| タイトル | Nonparametric maximum likelihood estimation of probability densities by penalty function methods |
| 本文（外部サイト） | http://hdl.handle.net/2060/19750022788 |
| 著者（英） | Tapia, R. A.; Demontricher, G. F.; Thompson, J. R. |
| 著者所属（英） | Rice Univ. |
| 発行日 | 1974-08-01 |
| 言語 | eng |
| 内容記述 | When it is known a priori exactly to which finite dimensional manifold the probability density function gives rise to a set of samples, the parametric maximum likelihood estimation procedure leads to poor estimates and is unstable; while the nonparametric maximum likelihood procedure is undefined. A very general theory of maximum penalized likelihood estimation which should avoid many of these difficulties is presented. It is demonstrated that each reproducing kernel Hilbert space leads, in a very natural way, to a maximum penalized likelihood estimator and that a well-known class of reproducing kernel Hilbert spaces gives polynomial splines as the nonparametric maximum penalized likelihood estimates. |
| NASA分類 | STATISTICS AND PROBABILITY |
| レポートNO | 75N30861
REPT-275-025-016
NASA-CR-144384 |
| 権利 | No Copyright |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/188850 |
