Order selection tests with multiply imputed data

Fabrizio Consentino and Gerda Claeskens

Computational Statistics & Data Analysis, 2010, vol. 54, issue 10, 2284-2295

Abstract: Nonparametric tests for the null hypothesis that a function has a prescribed form are developed and applied to data sets with missing observations. Omnibus nonparametric tests such as the order selection tests, do not need to specify a particular alternative parametric form, and have power against a large range of alternatives. More specifically, likelihood-based order selection tests are defined that can be used for multiply imputed data when the data are missing-at-random. A simulation study and data analysis illustrate the performance of the tests. In addition, an Akaike information criterion for model selection is presented that can be used with multiply imputed datasets.

Keywords: Akaike; information; criterion; Hypothesis; test; Multiple; imputation; Lack-of-fit; test; Missing; data; Omnibus; test; Order; selection (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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