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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/263

Title: Testing lack of fit in multiple regression
Authors: AERTS, Marc
HART, Jeffrey
Keywords: Non and semiparametric methods
Issue Date: 2000
Citation: Biometrika, 87(2). p. 405-424
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 Are 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.
URI: http://hdl.handle.net/1942/263
DOI: 10.1093/biomet/87.2.405
ISI #: 000087815100012
ISSN: 0006-3444
Category: A1
Type: Journal Contribution
Validation: ecoom, 2001
Appears in Collections: Research publications

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