Convergence rate of the kernel regression estimator for associated and truncated data
| Autor: | Z. Guessoum, F. Hamrani |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
010102 general mathematics Estimator 01 natural sciences Nonparametric regression 010104 statistics & probability Minimum-variance unbiased estimator Bias of an estimator Kernel (statistics) Consistent estimator Statistics Principal component regression Kernel regression 0101 mathematics Statistics Probability and Uncertainty Mathematics |
| Zdroj: | Journal of Nonparametric Statistics. 29:425-446 |
| ISSN: | 1029-0311 1048-5252 |
| DOI: | 10.1080/10485252.2017.1303059 |
| Popis: | This paper studies the behaviour of the kernel estimator of the regression function for associated data in the random left truncated model. The uniform strong consistency rate over a real compact set of the estimate is established. The finite sample performance of the estimator is investigated through extensive simulation studies. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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