A popular tool for analyzing product choices of consumers is the well-known conditional logit discrete choice model. Originally publicized by McFadden (1974), this model assumes that the random components of the underlying latent utility functions of the consumers follow independent Gumbel distributions. However, in practice the independence assumption may be violated and a more reasonable model should account for the dependence of the utilities. In this dissertation we use the Gaussian copula with compound symmetric and autoregressive of order one correlation matrices to construct a general multivariate model for the joint distribution of the utilities. The induced correlations on the utilities and the choice probabilities are studied using analytic expressions and simulations. For regression with consumer and product specic covariates, we derive expressions for the likelihood function and the score functions. We use numerical methods and computer code to obtain the maximum likelihood estimates of the regression and correlation parameters. The standard errors of the estimates were obtained using bootstrap. Comparison of our model with other competing methods and practical applicability is illustrated using both real world consumer preference and simulated data.
","abstract_html":"<p>A popular tool for analyzing product choices of consumers is the well-known conditional logit discrete choice model. Originally publicized by McFadden (1974), this model assumes that the random components of the underlying latent utility functions of the consumers follow independent Gumbel distributions. However, in practice the independence assumption may be violated and a more reasonable model should account for the dependence of the utilities. In this dissertation we use the Gaussian copula with compound symmetric and autoregressive of order one correlation matrices to construct a general multivariate model for the joint distribution of the utilities. The induced correlations on the utilities and the choice probabilities are studied using analytic expressions and simulations. For regression with consumer and product specic covariates, we derive expressions for the likelihood function and the score functions. We use numerical methods and computer code to obtain the maximum likelihood estimates of the regression and correlation parameters. The standard errors of the estimates were obtained using bootstrap. Comparison of our model with other competing methods and practical applicability is illustrated using both real world consumer preference and simulated data.</p>","abstract_has_math":false,"creators":["Poddar, Arjun"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Mathematics & Statistics","degree_department":null,"school":null,"contributors":["N. Rao Chaganty","Norou Diawara","Kayoung Park","Rajesh Paleti"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-01T07:00:00Z","date_published":"2016-07-01T07:00:00Z","updated_at":"2026-07-24T03:33:54Z","subjects":["Correlated discrete choice models","Dependent discrete choice models","Derivative of multivariate normal CDF","Gaussian copula","Generalized conditional logit model","Gumbel distribution","Economics","Marketing","Statistical Models","Statistics and Probability"],"languages":[],"rights":["In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
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"]},{"key":"dc:title","label":"Title","values":["Analysis off Dependent Discrete Choices Using Gaussian Copula"]}]}],"canonical_facts":{"dc:contributor":["N. Rao Chaganty","Norou Diawara","Kayoung Park","Rajesh Paleti"],"dc:creator":["Poddar, Arjun"],"dc:date.available":["2018-10-19T07:00:00Z"],"dc:description.abstract":["A popular tool for analyzing product choices of consumers is the well-known conditional logit discrete choice model. Originally publicized by McFadden (1974), this model assumes that the random components of the underlying latent utility functions of the consumers follow independent Gumbel distributions. However, in practice the independence assumption may be violated and a more reasonable model should account for the dependence of the utilities. In this dissertation we use the Gaussian copula with compound symmetric and autoregressive of order one correlation matrices to construct a general multivariate model for the joint distribution of the utilities. The induced correlations on the utilities and the choice probabilities are studied using analytic expressions and simulations. For regression with consumer and product specic covariates, we derive expressions for the likelihood function and the score functions. We use numerical methods and computer code to obtain the maximum likelihood estimates of the regression and correlation parameters. The standard errors of the estimates were obtained using bootstrap. Comparison of our model with other competing methods and practical applicability is illustrated using both real world consumer preference and simulated data.
"],"dc:identifier":["9781369170535","https://digitalcommons.odu.edu/mathstat_etds/1"],"dc:rights":["In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
"],"dc:subject":["Correlated discrete choice models","Dependent discrete choice models","Derivative of multivariate normal CDF","Gaussian copula","Generalized conditional logit model","Gumbel distribution","Economics","Marketing","Statistical Models","Statistics and Probability"],"dc:title":["Analysis off Dependent Discrete Choices Using Gaussian Copula"],"thesis:degree_discipline":["Mathematics & Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:33:54Z"}