Upadhyay / Singh / Dey Current Trends in Bayesian Methodology with Applications

1. Auflage 2015
ISBN: 978-1-4822-3512-8
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 680 Seiten

Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.

Each chapter is self-contained and focuses on a Bayesian methodology. It gives an overview of the area, presents theoretical insights, and emphasizes applications through motivating examples.

This book reflects the diversity of Bayesian analysis, from novel Bayesian methodology, such as nonignorable response and factor analysis, to state-of-the-art applications in economics, astrophysics, biomedicine, oceanography, and other areas. It guides readers in using Bayesian techniques for a range of statistical analyses.

Autoren/Hrsg.

Upadhyay, Satyanshu K.

Singh, Umesh

Dey, Dipak K.

Loganathan, Appaia

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Bayesian Inference on the Brain John A.D. Aston and Adam M. Johansen

Forecasting Indian Macroeconomic Variables Using Medium-Scale VAR Models Goodness C. Aye, Pami Dua, and Rangan Gupta

Comparing Proportions: A Modern Solution to a Classical Problem José M. Bernardo

Hamiltonian Monte Carlo for Hierarchical Models Michael Betancourt and Mark Girolami

On Bayesian Spatio-Temporal Modeling of Oceanographic Climate Characteristics Madhuchhanda Bhattacharjee and Snigdhansu Chatterjee

Sequential Bayesian Inference for Dynamic State Space Model Parameters Arnab Bhattacharya and Simon Wilson

Bayesian Active Contours with Affine-Invariant Elastic Shape Prior Darshan Bryner and Anuj Srivastava

Bayesian Semiparametric Longitudinal Data Modeling Using NI Densities Luis M. Castro, Victor H. Lachos, Diana M. Galvis, and Dipankar Bandyopadhyay

Bayesian Factor Analysis Based on Concentration Yun Cao, Michael Evans, and Irwin Guttman

Regional Fertility Data Analysis: A Small Area Bayesian Approach Eduardo A. Castro, Zhen Zhang, Arnab Bhattacharjee, José M. Martins, and Tapabrata Maiti

In Search of Optimal Objective Priors for Model Selection and Estimation Jyotishka Datta and Jayanta K. Ghosh

Bayesian Variable Selection for Predictively Optimal Regression Tanujit Dey and Ernest Fokoué

Scalable Subspace Clustering with Application to Motion Segmentation Liangjing Ding and Adrian Barbu

Bayesian Inference for Logistic Regression Models Using Sequential Posterior Simulation John Geweke, Garland Durham, and Huaxin Xu

From Risk Analysis to Adversarial Risk Analysis David Ríos Insua, Javier Cano, Michael Pellot, and Ricardo Ortega

Symmetric Power Link with Ordinal Response Model Xun Jiang and Dipak K. Dey

Elastic Prior Shape Models of 3D Objects for Bayesian Image Analysis Sebastian Kurtek and Qian Xie

Multi-State Models for Disease Natural History Amy E. Laird, Rebecca A. Hubbard, and Lurdes Y.T. Inoue

Priors on Hypergraphical Models via Simplicial Complexes Simón Lunagómez, Sayan Mukherjee, and Robert Wolpert

A Bayesian Uncertainty Analysis for Nonignorable Nonresponse Balgobin Nandram and Namkyo Woo

Stochastic Volatility and Realized Stochastic Volatility Models Yasuhiro Omori and Toshiaki Watanabe

Monte Carlo Methods and Zero Variance Principle Theodore Papamarkou, Antonietta Mira, and Mark Girolami

A Flexible Class of Reduced Rank Spatial Models for Large Non-Gaussian Dataset Rajib Paul, Casey M. Jelsema, and Kwok Wai Lau

A Bayesian Reweighting Technique for Small Area Estimation Azizur Rahman and Satyanshu K. Upadhyay

Empirical Bayes Methods for the Transformed Gaussian Random Field Model with Additive Measurement Errors Vivekananda Roy, Evangelos Evangelou, and Zhengyuan Zhu

Mixture Kalman Filters and Beyond Saikat Saha, Gustaf Hendeby, and Fredrik Gustafsson

Some Aspects of Bayesian Inference in Skewed Mixed Logistic Regression Models Cristiano C. Santos and Rosangela H. Loschi

A Bayesian Analysis of the Solar Cycle Using Multiple Proxy Variables David C. Stenning, David A. van Dyk, Yaming Yu, and Vinay Kashyap

Fuzzy Information, Likelihood, Bayes’ Theorem, and Engineering Application Reinhard Viertl and Owat Sunanta

Bayesian Parallel Computation for Intractable Likelihood Using Griddy-Gibbs Sampler Nuttanan Wichitaksorn and S.T. Boris Choy

Index

Über Autor(innen)

Satyanshu K. Upadhyay is a professor and head of the Department of Statistics at Banaras Hindu University.

Umesh Singh is a professor in the Department of Statistics at Banaras Hindu University.

Dipak K. Dey is a distinguished professor in the Department of Statistics at the University of Connecticut.

Appaia Loganathan is a professor in the Department of Statistics at Manonmaniam Sundaranar University.