Randomization-based statistical inference: A resampling and simulation infrastructure

被引：1

作者：

Dinov, Ivo D. ^{[1
,2
,3
]}

Palanimalai, Selvam ^{[1
]}

Khare, Ashwini ^{[1
]}

Christou, Nicolas ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Stat Online Computat Resource, Los Angeles, CA 90095 USA

[2] Univ Michigan, UMSN, Stat Online Computat Resource, Ann Arbor, MI 48109 USA

[3] Univ Michigan, Michigan Inst Data Sci, Ann Arbor, MI 48109 USA

来源：

TEACHING STATISTICS | 2018年 / 40卷 / 02期

关键词：

Resampling; Simulation; Statistical inference; Randomization; Bootstrapping; Statistics Online Computational Resource (SOCR);

D O I：

10.1111/test.12156

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

Statistical inference involves drawing scientifically-based conclusions describing natural processes or observable phenomena from datasets with intrinsic random variation. We designed, implemented, and validated a new portable randomization-based statistical inference infrastructure (http://socr.umich.edu/HTML5/Resampling_Webapp) that blends research-driven data analytics and interactive learning, and provides a backend computational library for managing large amounts of simulated or user-provided data.

引用

页码：64 / 73

页数：10

共 33 条

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