| Автор | Foster, Peter |
| Дата выпуска | 1995 |
| dc.description | Methods for obtaining kernel-based density estimators with lower bias and mean integrated squared error than an estimator based on a standard Normal kernel function are described and discussed. Three main approaches are considered which are: firstly by using 'optimal' polynomial kernels as described, for example, by Gasser er a1 (1985); secondly by employing generalised jackknifing as proposed by Jones nd Foster (1993) and thirdly by subtracting an estimator of the principal asymptotic bias term from the original estimator. The emphasis in this initial discussion is on their asymptotic properties. The finite sample performance of those that have the best asymptotic properties are compared with two adaptive estimators, as well as the fixed Normal kernel estimator, in a simulation study. |
| Формат | application.pdf |
| Издатель | Gordon and Breach Science Publishers |
| Копирайт | Copyright Taylor and Francis Group, LLC |
| Тема | Bias reduction |
| Тема | Density estimation |
| Тема | Derivatives |
| Тема | Jackknifing |
| Тема | MISE |
| Тема | Optimal kernels |
| Тема | Smoothing |
| Название | A comparative study of some bias correction techniques for kernel- based density estimators |
| Тип | research-article |
| DOI | 10.1080/00949659508811628 |
| Electronic ISSN | 1563-5163 |
| Print ISSN | 0094-9655 |
| Журнал | Journal of Statistical Computation and Simulation |
| Том | 51 |
| Первая страница | 137 |
| Последняя страница | 152 |
| Аффилиация | Foster, Peter; Statistical Laboratory, Department of Mathematics, The University |
| Выпуск | 2-4 |
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