| Автор | Swain, James J. |
| Дата выпуска | 1988 |
| dc.description | A Monte Carlo control variate method is used to study the estimators obtained in nonlinear regression under nonnormal error distributions. Two forms of the standard linear approximator are used as the control variates: a natural approximator using the nonnormal errors sampled, and a normalized approximator obtained by transformation of the errors. The natural approximator is shown to be most effective when the sampling distribution is itself nonnormal; its effectiveness is well approximated by a function of the Beale measure of nonlinearity. The normalized approximator is most effective when the estimator sampling distribution is approximately normal. A one-parameter model is used for illustration with uniform and gamma distributed errors |
| Формат | application.pdf |
| Издатель | Marcel Dekker, Inc. |
| Копирайт | Copyright Taylor and Francis Group, LLC |
| Тема | control variates |
| Тема | nonlinear estimation |
| Тема | simulation swindle |
| Тема | variance reduction |
| Название | Control variates for monte carlo analysis of nonlinear statisticalmodels. IV |
| Тип | research-article |
| DOI | 10.1080/03610918808812660 |
| Electronic ISSN | 1532-4141 |
| Print ISSN | 0361-0918 |
| Журнал | Communications in Statistics - Simulation and Computation |
| Том | 17 |
| Первая страница | 251 |
| Последняя страница | 274 |
| Аффилиация | Swain, James J.; Industrial and Systems Engineering, Georiga Institute of Technology |
| Выпуск | 1 |
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| Библиографическая ссылка | Swain, J.J. 1982. “Monte Carlo Estimation of the Sampling Distribution of Nonlinear Parameter Estimators." Unpublished Ph.D. Thesis”. In School of Industrial Engineering, Unpublished Ph.D. Thesis. Purdue University, W. Lafayette. IN |
