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A Bayesian analysis of nested logit ...

Akincigil, Ayse.

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A Bayesian analysis of nested logit models by Markov chain Monte Carlo algorithms, with an application to the health care decision making.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Bayesian analysis of nested logit models by Markov chain Monte Carlo algorithms, with an application to the health care decision making./
Author:	Akincigil, Ayse.
Description:	94 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-12, Section: A, page: 4549.
Contained By:	Dissertation Abstracts International64-12A.
Subject:	Economics, General. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3117588

A Bayesian analysis of nested logit models by Markov chain Monte Carlo algorithms, with an application to the health care decision making.
Akincigil, Ayse.

A Bayesian analysis of nested logit models by Markov chain Monte Carlo algorithms, with an application to the health care decision making. - 94 p.

Source: Dissertation Abstracts International, Volume: 64-12, Section: A, page: 4549.

Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2004.

Nested logit models have been widely estimated by maximum likelihood estimation. A key issue is inference on the coefficient of the inclusive variable, because this coefficient provides an insight into the decision making process of the economic agent and the degree of substitution between the available choices. In theory, this coefficient needs to be within the unit interval. When this constraint is violated, the data is deemed inconsistent with random utility maximization. Constraining this coefficient within the unit interval is not a solution since, in many applications, the estimate hits one of the bounds (0 or 1) and is often unable to make inference because the constrained maximum likelihood estimation procedures do not produce standard error estimates.Subjects--Topical Terms:

1017424
Economics, General.

A Bayesian analysis of nested logit models by Markov chain Monte Carlo algorithms, with an application to the health care decision making.
LDR:02355nmm 2200289 4500 001 1864507
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035 $a (UnM)AAI3117588
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100 1 $a Akincigil, Ayse. $3 1951996
245 1 2 $a A Bayesian analysis of nested logit models by Markov chain Monte Carlo algorithms, with an application to the health care decision making.
300 $a 94 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-12, Section: A, page: 4549.
500 $a Director: Hiroki Tsurumi.
502 $a Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2004.
520 $a Nested logit models have been widely estimated by maximum likelihood estimation. A key issue is inference on the coefficient of the inclusive variable, because this coefficient provides an insight into the decision making process of the economic agent and the degree of substitution between the available choices. In theory, this coefficient needs to be within the unit interval. When this constraint is violated, the data is deemed inconsistent with random utility maximization. Constraining this coefficient within the unit interval is not a solution since, in many applications, the estimate hits one of the bounds (0 or 1) and is often unable to make inference because the constrained maximum likelihood estimation procedures do not produce standard error estimates.
520 $a I study nested logit models under a Bayesian framework, and present a new Markov Chain Monte Carlo algorithm to estimate such models. The algorithm is designed block-wise, and is a single channel Metropolis-Hastings algorithm. Bayesian framework has two advantages: (1) it provides posterior probability density functions of coefficients, and (2) it allows imposition of constraints on coefficients much more easily than maximum likelihood estimation. The algorithm is applied to ambulatory health care data to analyze the choices of physicians in treating patients diagnosed with depression.
590 $a School code: 0190.
650 4 $a Economics, General. $3 1017424
650 4 $a Health Sciences, Mental Health. $3 1017693
650 4 $a Health Sciences, Public Health. $3 1017659
690 $a 0501
690 $a 0347
690 $a 0573
710 2 0 $a Rutgers The State University of New Jersey - New Brunswick. $3 1017590
773 0 $t Dissertation Abstracts International $g 64-12A.
790 1 0 $a Tsurumi, Hiroki, $e advisor
790 $a 0190
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3117588