Full information maximum likelihood estimation in factor analysis with a large number of missing values

Kei Hirose, Sunyong Kim, Yutaka Kano, Miyuki Imada, Manabu Yoshida, Masato Matsuo

研究成果: Contribution to journalArticle査読

3 被引用数 (Scopus)

抄録

We consider the problem of full information maximum likelihood (FIML) estimation in factor analysis when a majority of the data values are missing. The expectation–maximization (EM) algorithm is often used to find the FIML estimates, in which the missing values on manifest variables are included in complete data. However, the ordinary EM algorithm has an extremely high computational cost. In this paper, we propose a new algorithm that is based on the EM algorithm but that efficiently computes the FIML estimates. A significant improvement in the computational speed is realized by not treating the missing values on manifest variables as a part of complete data. When there are many missing data values, it is not clear if the FIML procedure can achieve good estimation accuracy. In order to investigate this, we conduct Monte Carlo simulations under a wide variety of sample sizes.

本文言語英語
ページ(範囲)91-104
ページ数14
ジャーナルJournal of Statistical Computation and Simulation
86
1
DOI
出版ステータス出版済み - 1 2 2016
外部発表はい

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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