Likelihood-based inference for interval censored regression models under heavy-tailed distributions

Yessenia A. Gil (), Aldo M. Garay () and Victor H. Lachos ()
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Yessenia A. Gil: Federal University of Pernambuco
Aldo M. Garay: Federal University of Pernambuco
Victor H. Lachos: University of Connecticut

Statistical Methods & Applications, 2025, vol. 34, issue 3, No 6, 519-544

Abstract: Abstract Scale mixtures of skew-normal distributions form a class of asymmetric thick-tailed distributions that include skew-normal, skew-t, skew-contaminated normal, and the entire family of scale mixtures of normal distributions as special cases. This paper proposes an interval-censored linear regression model based on the class of scale mixtures of skew-normal distributions, providing an appealing, robust alternative to the usual Gaussian assumption in censored regression models. A novel Expectation/Conditional Maximization Either algorithm is proposed for maximum likelihood estimation, with analytical expressions at the E-step, as opposed to Monte Carlo simulations. These expressions rely on formulas for the mean and variance of truncated scale mixtures of skew-normal distributions that can be computed using the MomTrunc R package. The proposed methodology is illustrated through intensive simulations and the analysis of a real data set from the Household Survey OHS99 conducted by Statistics South Africa.

Keywords: Censored regression models; Heavy-tailed distributions; ECM algorithm; Scale mixtures of skew-normal distributions (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10260-025-00797-x

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