Smoothing, Sampling, and Basu's elephants

Einbeck, Jochen; Augustin, Thomas; Singer, Julio M.

Abstract

We investigate design-weighted local smoothing and show that the optimal (bias-minimizing) weights have similar form and interpretation as the optimal weights given by the Horvitz-Thompson theorem known from sampling theory. We set forth that the hazards in using bias-minimizing weights apply to kernel smoothing, too, suggesting to be cautious with the application of bias-minimizing weights in general.

Citation

Einbeck, J., Augustin, T., & Singer, J. M. (2007, July). Smoothing, Sampling, and Basu's elephants. Presented at 22nd International Workshop on Statistical Modelling., Barcelona, Spain

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 22nd International Workshop on Statistical Modelling.
• Publication Date: 2007-07
• Pages: 245-248
• Series Title: Proceedings of the IWSM
• Keywords: Weighting, Horvitz-Thompson estimator; local polynomials
• Public URL: https://durham-repository.worktribe.com/output/1160839
• Publisher URL: http://www.maths.dur.ac.uk/~dma0je/Papers/einbeck_augustin_singer_iwsm07.pdf