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Bandwidth Choice for Average Derivative Estimation
W. Hardle, J. Hart, J. S. Marron and A. B. Tsybakov
Journal of the American Statistical Association
Vol. 87, No. 417 (Mar., 1992), pp. 218-226
Stable URL: http://www.jstor.org/stable/2290472
Page Count: 9
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The average derivative is the expected value of the derivative of a regression function. Kernel methods have been proposed as a means of estimating this quantity. The problem of bandwidth selection for these kernel estimators is addressed here. Asymptotic representations are found for the variance and squared bias. These are compared with each other to find an insightful representation for a bandwidth optimizing terms of lower order than n-1. It is interesting that, for dimensions greater than 1, negative kernels have to be used to prevent domination of bias terms in the asymptotic expression of the mean squared error. The extent to which the theoretical conclusions apply in practice is investigated in an economical example related to the so-called "law of demand."
Journal of the American Statistical Association © 1992 American Statistical Association