Combining two classical approaches for statistical selection

IF 0.7 4区 物理与天体物理 Q3 COMPUTER SCIENCE, THEORY & METHODS
J. Verheijen, F. Coolen, V. D. Laan
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引用次数: 13

Abstract

This paper presents a selection procedure that combines Bechhofer's indifference zone selection and Gupta's subset selection approaches, by using a preference threshold. For normal populations with common known variance, a subset is selected of all populations that have sample sums within the distance of this threshold from the largest sample sum. We derive the minimal necessary sample size and the value for the preference threshold, in order to satisfy two probability requirements for correct selection, one related to indifference zone selection, the other to subset selection. Simulation studies are used to illustrate the method.
结合两种经典的统计选择方法
本文通过使用偏好阈值,提出了一种结合Bechhofer的无差异区选择方法和Gupta的子集选择方法的选择过程。对于方差已知的正态总体,从样本和在最大样本和的阈值范围内的所有总体中选择一个子集。为了满足正确选择的两个概率要求,一个与无差异区选择有关,另一个与子集选择有关,我们导出了最小必要样本量和偏好阈值。仿真研究说明了该方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Quantum Information & Computation
Quantum Information & Computation 物理-计算机:理论方法
CiteScore
1.70
自引率
0.00%
发文量
42
审稿时长
3.3 months
期刊介绍: Quantum Information & Computation provides a forum for distribution of information in all areas of quantum information processing. Original articles, survey articles, reviews, tutorials, perspectives, and correspondences are all welcome. Computer science, physics and mathematics are covered. Both theory and experiments are included. Illustrative subjects include quantum algorithms, quantum information theory, quantum complexity theory, quantum cryptology, quantum communication and measurements, proposals and experiments on the implementation of quantum computation, communications, and entanglement in all areas of science including ion traps, cavity QED, photons, nuclear magnetic resonance, and solid-state proposals.
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