Please use this identifier to cite or link to this item:
|標題:||Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms||作者:||Ho, H.J.
|關鍵字:||ECM algorithm;ECME algorithm;Flow cytometry;Outliers;ST mixtures;STN mixtures;density-estimation;model;ecm||Project:||Statistics and Computing||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.
|Appears in Collections:||期刊論文|
Show full item record
TAIR Related Article
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.