Maximum likelihood estimation in covariance structure analysis with truncated data.

M L Tang; P M Bentler

doi:10.1111/j.2044-8317.1997.tb01149.x

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Maximum likelihood estimation in covariance structure analysis with truncated data.

M L Tang·P M Bentler

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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.

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Algorithms Analysis of Variance Bias Data Interpretation, Statistical Humans Likelihood Functions Models, Statistical Journal Article Research Support, U.S. Gov't, P.H.S.

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M L Tang, P M Bentler. 1997. Maximum likelihood estimation in covariance structure analysis with truncated data.. https://doi.org/10.1111/j.2044-8317.1997.tb01149.x

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