Testing lack of fit in multiple regression Academic Article

abstract

• We study lack-of-fit tests based on orthogonal series estimators. A common feature of these tests is that they are functions of score statistics that employ data-driven model dimensions. The criteria used to select the dimension are score-based versions of AIC and BIC. The tests can be applied in a wide variety of settings, including both continuous and discrete data. With two or more covariates, a model sequence, i.e. a path in the alternative models space, has to be chosen. Critical points and p-values of the lack-of-fit tests can be obtained via asymptotic distribution theory or by use of the bootstrap. Data examples and a simulation study illustrate the applicability of the tests. 2000 Biometrika Trust.

authors

• Hart, Jeffrey

published proceedings

• BIOMETRIKA

author list (cited authors)

• Aerts, M., Claeskens, G., & Hart, J. D.

citation count

• 45

complete list of authors

• Aerts, M||Claeskens, G||Hart, JD

publication date

• January 2000

publisher

• Oxford University Press (OUP)  Publisher

published in

• Biometrika  Journal

keywords

• Additive Model
• Lack Of Fit
• Multiple Regression
• Nonparametric Series Estimation
• Omnibus Test
• Penalised Score
• Score Statistic

Digital Object Identifier (DOI)

• 10.1093/biomet/87.2.405

start page

• 405

end page

• 424

volume

• 87

issue

• 2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1093%2Fbiomet%2F87.2.405