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Explicit influence analysis in two-treatment balanced crossover models
Dept. Statist., Stockholm Univ., Stockholm, Sweden; Dept. Automation, Shanghai Jiao Tong Univ., Shanghai, China.
Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, Faculty of Science & Engineering. Dept. Energy and Technol., Swedish Univ. Agricultural Sci., Uppsala, Sweden.
Dept. Statist.Stockholm Univ., Stockholm, Sweden.
2015 (English)In: Mathematical Methods of Statistics, ISSN 1066-5307, E-ISSN 1934-8045, Vol. 24, no 1, p. 16-36Article in journal (Refereed) Published
Abstract [en]

This paper considers how to detect influential observations in crossover models with random individual effects. Two influence measures, the delta-beta influence and variance-ratio influence, are utilized as tools to evaluate the influence of the model on the estimates of mean and variance parameters with respect to case-weighted perturbations, which are introduced to the model for studying the ‘influence’ of cases. The paper provides explicit expressions of the delta-beta and variance-ratio influences for the general two-treatment balanced crossover models when the proposed decompositions for the perturbed models hold. The influence measures for each parameter turn out to be closed-form functions of orthogonal projections of specific residuals in the unperturbed model.

Place, publisher, year, edition, pages
New York, NY, United States: Allerton Press, Inc. , 2015. Vol. 24, no 1, p. 16-36
Keywords [en]
delta-beta influence, explicit maximum likelihood estimate, mixed linear model, multiple-period crossover design, perturbation scheme
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-152261DOI: 10.3103/S1066530715010020ISI: 000415027700002Scopus ID: 2-s2.0-84925964736OAI: oai:DiVA.org:liu-152261DiVA, id: diva2:1258260
Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2018-11-02Bibliographically approved

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von Rosen, Dietrich

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  • sv-SE
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