Maximum likelihood estimation in covariance structure analysis with truncated data

Br J Math Stat Psychol. 1997 Nov:50 ( Pt 2):339-49. doi: 10.1111/j.2044-8317.1997.tb01149.x.

Abstract

We study an estimation procedure for maximum likelihood estimation in covariance structure analysis with truncated data, and obtain the statistical properties of the estimator as well as a test of the model structure. Truncated data with and without knowledge about the number of unmeasured observations are both considered. The Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, which requires only first derivatives, is proposed to obtain the maximum likelihood estimates. We illustrate the statistics and parameter estimates by a fictitious example. The maximum likelihood method is compared to an alternative two-stage method.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Algorithms
  • Analysis of Variance*
  • Bias
  • Data Interpretation, Statistical*
  • Humans
  • Likelihood Functions*
  • Models, Statistical