Checking the adequacy of partial linear models with missing covariates at random

Wangli Xu () and Xu Guo ()

Annals of the Institute of Statistical Mathematics, 2013, vol. 65, issue 3, 473-490

Abstract: In this paper, we consider the goodness-of-fit for checking whether the nonparametric function in a partial linear regression model with missing covariate at random is a parametric one or not. We estimate the selection probability by using parametric and nonparametric approaches. Two score type tests are constructed with the estimated selection probability. The asymptotic distributions of the test statistics are investigated under the null and local alterative hypothesis. Simulation studies are carried out to examine the finite sample performance of the sizes and powers of the tests. We apply the proposed procedure to a data set on the AIDS clinical trial group (ACTG 315) study. Copyright The Institute of Statistical Mathematics, Tokyo 2013

Keywords: Partial linear model; Lack-of-fit test; Covariates missing at random; Inverse probability weights (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:aistmt:v:65:y:2013:i:3:p:473-490

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DOI: 10.1007/s10463-012-0379-4

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