| タイトル | AIC for ergodic diffusion processes from discrete observations |
| 本文（外部サイト） | https://catalog.lib.kyushu-u.ac.jp/opac_download_md/3362/2005-12.pdf |
| 参考URL | http://hdl.handle.net/2324/3362 |
| 著者（英） | Uchida, Masayuki; Yoshida, Nakahiro |
| 発行日 | 2009-04-22 |
| 発行機関など | Faculty of Mathematics, Kyushu University |
| 刊行物名 | MHF Preprint Series |
| 巻 | MHF2005-12 |
| 刊行年月日 | 2005-03-08 |
| 言語 | eng |
| 内容記述 | Akaike’s information criterion (AIC) is proposed for evaluating statistical models constructed by the maximum likelihood estimators under the situation where the parametric models contain the true model. In order to obtain AIC, it suffices to get a log likelihood function and the maximum likelihood estimator. However, we can not generally derive AIC for discretely observed diffusion processes since the transition densities of diffusion processes do not commonly have explicit forms. This paper presents AIC type of information criterion for discretely observed ergodic diffusion processes. The information criterion is constructed by using an approximate log likelihood function and an asymptotically efficient estimator. The approximate log likelihood function is based on a result of Dacunha-Castelle and Florens-Zmirou (1986). The asymptotically efficient estimator is derived from a contrast function based on a locally Gaussian approximation. |
| キーワード | 62B10; 62M05; 62F12; 60J60; Akaike’s information criteria; model selection; maximum contrast estimator; approximate log likelihood function; discrete time observation |
| 資料種別 | Preprint |
| 著者版フラグ | author |
