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

Bootstrap algorithms for risk models with auxiliary variable and complex samples / G. Manzi, F. Mecatti. - In: METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY. - ISSN 1387-5841. - 11:1(2009 Mar), pp. 21-27.

Bootstrap algorithms for risk models with auxiliary variable and complex samples

G. Manzi
Primo
;
2009

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
Probability-proportional-to-size sampling ; Ratio model ; Ratio estimator ; Regression estimator ; Resampling methods
Settore SECS-S/01 - Statistica
mar-2009
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/55124
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