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

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)91-104
Number of pages14
JournalJournal of Statistical Computation and Simulation
Volume86
Issue number1
DOIs
Publication statusPublished - Jan 2 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

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

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