통계연구소에서는 다음과 같이 통계 세미나를 개최하오니 많은 참여 바랍니다.
일시: 2011년 6월 29일(수) 오후 5시
장소: 고려대학교 정경관 509A호
연사: Yeojin Chung
(Post-doctoral Scholar,
Graduate School of Education University of California, Berkeley)
Likelihood-tuned Density Estimator
and Its Application to Clustering
<Abstract>
Nonparametric density estimation is widely used for investigating underlying features of data. We introduce a likelihood enhanced nonparametric density estimator which arises from treating the kernel density estimator as an element of the model that consists of all mixtures of the kernel, continuous or discrete. One can obtain the kernel density estimator with “likelihood-tuning” by using the uniform density as the starting value in an EM algorithm. We prove algorithmic convergence of this EM algorithm to the nonparametric mixture maximum likelihood estimator. The second tuning step leads to a fitted density with higher likelihood than the kernel density estimator. This twice tuned density estimator reduces the bias of the kernel density estimator while the order of variance stays the same. Our simulation study shows that the second-tuned estimator performed robustly against the type of densities.