Influence diagnostics for censored regression autoregressive models

What is it about?

Observations collected over time are often autocorrelated rather than independent, and sometimes include observations below or above detection limits (i.e. censored values reported as less or more than a level of detection) and/or missing data. Practitioners commonly disregard censored data cases or replace these observations with some function of the limit of detection, which often results in biased estimates. Moreover, parameter estimation can be greatly affected by the presence of influential observations in the data. In this paper we derive local influence diagnostic measures for censored regression models with autoregressive errors of order p (hereafter, AR(p)-CR models) on the basis of the Q-function under three useful perturbation schemes.

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Why is it important?

This article proposes influence diagnostic tools for detecting influential observations on the context of censored linear models with autoregressive errors.