The skew-t censored regression model: parameter estimation via an EM-type algorithm

Author(s)

Lachos, Victor H. ; Bazan, Jorge L. ; Castro, Luis M. ; Park, Jiwon

• Document type: Journal article
• Source: COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS; v. 29, n. 3, p. 19-pg., 2022-05-01.

Abstract

The skew-t distribution is an attractive family of asymmetrical heavy-tailed densities that includes the normal, skew-normal and Student's-t distributions as special cases. In this work, we propose an EM-type algorithm for computing the maximum likelihood estimates for skew-t linear regression models with censored response. In contrast with previous proposals, this algorithm uses analytical expressions at the E-step, as opposed to Monte Carlo simulations. These expressions rely on formulas for the mean and variance of a truncated skew-t distribution, and can be computed using the R library MomTrunc. The standard errors, the prediction of unobserved values of the response and the log-likelihood function are obtained as a by-product. The proposed methodology is illustrated through the analyses of simulated and a real data application on Letter-Name Fluency test in Peruvian students. (AU)

• FAPESP's process: 21/11720-0 - Supervised learning on computer-aided discrete response data with applications in imbalanced data
• Grantee: Jorge Luis Bazan Guzman
• Support Opportunities: Regular Research Grants