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Abstract:
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Semiparametric modeling has provided an excellent framework for the modern complex data due to its flexibility to model some features of the data parametrically but without assuming anything for the other features. The bootstrap is the most popular data-resampling method used in statistical analysis, and has recently been applied to the semiparametric models arising from a wide variety of contexts. Hence, the first focus of this talk is to prove the theoretical validity of the bootstrap method as a general inferential tool for the semiparametric models. In practice, the computational cost of the bootstrap inference procedure is particularly high for the semiparametric models. Thus, we propose an approximate bootstrap method, i.e. k-step bootstrap, and will show that this novel approach results in huge computational savings but without sacrificing any degree of inference accuracy.
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