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Explicit influence analysis in two-treatment balanced crossover models

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Abstract

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.

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Author information

Authors and Affiliations

  1. Dept. Statist., Stockholm Univ., Stockholm, Sweden

    C. Hao & T. von Rosen

  2. Dept. Automation, Shanghai Jiao Tong Univ., Shanghai, China

    C. Hao

  3. Dept. Energy and Technol., Swedish Univ. Agricultural Sci., Uppsala, Sweden

    D. von Rosen

  4. Dept. Math., Linköping University, Linköping, Sweden

    D. von Rosen

Authors
  1. C. Hao
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  2. D. von Rosen
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  3. T. von Rosen
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Corresponding author

Correspondence to C. Hao.

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Hao, C., von Rosen, D. & von Rosen, T. Explicit influence analysis in two-treatment balanced crossover models. Math. Meth. Stat. 24, 16–36 (2015). https://doi.org/10.3103/S1066530715010020

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  • DOI: https://doi.org/10.3103/S1066530715010020

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