Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/71337
標題: Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms
作者: Ho, H.J.
Pyne, S.
Lin, T.I.
關鍵字: ECM algorithm
ECME algorithm
Flow cytometry
Outliers
ST mixtures
STN mixtures
density-estimation
model
ecm
期刊/報告no:: Statistics and Computing, Volume 22, Issue 1, Page(s) 287-299.
摘要: This paper deals with the problem of maximum likelihood estimation for a mixture of skew Student-t-normal distributions, which is a novel model-based tool for clustering heterogeneous (multiple groups) data in the presence of skewed and heavy-tailed outcomes. We present two analytically simple EM-type algorithms for iteratively computing the maximum likelihood estimates. The observed information matrix is derived for obtaining the asymptotic standard errors of parameter estimates. A small simulation study is conducted to demonstrate the superiority of the skew Student-t-normal distribution compared to the skew t distribution. The proposed methodology is particularly useful for analyzing multimodal asymmetric data as produced by major biotechnological platforms like flow cytometry. We provide such an application with the help of an illustrative example.
URI: http://hdl.handle.net/11455/71337
ISSN: 0960-3174
文章連結: http://dx.doi.org/10.1007/s11222-010-9225-9
Appears in Collections:期刊論文

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.