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Robustness of parameter estimation procedures in multilevel models when random effects are MEP distributed

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  • Nadia Solaro

    (University of Milan-Bicocca, Milan, Italy)

  • Pier Alda Ferrari

    (Department of Economics, Business and Statistics)

Abstract

In this paper we examine maximum likelihood estimation procedures in multilevel models for two level nesting structures. Usually, for fixed effects and variance components estimation, level-one error terms and random effects are assumed to be normally distributed. Nevertheless, in some circumstances this assumption might not be realistic, especially as concerns random effects. Thus we assume for random effects the family of multivariate exponential power distributions (MEP); subsequently, by means of Monte Carlo simulation procedures, we study robustness of maximum likelihood estimators under normal assumption when, actually, random effects are MEP distributed.

Suggested Citation

  • Nadia Solaro & Pier Alda Ferrari, 2005. "Robustness of parameter estimation procedures in multilevel models when random effects are MEP distributed," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1013, Universitá degli Studi di Milano.
    • Handle: RePEc:bep:unimip:unimi-1013
    Note: oai:cdlib1:unimi-1013
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    1. Lindsey, J. K., 1999. "Models for Repeated Measurements," OUP Catalogue, Oxford University Press, edition 2, number 9780198505594.
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    1. Ho-Chul Park & Yang-Jun Joo & Seung-Young Kho & Dong-Kyu Kim & Byung-Jung Park, 2019. "Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors," Sustainability, MDPI, vol. 11(11), pages 1-17, June.

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