Raw Data Maximum Likelihood Estimation for Common Principal Component Models: A State Space Approach
Fei Gu () and
Hao Wu ()
Additional contact information
Fei Gu: McGill University
Hao Wu: Boston College
Psychometrika, 2016, vol. 81, issue 3, No 8, 773 pages
Abstract:
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 model; principal component analysis; common principal component model (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s11336-016-9504-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:psycho:v:81:y:2016:i:3:d:10.1007_s11336-016-9504-2
Ordering information: This journal article can be ordered from
http://www.springer. ... gy/journal/11336/PS2
DOI: 10.1007/s11336-016-9504-2
Access Statistics for this article
Psychometrika is currently edited by Irini Moustaki
More articles in Psychometrika from Springer, The Psychometric Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().