Diagnostic Measures for Functional Linear Model with Nonignorable Missing Responses

Abstract

Assessing the influence of individual observations of the functional linear models is important and challenging, especially when the observations are subject to missingness. In this paper, we introduce three case-deletion diagnostic measures to identify influential observations in functional linear models when the covariate is functional and observations on the scalar response are subject to nonignorable missingness. The nonignorable missing data mechanism is modeled via an exponential tilting semiparametric functional model. A semiparametric imputation procedure is developed to mitigate the effects of missing data. Valid estimations of the functional coefficients are based on functional principal components analysis using the imputed dataset. A smoothed bootstrap sampling method is introduced to estimate the diagnostic probability for each proposed diagnostic measure, which is helpful to unveil which observations have the larger influence on estimation and prediction. Simulation studies and a real data example are conducted to illustrate the finite performance of the proposed methods.

Keywords

Case deletion / Diagnostic measure / Functional linear model / Nonignorable nonresponse / Semiparametric imputation

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Funding

National Natural Science Foundation of China(12071416)