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Bootstrap Algorithms for Risk Models with Auxiliary Variable and Complex Samples

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Abstract

Resampling methods are often invoked in risk modelling when the stability of estimators of model parameters has to be assessed. The accuracy of variance estimates is crucial since the operational risk management affects strategies, decisions and policies. However, auxiliary variables and the complexity of the sampling design are seldom taken into proper account in variance estimation. In this paper bootstrap algorithms for finite population sampling are proposed in presence of an auxiliary variable and of complex samples. Results from a simulation study exploring the empirical performance of some bootstrap algorithms are presented.

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Authors and Affiliations

  1. Department of Economics, Business and Statistics, University of Milan, Milan, Italy

    Giancarlo Manzi

  2. Department of Statistics, University of Milan–Bicocca, Milan, Italy

    Fulvia Mecatti

Authors
  1. Giancarlo Manzi
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  2. Fulvia Mecatti
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Correspondence to Fulvia Mecatti.

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Manzi, G., Mecatti, F. Bootstrap Algorithms for Risk Models with Auxiliary Variable and Complex Samples. Methodol Comput Appl Probab 11, 21–27 (2009). https://doi.org/10.1007/s11009-008-9072-8

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  • DOI: https://doi.org/10.1007/s11009-008-9072-8

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