Designing to detect heteroscedasticity in a regression model
成果类型：
Article
署名作者：
Lanteri, Alessandro; Leorato, Samantha; Lopez-Fidalgo, Jesus; Tommasi, Chiara
署名单位：
University of Milan; University of Navarra; University of Milan
刊物名称：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
ISSN/ISSBN：
1369-7412
DOI：
10.1093/jrsssb/qkad004
发表日期：
2023
页码：
315-326
关键词：
摘要：
We consider the problem of designing experiments to detect the presence of a specified heteroscedastity in Gaussian regression models. We study the relationship of the D-s- and KL-criteria with the noncentrality parameter of the asymptotic chi-squared distribution of a likelihood-based test, for local alternatives. We found that, when the heteroscedastity depends on one parameter, the two criteria coincide asymptotically and that the D-1-criterion is proportional to the noncentrality parameter. Differently, when it depends on several parameters, the KL-optimum design converges to the design that maximizes the noncentrality parameter. Our theoretical findings are confirmed through a simulation study.
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