Deconvolution kernel estimator for mean transformation with ordinary smooth error

Huai-Zhen Qin and Shi-Yong Feng

Statistics & Probability Letters, 2003, vol. 61, issue 4, 337-346

Abstract: Consider the convolution model Y=X+[var epsilon] in which [var epsilon] is the ordinary smooth measurement error with a known distribution. The estimator of mean transformation [theta]=E[G(X)] is constructed by deconvolution kernel technique. Moment and weak convergence rates of the proposed estimator are derived under some mild regularity conditions. Simulation results indicate that the underlying estimator is highly accurate and robust.

Keywords: Measurement; error; Bandwidth; selection; Super; population; Rates; of; convergence (search for similar items in EconPapers)
Date: 2003
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