Biostatistics FREE:Statistical Theory and Methods |
一般注記 | 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 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 HTTP:URL=https://doi.org/10.1007/978-3-319-19518-6 |
目次/あらすじ
所蔵情報を非表示
電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
電子ブック | オンライン | 電子ブック |
|
|
Springer eBooks | 9783319195186 |
|
電子リソース |
|
EB00238841 |
類似資料
この資料の利用統計
このページへのアクセス回数:5回
※2017年9月4日以降