Nonparametric inference for covariate-adjusted model

Shuang Dai and Zhensheng Huang

Statistics & Probability Letters, 2020, vol. 162, issue C

Abstract: In this paper, we provide a nonparametric test for the covariate-adjusted model where the variables are not directly observed, but are observed after being distorted by unknown functions of a commonly observable covariate in a multiplicative fashion. We estimate the distorting functions by nonparametrically regressing the response and predictors on the distorting covariate, and calculate the estimators of the unobserved variables. Based on the calibrated variables, we propose a generalized likelihood ratio (GLR) test statistic to check the adequacy for the covariate-adjusted model and establish the asymptotic property of the GLR test statistic. Moreover, we carry out some simulated and real examples to evaluate the performance of the GLR test statistic, and make comparisons with these results obtained by Zhang et al. (2015).

Keywords: Covariate-adjusted model; Nonparametric inference; Generalized likelihood ratio test; Bootstrap method (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.spl.2020.108766

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