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Activity Number: 667
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #320198
Title: Estimation of Heterogeneity for Multinomial Probit Models
Author(s): Yixi Xu* and Qiang Liu and Xiao Wang
Companies: and Purdue University and Purdue University
Keywords: multinomial probit model ; dictionary learning ; EM algorithm ; heterogeneity ; random effect

In this paper, we study the multinomial probit model in which covariate effects are modeled semiparametrically. We incorporate heterogeneity across subjects by including the random effect for both parametric components and nonparametric components. In particular, novel techniques motivated from dictionary learning are developed for modeling individual nonparametric functions. Efficient algorithm is implemented to estimate the unknown parameters. The finite sample performance is illustrated by simulations and an application of brand choice study.

Authors who are presenting talks have a * after their name.

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