Buch, Englisch, 288 Seiten, Format (B × H): 158 mm x 238 mm, Gewicht: 574 g
Buch, Englisch, 288 Seiten, Format (B × H): 158 mm x 238 mm, Gewicht: 574 g
Reihe: Chapman & Hall/CRC Texts in Statistical Science
ISBN: 978-0-8153-7864-8
Verlag: CRC Press
In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics:
- Advice on selecting prior distributions
- Computational methods including Markov chain Monte Carlo (MCMC)
- Model-comparison and goodness-of-fit measures, including sensitivity to priors
- Frequentist properties of Bayesian methods
Case studies covering advanced topics illustrate the flexibility of the Bayesian approach:
- Semiparametric regression
- Handling of missing data using predictive distributions
- Priors for high-dimensional regression models
- Computational techniques for large datasets
- Spatial data analysis
The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book’s website.
Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award.
Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.
Zielgruppe
Postgraduate
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
Weitere Infos & Material
1. Introduction to Bayesian Inferential Framework. 2. Prior Knowledge to Posterior Inference. 3. Computational Methods. 4. Linear and Generalized Linear Regression Methods. 5. Models for Large Dimensional Parameters. 6. Models for Dependent Data. 7. Models for Data with Irregularities. 8. Models for Infinite Dimensional Parameters. 9. Advanced Computational Methods. 10. Case Studies Using Advanced Bayesian Methods
The code and data is at https://bayessm.wordpress.ncsu.edu/.