A0218
Title: Censored autoregressive regression models with Student-t innovations
Authors: Christian Eduardo Galarza Morales - Escuela Superior Politecnica del Litoral (Ecuador) [presenting]
Fernanda Schumacher - The Ohio State University (United States)
Katherine Andreina Loor Valeriano - University of Campinas (Brazil)
Larissa Avila Matos - Campinas State University (Brazil)
Abstract: Data collected over time is common in applications and may contain censored or missing observations, making it difficult to use standard statistical procedures. The focus is on proposing an algorithm to estimate the parameters of a censored linear regression model with errors serially correlated and innovations following a Student-t distribution. This distribution is widely used in the statistical modelling of data containing outliers since its longer-than-normal tails provide a robust approach to handling such data. The maximum likelihood estimates of the proposed model are obtained through a stochastic approximation of the EM algorithm. The methods are applied to an environmental dataset regarding ammonia-nitrogen concentration, which is subjected to a limit of detection (left censoring) and contains missing observations. Additionally, two simulation studies are conducted to examine the asymptotic properties of the estimates and the robustness of the model.
