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Robustness of resampling-based error rate estimators in two class discrimination under non-normal population

机译:非正常人群下两类歧视中基于重采样的错误率估计器的鲁棒性

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摘要

This paper numerically investigates robustness of resampling-based error rate estimators to kurtosis when Fisher's linear discriminant function is used. In order to control the population kurtosis and to examine the robustness of estimators, we assume Pearson's type VII and exponential power distributions as a population distribution. The robustness study is carried out for several resampling-based estimators based on graphical and quantitative approaches.
机译:本文使用Fisher线性判别函数对基于重采样的误差率估计值对峰度的鲁棒性进行了数值研究。为了控制人口峰度并检验估计量的稳健性,我们假设Pearson的VII型和指数幂分布为人口分布。基于图形和定量方法,对几种基于重采样的估计量进行了稳健性研究。

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  • 作者单位

    Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan;

    Research Division, National Center for University Entrance Examinations, 2-19-23 Komaba, Meguro-ku, Tokyo 153-8501, Japan;

    Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan;

    Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    error rate; robustness; resampling-based estimator; non-normality;

    机译:错误率健壮性基于重采样的估计器;非正态;
  • 入库时间 2022-08-17 13:50:25

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