Gradient-based smoothing parameter selection for nonparametric regression estimation Academic Article uri icon


  • 2014 Elsevier B.V. Estimating gradients is of crucial importance across a broad range of applied economic domains. Here we consider data-driven bandwidth selection based on the gradient of an unknown regression function. This is a difficult problem given that direct observation of the value of the gradient is typically not observed. The procedure developed here delivers bandwidths which behave asymptotically as though they were selected knowing the true gradient. Simulated examples showcase the finite sample attraction of this new mechanism and confirm the theoretical predictions.

published proceedings


author list (cited authors)

  • Henderson, D. J., Li, Q. i., Parmeter, C. F., & Yao, S.

citation count

  • 15

complete list of authors

  • Henderson, Daniel J||Li, Qi||Parmeter, Christopher F||Yao, Shuang

publication date

  • January 2015