000728359 000__ 04846cam\a2200469Ii\4500
000728359 001__ 728359
000728359 005__ 20230306141008.0
000728359 006__ m\\\\\o\\d\\\\\\\\
000728359 007__ cr\cn\nnnunnun
000728359 008__ 150728s2015\\\\sz\a\\\\o\\\\\001\0\eng\d
000728359 020__ $$a9783319195186$$qelectronic book
000728359 020__ $$a3319195182$$qelectronic book
000728359 020__ $$z9783319195179
000728359 035__ $$aSP(OCoLC)ocn914706235
000728359 035__ $$aSP(OCoLC)914706235
000728359 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dGW5XE$$dIDEBK$$dN$T$$dAZU$$dYDXCP
000728359 049__ $$aISEA
000728359 050_4 $$aQH323.5
000728359 08204 $$a570.1/5195$$223
000728359 24500 $$aNonparametric Bayesian inference in biostatistics$$h[electronic resource] /$$cRiten Mitra, Peter Müller, editors.
000728359 264_1 $$aCham :$$bSpringer,$$c[2015]
000728359 264_4 $$c©2015
000728359 300__ $$a1 online resource (xvii, 448 pages) :$$bcolor illustrations.
000728359 336__ $$atext$$btxt$$2rdacontent
000728359 337__ $$acomputer$$bc$$2rdamedia
000728359 338__ $$aonline resource$$bcr$$2rdacarrier
000728359 4901_ $$aFrontiers in probability and the statistical sciences
000728359 500__ $$aIncludes index.
000728359 5050_ $$aPart 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.
000728359 506__ $$aAccess limited to authorized users.
000728359 520__ $$aAs 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.
000728359 588__ $$aOnline resource; title from PDF title page (viewed July 31, 2015)
000728359 650_0 $$aBiometry.
000728359 650_0 $$aNonparametric statistics.
000728359 650_0 $$aBayesian statistical decision theory.
000728359 7001_ $$aMitra, Riten,$$eeditor.
000728359 7001_ $$aMüller, Peter,$$d1963 August 9-$$eeditor.
000728359 830_0 $$aFrontiers in probability and the statistical sciences.
000728359 852__ $$bebk
000728359 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-19518-6$$zOnline Access$$91397441.1
000728359 909CO $$ooai:library.usi.edu:728359$$pGLOBAL_SET
000728359 980__ $$aEBOOK
000728359 980__ $$aBIB
000728359 982__ $$aEbook
000728359 983__ $$aOnline
000728359 994__ $$a92$$bISE