Bandwidth Choice for Average Derivative Estimation Academic Article uri icon


  • 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 n1. 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. 1992 Taylor & Francis Group, LLC.

published proceedings

  • Journal of the American Statistical Association

altmetric score

  • 3

author list (cited authors)

  • Hrdle, W., Hart, J., Marron, J. S., & Tsybakov, A. B.

citation count

  • 23

complete list of authors

  • Härdle, W||Hart, J||Marron, JS||Tsybakov, AB

publication date

  • January 1992