Efficient designs for multivariate crossover trials

Shubham Niphadkar () and Siuli Mukhopadhyay ()
Additional contact information
Shubham Niphadkar: Indian Institute of Technology Bombay
Siuli Mukhopadhyay: Indian Institute of Technology Bombay

Metrika: International Journal for Theoretical and Applied Statistics, 2025, vol. 88, issue 6, No 20, 1227-1255

Abstract: Abstract This article aims to study efficient designs for crossover trials with multiple responses recorded from each subject in the time periods. A multivariate fixed effects model is proposed with direct and carryover effects corresponding to the multiple responses. The corresponding error dispersion matrix is chosen to be either of the proportional or the generalized Markov covariance type, permitting the existence of direct and cross-correlations within and between the multiple responses. The corresponding information matrices for direct effects under the two types of dispersions are used to determine efficient designs. The efficiency of orthogonal array designs of type I and strength 2 is investigated for a wide choice of covariance functions, namely, Mat(0.5), Mat(1.5) and Mat( $$\infty $$ ). To motivate these multivariate crossover designs, a gene expression dataset in a $$3 \times 3$$ framework is utilized.

Keywords: Completely symmetric; Correlated response; Markov-type covariance; Orthogonal arrays; Proportional covariance (search for similar items in EconPapers)
JEL-codes: C30 C90 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00184-025-01003-4 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:metrik:v:88:y:2025:i:6:d:10.1007_s00184-025-01003-4

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/184/PS2

DOI: 10.1007/s00184-025-01003-4

Access Statistics for this article

Metrika: International Journal for Theoretical and Applied Statistics is currently edited by U. Kamps and Norbert Henze

More articles in Metrika: International Journal for Theoretical and Applied Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().