Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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The skew-t censored regression model: parameter estimation via an EM-type algorithm

  • Lachos, Victor H. (Department of Statistics, University of Connecticut) ;
  • Bazan, Jorge L. (Department of Applied Mathematics and Statistics, Universidade of Sao Paulo) ;
  • Castro, Luis M. (Department of Statistics, Pontificia Universidad Catolica de Chile) ;
  • Park, Jiwon (Department of Statistics, University of Connecticut)
  • Received : 2021.10.19
  • Accepted : 2022.04.19
  • Published : 2022.05.31
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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.

Keywords

Acknowledgement

We thank the associate editor and two anonymous referees for their important comments and suggestions which lead to an improvement of this paper. Jorge L. Bazan acknowledges support from FAPESP-Brazil (Grant 2021/11720-0). L. M. Castro acknowledges support from Grant FONDECYT 1220799 from the Chilean government.

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