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Estimating linear functionals in nonlinear regression with responses missing at random. (English) Zbl 1173.62052

Summary: We consider regression models with parametric (linear or nonlinear) regression functions and allow responses to be “missing at random”. We assume that the errors have mean zero and are independent of the covariates. In order to estimate expectations of functions of covariates and responses we use a fully imputed estimator, namely an empirical estimator based on estimators of conditional expectations given the covariate. We exploit the independence of covariates and errors by writing the conditional expectations as unconditional expectations, which can now be estimated by empirical plug-in estimators. The mean zero constraint on the error distribution is exploited by adding suitable residual-based weights. We prove that the estimator is efficient (in the sense of Hájek and Le Cam) if an efficient estimator of the parameter is used. Our results give rise to new efficient estimators of smooth transformations of expectations. Estimation of the mean response is discussed as a special (degenerate) case.

MSC:

62J02 General nonlinear regression
62G20 Asymptotic properties of nonparametric inference
62G08 Nonparametric regression and quantile regression
65C60 Computational problems in statistics (MSC2010)
62N01 Censored data models
62F12 Asymptotic properties of parametric estimators

Software:

BayesDA
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References:

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