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Raw Data Maximum Likelihood Estimation for Common Principal Component Models: A State Space Approach

Published online by Cambridge University Press:  01 January 2025

Fei Gu  and
Hao Wu
Fei Gu*
Affiliation:
McGill University
Hao Wu
Affiliation:
Boston College
*
Correspondence should bemade to Fei Gu, McGill University,Montreal, Quebec Canada. Email: fei.gu@mcgill.ca
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Abstract

The specifications of state space model for some principal component-related models are described, including the independent-group common principal component (CPC) model, the dependent-group CPC model, and principal component-based multivariate analysis of variance. Some derivations are provided to show the equivalence of the state space approach and the existing Wishart-likelihood approach. For each model, a numeric example is used to illustrate the state space approach. In addition, a simulation study is conducted to evaluate the standard error estimates under the normality and nonnormality conditions. In order to cope with the nonnormality conditions, the robust standard errors are also computed. Finally, other possible applications of the state space approach are discussed at the end.

Keywords

state space modelprincipal component analysiscommon principal component model

Information

Type
Original Paper
Information
Psychometrika , Volume 81 , Issue 3 , September 2016 , pp. 751 - 773
Copyright
Copyright © 2016 The Psychometric Society

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