Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31119
Authors: 
Einbeck, Jochen
Augustin, Thomas
Year of Publication: 
2005
Series/Report no.: 
Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 465
Abstract: 
Weighting is a largely used concept in many fields of statistics and has frequently cause controversies on its justification and profit. In this paper, we analyze a weighted version of the well-known local polynomial regression estimators, derive their asymptotic bias and variance, and find that the conflict between the asymptotically optimal weighting scheme and the practical requirements has a surprising counterpart in sampling theory, leading us back to the discussion on Basu's (1971) elephants,
Subjects: 
Bias reduction
nonparametric smoothing
local polynomial modelling
kernel smoothing : leverage values
Horvitz-Thompson theorem
stratification
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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