Testing error heterogeneity in censored linear regression

Caiyun Fan, Wenbin Lu and Yong Zhou

Computational Statistics & Data Analysis, 2021, vol. 161, issue C

Abstract: In censored linear regression, a key assumption is that the error is independent of predictors. We develop an omnibus test to check error heterogeneity in censored linear regression. Our approach is based on testing the variance component in a working kernel machine regression model. The limiting null distribution of the proposed test statistic is shown to be a weighted sum of independent chi-squared distributions with one degree of freedom. A resampling scheme is derived to approximate the null distribution. The empirical performance of the proposed tests is evaluated via simulation and two real data sets.

Keywords: Censored linear regression; Error heterogeneity; Kernel machine regression; Resampling (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:161:y:2021:i:c:s0167947321000414

DOI: 10.1016/j.csda.2021.107207

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