Censored autoregressive regression models with Student-t innovations and their application in river water quality assessment
Author
Larissa Avila Matos
Co-author
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Katherine A. L. Valeriano
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Fernanda Lang Schumacher
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Christian Eduardo Galarza Morales
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Larissa Avila Matos
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Conference
65th ISI World Statistics Congress 2025
Format: IPS Abstract - WSC 2025
Keywords: censoring, heavy-tailed, statistical models
Session: IPS 910 - Handling Time in Environmental Studies
Thursday 9 October 2 p.m. - 3:40 p.m. (Europe/Amsterdam)
Abstract
Data collected over time are common in applications and may contain censored or missing observations, making it difficult to use standard statistical procedures. This work proposes 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 modeling of data containing outliers because its longer-than-normal tails provide a robust approach to handling such data. The methods are applied to an environmental dataset regarding ammonia-nitrogen concentration, which is subject to a limit of detection (left censoring) and contains missing observations.