Abstract
Weighting is a largely used concept in many fields of statistics and has frequently caused 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.
| Item Type: | Paper |
|---|---|
| Keywords: | Bias reduction, nonparametric smoothing, local polynomial modelling, kernel smoothing, leverage values, Horvitz-Thompson theorem, stratification |
| Faculties: | Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386 Special Research Fields > Special Research Field 386 |
| Subjects: | 500 Science > 510 Mathematics |
| URN: | urn:nbn:de:bvb:19-epub-1834-7 |
| Language: | English |
| Item ID: | 1834 |
| Date Deposited: | 11. Apr 2007 |
| Last Modified: | 04. Nov 2020 12:45 |

