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Local influence for functional comparative calibration models with replicated data

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Abstract

We investigate local influence analysis in functional comparative calibration models with replicated data. A method for selecting appropriate perturbation schemes based on the expected Fisher information matrix with respect to the perturbation vector is proposed. It is shown that arbitrarily perturbing these models may result in misleading inference about the influential subjects. First-order influence measures for identifying the correct influential subjects and replicates on corrected score estimators are defined. We introduce different perturbation schemes including perturbation of subjects and replicates on the corrected likelihood function and obtain the density of the perturbed model from which the methodology is based. Particularly, three perturbation of variances schemes could be a better way to handle badly modeled subjects or replicates. Two real data sets are analyzed to illustrate the use of our local influence measures.

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Authors and Affiliations

  1. Departamento de Matemática, FCEYN, Universidad Nacional de Mar del Plata, Argentina, Funes 3350, 7600, Mar del Plata, Argentina

    Patricia Giménez & María Laura Patat

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  1. Patricia Giménez
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  2. María Laura Patat
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Correspondence to Patricia Giménez.

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Giménez, P., Patat, M.L. Local influence for functional comparative calibration models with replicated data. Stat Papers 55, 431–454 (2014). https://doi.org/10.1007/s00362-012-0489-3

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  • DOI: https://doi.org/10.1007/s00362-012-0489-3

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