SINGLE-FACTOR ANALYSIS BY MINIMUM MESSAGE LENGTH ESTIMATION
成果类型：
Article
署名作者：
WALLACE, CS; FREEMAN, PR
署名单位：
University of Leicester
刊物名称：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
ISSN/ISSBN：
1369-7412
发表日期：
1992
页码：
195-209
关键词：
摘要：
The minimum message length (MML) technique is applied to the problem of estimating the parameters of a multivariate Gaussian model in which the correlation structure is modelled by a single common factor. Implicit estimator equations are derived and compared with those obtained from a maximum likelihood (ML) analysis. Unlike ML, the MML estimators remain consistent when used to estimate both the factor loadings and the factor scores. Tests on simulated data show the MML estimates to be on average more accurate than the ML estimates when the former exist. If the data show little evidence for a factor, the MML estimate collapses. It is shown that the condition for the existence of an MML estimate is essentially that the log-likelihood ratio in favour of the factor model exceeds the value expected under the null (no-factor) hypotheses.