Generalized random utility models with multiple types

Hossein Azari Soufiani; Hansheng Diao; Zhenyu Lai; David C. Parkes

Generalized random utility models with multiple types

Hossein Azari Soufiani, Hansheng Diao, Zhenyu Lai, David C. Parkes

Mathematics

Research output: Contribution to journal › Conference article › peer-review

12 Scopus citations

Abstract

We propose a model for demand estimation in multi-agent, differentiated product settings and present an estimation algorithm that uses reversible jump MCMC techniques to classify agents' types. Our model extends the popular setup in Berry, Levinsohn and Pakes (1995) to allow for the data-driven classification of agents' types using agent-level data. We focus on applications involving data on agents' ranking over alternatives, and present theoretical conditions that establish the identifiability of the model and uni-modality of the likelihood/posterior. Results on both real and simulated data provide support for the scalability of our approach.

Original language	English (US)
Journal	Advances in Neural Information Processing Systems
State	Published - 2013
Event	27th Annual Conference on Neural Information Processing Systems, NIPS 2013 - Lake Tahoe, NV, United States Duration: Dec 5 2013 → Dec 10 2013

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

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title = "Generalized random utility models with multiple types",

abstract = "We propose a model for demand estimation in multi-agent, differentiated product settings and present an estimation algorithm that uses reversible jump MCMC techniques to classify agents' types. Our model extends the popular setup in Berry, Levinsohn and Pakes (1995) to allow for the data-driven classification of agents' types using agent-level data. We focus on applications involving data on agents' ranking over alternatives, and present theoretical conditions that establish the identifiability of the model and uni-modality of the likelihood/posterior. Results on both real and simulated data provide support for the scalability of our approach.",

author = "Soufiani, {Hossein Azari} and Hansheng Diao and Zhenyu Lai and Parkes, {David C.}",

year = "2013",

language = "English (US)",

journal = "Advances in Neural Information Processing Systems",

issn = "1049-5258",

publisher = "Neural information processing systems foundation",

note = "27th Annual Conference on Neural Information Processing Systems, NIPS 2013 ; Conference date: 05-12-2013 Through 10-12-2013",

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TY - JOUR

T1 - Generalized random utility models with multiple types

AU - Soufiani, Hossein Azari

AU - Diao, Hansheng

AU - Lai, Zhenyu

AU - Parkes, David C.

PY - 2013

Y1 - 2013

N2 - We propose a model for demand estimation in multi-agent, differentiated product settings and present an estimation algorithm that uses reversible jump MCMC techniques to classify agents' types. Our model extends the popular setup in Berry, Levinsohn and Pakes (1995) to allow for the data-driven classification of agents' types using agent-level data. We focus on applications involving data on agents' ranking over alternatives, and present theoretical conditions that establish the identifiability of the model and uni-modality of the likelihood/posterior. Results on both real and simulated data provide support for the scalability of our approach.

AB - We propose a model for demand estimation in multi-agent, differentiated product settings and present an estimation algorithm that uses reversible jump MCMC techniques to classify agents' types. Our model extends the popular setup in Berry, Levinsohn and Pakes (1995) to allow for the data-driven classification of agents' types using agent-level data. We focus on applications involving data on agents' ranking over alternatives, and present theoretical conditions that establish the identifiability of the model and uni-modality of the likelihood/posterior. Results on both real and simulated data provide support for the scalability of our approach.

UR - http://www.scopus.com/inward/record.url?scp=84899013085&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84899013085&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84899013085

SN - 1049-5258

JO - Advances in Neural Information Processing Systems

JF - Advances in Neural Information Processing Systems

T2 - 27th Annual Conference on Neural Information Processing Systems, NIPS 2013

Y2 - 5 December 2013 through 10 December 2013

ER -

Generalized random utility models with multiple types

Abstract

All Science Journal Classification (ASJC) codes

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