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Author: An Kang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: An Kang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: J.K. Ghosh
Publisher: Springer Science & Business Media
ISBN: 0387226540
Category : Mathematics
Languages : en
Pages : 311
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Book Description
This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
Author: Sara Kathryn Wade
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Chao Chang (Mathematician)
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 140
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Book Description
We propose new nonparametric Bayesian approaches to quantile regression using Dirichlet process mixture (DPM) models. All the existing quantile regression methods based on DPMs require the kernel density to satisfy the quantile constraint, hence the kernel densities are themselves usually in the form of mixtures. One innovation of our approaches is that we impose no constraint on the kernel, thus a wide range of densities can be chosen as the kernels of the DPM model. The quantile constraint is satisfied by a post-processing of the DPM by a suitable location shift. As a result, our proposed models use simpler kernels and yet possess great flexibility by mixing over both the location parameter and the scale parameter. The posterior consistency of our proposed model is studied carefully. And Markov chain Monte Carlo algorithms are provided for posterior inference. The performance of our approaches is evaluated using simulated data and real data. Moreover, we are able to incorporate random effects into our models such that our approaches can be extended to handle longitudinal data.
Author: Peter Müller
Publisher: Springer
ISBN: 3319189689
Category : Mathematics
Languages : en
Pages : 203
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Book Description
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.
Author: Athanasios Kottas
Publisher:
ISBN:
Category :
Languages : en
Pages : 294
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Book Description
Author: David R. Hunter
Publisher: World Scientific
ISBN: 9814340553
Category : Mathematics
Languages : en
Pages : 370
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Book Description
This festschrift includes papers authored by many collaborators, colleagues, and students of Professor Thomas P Hettmansperger, who worked in research in nonparametric statistics, rank statistics, robustness, and mixture models during a career that spanned nearly 40 years. It is a broad sample of peer-reviewed, cutting-edge research related to nonparametrics and mixture models.
Author: Abel Rodriguez
Publisher: LAP Lambert Academic Publishing
ISBN: 9783838300122
Category : Bayesian statistical decision theory
Languages : en
Pages : 168
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Book Description
Bayesian nonparametric and semiparametric mixture models have become extremely popular in the last 10 years because they provide flexibility and interpretability while preserving computational simplicity. This book is a contribution to this growing literature, discussing the design of models for collections of distributions and their application to density estimation and nonparametric regression. All methods introduced in this book are discussed in the context of complex scientific applications in public health, epidemiology and finance.
Author: Tatiana V Tatarinova
Publisher: World Scientific
ISBN: 1783266279
Category : Mathematics
Languages : en
Pages : 296
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Book Description
This book, written by two mathematicians from the University of Southern California, provides a broad introduction to the important subject of nonlinear mixture models from a Bayesian perspective. It contains background material, a brief description of Markov chain theory, as well as novel algorithms and their applications. It is self-contained and unified in presentation, which makes it ideal for use as an advanced textbook by graduate students and as a reference for independent researchers. The explanations in the book are detailed enough to capture the interest of the curious reader, and complete enough to provide the necessary background material needed to go further into the subject and explore the research literature.In this book the authors present Bayesian methods of analysis for nonlinear, hierarchical mixture models, with a finite, but possibly unknown, number of components. These methods are then applied to various problems including population pharmacokinetics and gene expression analysis. In population pharmacokinetics, the nonlinear mixture model, based on previous clinical data, becomes the prior distribution for individual therapy. For gene expression data, one application included in the book is to determine which genes should be associated with the same component of the mixture (also known as a clustering problem). The book also contains examples of computer programs written in BUGS. This is the first book of its kind to cover many of the topics in this field.
Author: Yunju Im (PhD)
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 0
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Book Description
Heterogeneity occurs in many regression problems, where members from different latent subgroups respond differently to the covariates of interest (e.g., treatments) even after adjusting for other covariates. Our work adopts a Bayesian model called the mixture of finite mixtures (MFM) to identify these subgroups. A key feature of this model is that the number of subgroups needs not to be known a priori, and is modeled as a random variable. The Bayesian MFM model was not commonly used in earlier applications largely due to computational difficulties. In comparison, an alternative infinite mixture model called the Dirichlet Process Mixture (DPM) model has been a main Bayesian tool for clustering even though it is a mis-specified model for many applications. The popularity of DPM is partly due to its convenient mathematical properties that enable efficient computing algorithms.
