Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김대경 | - |
dc.date.accessioned | 2023-07-20T01:20:36Z | - |
dc.date.available | 2023-07-20T01:20:36Z | - |
dc.date.issued | 2009-05 | - |
dc.identifier.citation | Communications for Statistical Applications and Methods, v. 16, NO. 3, Page. 479-485 | - |
dc.identifier.issn | 2287-7843 | - |
dc.identifier.uri | https://kiss.kstudy.com/thesis/thesis-view.asp?key=2776581 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/183950 | - |
dc.description.abstract | When a part of data is unobserved the marginal likelihood of parameters given the observed data often involves analytically intractable high dimensional integral and hence it is hard to find the maximum likelihood estimate of the parameters. Simulated maximum likelihood(SML) method which estimates the marginal likelihood via Monte Carlo importance sampling and optimize the estimated marginal likelihood has been used in many applications. A key issue in SML is to find a good proposal density from which Monte Carlo samples are generated. The optimal proposal density is the conditional density of the unobserved data given the parameters and the observed data, and attempts have been given to find a good approximation to the optimal proposal density. Algorithms which adaptively improve the proposal density have been widely used due to its simplicity and efficiency. In this paper, we describe a fully adaptive algorithm which has been used by some practitioners but has not been well recognized in statistical literature, and evaluate its estimation performance and robustness via a simulation study. The simulation study shows a great improvement in the order of magnitudes in the mean squared error, compared to non-adaptive or partially adaptive SML methods. Also, it is shown that the fully adaptive SML is robust in a sense that it is insensitive to the starting points in the optimization routine. | - |
dc.description.sponsorship | This article was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant Number R11-2008-044-01002-0) and Korea Industrial Technology Foundation (KOTEF) through the Human Resource Training Project for Strategic Technology. This work was also supported by grants from the Korea Health 21 R&D Project, Ministry of Health and Welfare (A090419 to IYC). S. H. K. and J. H. K. are partially supported from BK21, Korea Research Foundation. | - |
dc.language | en | - |
dc.publisher | 한국통계학회 | - |
dc.subject | Monte Carlo | - |
dc.subject | importance sampling | - |
dc.subject | marginal likelihood | - |
dc.subject | missing | - |
dc.title | Efficiency and Robustness of Fully Adaptive Simulated Maximum Likelihood Method | - |
dc.type | Article | - |
dc.relation.no | 3 | - |
dc.relation.volume | 16 | - |
dc.relation.page | 479-485 | - |
dc.relation.journal | Communications for Statistical Applications and Methods | - |
dc.contributor.googleauthor | 오만숙 | - |
dc.contributor.googleauthor | 김대경 | - |
dc.sector.campus | E | - |
dc.sector.daehak | 과학기술융합대학 | - |
dc.sector.department | 수리데이터사이언스학과 | - |
dc.identifier.pid | dgkim | - |
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