The Asymmetric Power-Student-t Model for Censored and Truncated Data - ROGER TOVAR-FALÓN; HELENO BOLFARINE; GUILLERMO MARTÍNEZ-FLÓREZ (Anais da Academia Brasileira de Ciências, 2021)
Abstract In this paper, we propose the power Student-t regression model for censored (limited) observations which extends the Student-t censored regression model. This extension is based on the asymmetric and heavy-tailed power Student-t distribution. The score functions and expected information matrix are given as well as the process for estimating the parameters in the model is discussed by using the likelihood approach. Two simulation studies are conducted to evaluate parameter recovery and properties of the model and finally, two applications to a real data set are reported to demonstrate the usefulness of this new methodology.