Estimating Disaggregate Models Using Aggregate Data Through Augmentation of individual Choice (Record no. 29447)
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000 -LEADER | |
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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 |
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 |
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Main Library | Main Library | 04/06/2008 | 0.00 | Che | AR9567 | 23/09/2014 | 0.00 | 23/09/2014 | Articles |