Detalle Publicación

Designing to detect heteroscedasticity in a regression model

Autores: Lanteri, A. (Autor de correspondencia); Leorato, S.; López Fidalgo, Jesús Fernando; Tommasi, C.
Título de la revista: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
ISSN: 1369-7412
Volumen: 85
Número: 2
Páginas: 315 - 326
Fecha de publicación: 2023
Resumen:
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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