The Asymmetric Power-Student-t Model for Censored and Truncated Data.

Roger Tovar-Falón,Heleno Bolfarine,Guillermo Martínez-Flórez

doi:10.1590/0001-3765202120190920

Roger Tovar-Falón, Heleno Bolfarine + Show 1 more

Open Access

https://doi.org/10.1590/0001-3765202120190920

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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.

Highlights

Regression models where the response variable is censored or limited are common in different fields: clinical essays, econometric analysis, social phenomena, engineering studies, among others
We analyzed the data set by fitting a PTCR model and we compare our proposal with Scale Mixture of Normal Censored Regression (SMNCR) models by Garay et al (2017): Student-t censored regression model (TCR) (Arellano-Valle et al 2012), Slash censored regression model (SLCR), and normal censored regression model (NCR), that is, the usual tobit model
It follows that the ordinary tobit model and the Student-t censored regression models are special cases

Summary

Introduction

Regression models where the response variable is censored or limited are common in different fields: clinical essays, econometric analysis, social phenomena, engineering studies, among others. Another extension of the Tobit model was proposed by Martínez-Flórez et al (2013) by considering that random errors follow a power-normal (PN) distribution (Gupta & Gupta 2008).

Results

Conclusion