Welcome to P K Kelkar Library, Online Public Access Catalogue (OPAC)

Bayesian non- and semi-parametric methods and applications (Record no. 567369)

MARC details
000 -LEADER
fixed length control field 02493 a2200253 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250224164542.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250218b xxu||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9780691145327
040 ## - CATALOGING SOURCE
Transcribing agency IIT Kanpur
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 330.01519542
Item number R735b
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Rossi, Peter E.
245 ## - TITLE STATEMENT
Title Bayesian non- and semi-parametric methods and applications
Statement of responsibility, etc Peter E. Rossi
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher Princeton University Press
Year of publication 2014
Place of publication Princeton
300 ## - PHYSICAL DESCRIPTION
Number of Pages xiii, 202p
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title The econometric and tinbergen institutes lectures
490 ## - SERIES STATEMENT
Series statement / edited by Herman K. van Dijk and Philip Hans Franses
520 ## - SUMMARY, ETC.
Summary, etc This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number of normal components in the mixture or an infinite number bounded only by the sample size. By using flexible distributional approximations instead of fixed parametric models, the Bayesian approach can reap the advantages of an efficient method that models all of the structure in the data while retaining desirable smoothing properties. Non-Bayesian non-parametric methods often require additional ad hoc rules to avoid "overfitting," in which resulting density approximates are nonsmooth. With proper priors, the Bayesian approach largely avoids overfitting, while retaining flexibility. This book provides methods for assessing informative priors that require only simple data normalizations. The book also applies the mixture of the normals approximation method to a number of important models in microeconometrics and marketing, including the non-parametric and semi-parametric regression models, instrumental variables problems, and models of heterogeneity. In addition, the author has written a free online software package in R, "bayesm," which implements all of the non-parametric models discussed in the book.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Bayesian statistical decision theory
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Econometrics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Economics -- Mathematical
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Home library Current library Date acquired Source of acquisition Cost, normal purchase price Full call number Accession Number Cost, replacement price Koha item type
        General Stacks PK Kelkar Library, IIT Kanpur PK Kelkar Library, IIT Kanpur 03/03/2025 60 3485.21 330.01519542 R735b A186709 4646.95 Books

Powered by Koha