DEVELOPMENT OF A NOVEL SELECTION CRITERION FOR OPTIMUM CHOICE OF “m”IN THE “m out of n” BOOTSTRAP
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Keywords
Bootstrap, Optimization, Simulation, resampling methods, consistency
Abstract
Abstract
Efron (1979) introduced the n-out-of-n bootstrap, which is indeed an important tool for statistical inference and has wide spread applications. However, there are situations, where the n-out-of-n bootstrap is not consistent. Thus, the m-out-of-n bootstrap was introduced to overcome the problem. It reduces the computational burden associated with bootstrapping. But, the problem with m-out-of-n bootstrap is the choice of m, which is one of the important aspects in bootstrapping. In this paper, we study criteria for choosing best value of m in m-out-of-n bootstrapping in linear regression. This is a pure computational study that gives general criteria for optimizing m in m-out of-n bootstrap, under which the chosen m ( ) behaves properly.
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Inayat Ullah
Department of Statistics, University of Peshawar
Alamgir
Department of Statistics, University of Peshawar
Shahid Iqbal
Center for Disaster Preparedness and Management, University of Peshawar