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Deconvolution kernel estimator for mean transformation with ordinary smooth error

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  • Qin, Huai-Zhen
  • Feng, Shi-Yong

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.

Suggested Citation

  • Qin, Huai-Zhen & Feng, Shi-Yong, 2003. "Deconvolution kernel estimator for mean transformation with ordinary smooth error," Statistics & Probability Letters, Elsevier, vol. 61(4), pages 337-346, February.
    • Handle: RePEc:eee:stapro:v:61:y:2003:i:4:p:337-346
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    References listed on IDEAS

    as
    1. Song Xi Chen, 1996. "A Kernel Estimate for the Density of a Biological Population by Using Line Transect Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(2), pages 135-150, June.
    2. Ioannides, D. A. & Alevizos, P. D., 1997. "Nonparametric regression with errors in variables and applications," Statistics & Probability Letters, Elsevier, vol. 32(1), pages 35-43, February.
    3. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-1430, November.
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