Parameter-Expanded Data Augmentation for Analyzing Nominal Data With Missing Values Using Multinomial Probit Models

Nominal data occur in many scientific fields, such as health-related studies, transportation, and econometrics. This paper proposes Parameter-Expanded Data Augmentation (PX-DA) to analyze nominal data with missing values using Multinomial Probit (MNP) models. The proposed methods significantly improve the convergence and mixing of MCMC sampling components and can handle nominal data with substantial missingness.

Keywords: Multinomial Probit (MNP) Model, Markov Chain Monte Carlo (MCMC), Parameter-Expanded Data Augmentation (PX-DA), Missing Data

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