Order selection tests with multiply imputed data |
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Authors: | Fabrizio Consentino |
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Affiliation: | ORSTAT and Leuven Statistics Research Center, K.U. Leuven, 3000 Leuven, Belgium |
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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. |
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Keywords: | Akaike information criterion Hypothesis test Multiple imputation Lack-of-fit test Missing data Omnibus test Order selection |
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