Resampling-based methods for biologists

Resampling Methods for Sample Surveys

Application of resampling methods in sample survey settings presents considerable practical and conceptual difficulties. Various potential solutions have recently been proffered in the statistical literature. This paper provides a brief critical review of these methods. Our main conclusion is that, while resampling methods may be useful in some problems, there is little evidence of their useful...

Resampling Methods in Complex Survey Data

islanding detection methods for microgrids

امروزه استفاده از منابع انرژی پراکنده کاربرد وسیعی یافته است . اگر چه این منابع بسیاری از مشکلات شبکه را حل می کنند اما زیاد شدن آنها مسائل فراوانی برای سیستم قدرت به همراه دارد . استفاده از میکروشبکه راه حلی است که علاوه بر استفاده از مزایای منابع انرژی پراکنده برخی از مشکلات ایجاد شده توسط آنها را نیز منتفی می کند . همچنین میکروشبکه ها کیفیت برق و قابلیت اطمینان تامین انرژی مشترکان را افزایش ...

Robust Resampling Methods for Time Series∗

We study the robustness of block resampling procedures for time series. We first derive a set of formulas to characterize their quantile breakdown point. For the moving block bootstrap and the subsampling, we find a very low quantile breakdown point. A similar robustness problem arises in relation to data-driven methods for selecting the block size in applications. This renders inference based ...

Hybrid Resampling Methods for Confidence Intervals

This paper considers the problem of constructing confidence intervals for a single parameter θ in a multiparameter or nonparametric family. Hybrid resampling methods, which “hybridize” the essential features of bootstrap and exact methods, are proposed and developed for both parametric and nonparametric situations. In particular, we apply such methods to construct confidence regions, whose cove...