Estimating Disaggregate Models Using Aggregate Data Through Augmentation of individual Choice (Record no. 29447)

MARC details
000 -LEADER
fixed length control field 01808pab a2200205 454500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 140923b0 xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency Welingkar Institute of Management Development & Research, Mumbai
Original cataloging agency Welingkar Institute of Management Development & Research, Mumbai
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title ENG
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number
Item number Che
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Chen Yuxin
245 ## - TITLE STATEMENT
Title Estimating Disaggregate Models Using Aggregate Data Through Augmentation of individual Choice
250 ## - EDITION STATEMENT
Edition statement 4
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc.
Name of publisher, distributor, etc. Nov 2007
Date of publication, distribution, etc. 0
300 ## - PHYSICAL DESCRIPTION
Extent 613-621 Pp.
490 ## - SERIES STATEMENT
Volume/sequential designation XLIV
520 ## - SUMMARY, ETC.
Summary, etc. In this article, the authors propose a Bayesian method for estimating disaggregate choice models using aggregate data. Compared with existing methods, the advantage of the proposed method is that it allows for the analysis of microlevel consumer dynamic behavior, such as the impact of purchase history on current brand choice, when only aggregate-level data are available. The essence of this approach is to simulate latent choice data that are consistent with the observed aggregate data. When the augmented choice data are made available in each iteration of the Markov chain Monte Carlo algorithm, the dynamics of consumer buying behavior can be explicitly modeled. The authors first demonstrate the validity of the method with a series of simulations and then apply the method to an actual store-level data set of consumer purchases of refrigerated orange juice. The authors find a significant amount of dynamics in consumer buying behavior. The proposed method is useful for managers to understand better the consumer purchase dynamics and brand price competition when they have access to aggregate data only.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Purchase Behavior, Bayesian Method,
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://192.168.6.13/libsuite/mm_files/Articles/AR9567.pdf">http://192.168.6.13/libsuite/mm_files/Articles/AR9567.pdf</a>
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a 28318
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Date acquired Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Cost, replacement price Price effective from Koha item type
        Main Library Main Library 04/06/2008 0.00   Che AR9567 23/09/2014 0.00 23/09/2014 Articles

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