00000nam a2200000 i 4500 UP-1685675941131626916 Buklod 20240220123345.0 m |o d | cr ||||||||||| 231212s2020 enk a b 001 0 eng d 2019943770 BB9G9996 bnb 019574895 Uk 780128158630 (ePub ebook) 12815862X (pbk.) 780128158623 (pbk.) 9780128158623 paperback 21019585 KMGB eng UKMGB rda OCLCO OCLCF YDX FIE SFB BDX DLC eng ccopycat QA278.2 .F584 2020 519.536 23 Flexible Bayesian regression modelling edited by Yanan Fan, David Nott, Michael S. Smith, Jean-Luc Dortet-Bernadet. London, United Kingdom San Diego, CA, United States Academic Press [2020] iv, 288 pages 23 cm txt rdacontent rdamedia nc rdacarrier ncludes bibliographical references and index. lexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity, and large sample sizes, necessitating the need for considerable flexibility in modeling techniques. It reviews three forms of flexibility: methods which provide flexibility in their error distribution; methods which model non-central parts of the distribution (such as quantile regression); and finally models that allow the mean function to be flexible (such as spline models). Each chapter discusses the key aspects of fitting a regression model. R programs accompany the methods. This book is particularly relevant to non-specialist practitioners with intermediate mathematical training seeking to apply Bayesian approaches in economics, biology, finance, engineering and medicine.-- Provided by publisher. Bayesian statistical decision theory. Regression analysis Mathematical models. Bayesian statistical decision theory. fast (OCoLC)fst00829019. Regression analysis Mathematical models. fast (OCoLC)fst01093277. Dortet-Bernadet, Jean-Luc editor. Nott, David editor. Smith, Michael S. editor. Fan, Y. (Yanan) editor. ebook version 9780128158630. FO UPD DSTC QA 278.2 F584 2020 Book