Multiple-response repeated measurement or multivariate growth curve model with distribution-free errors

Xin Xin and Feng Qiu

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 24, 12370-12386

Abstract: In this article, we propose a new modeling approach for the multivariate growth curve model with distribution-free errors, which is a useful tool for analyzing multiple-response repeated measurements. We first use the outer product least-squares technique to directly estimate covariance and then explore the feasible generalized least-squares technique to derive the estimator of regression coefficients. Large-sample properties are investigated for these estimators. Moreover, the above estimations for covariance and regression coefficients are extended to the situation under certain null hypothesis tests and the best subset BIC is used for variable selection. A real dataset is analyzed to demonstrate the usefulness and competency of the proposed methodology for model specification (identification) and model fitting (parameter estimation) in multiple-response repeated measurements.

Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2017.1300273 (text/html)
Access to full text is restricted to subscribers.

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:taf:lstaxx:v:46:y:2017:i:24:p:12370-12386

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2017.1300273

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().