Rand R. Wilcox
University of Southern California
425 Papers
2.4K Citations
Rand R. Wilcox is an academic researcher from University of Southern California. The author has contributed to research in topics: Heteroscedasticity & Estimator. The author has an hindex of 48, co-authored 399 publications. Previous affiliations of Rand R. Wilcox include University of California, Los Angeles & University of Cincinnati.
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Papers
•Book
Introduction to Robust Estimation and Hypothesis Testing
Rand R. Wilcox
- 07 Apr 1997
TL;DR: In this paper, the authors present a foundation for robust regression methods for estimating measures of location and scale, including confidence intervals in the one-sample case, and the correlation and tests of independence.
2.2K
Robust statistical methods in R using the WRS2 package.
Patrick Mair,Rand R. Wilcox +1 more
TL;DR: The R package WRS2 is introduced that implements various robust statistical methods by introducing robust location, dispersion, and correlation measures, and robust ANCOVA as well as robust mediation models are introduced.
Robust Correlation Analyses: False Positive and Power Validation Using a New Open Source Matlab Toolbox
TL;DR: A free Matlab(R) based toolbox is described that computes robust measures of association between two or more random variables: the percentage-bend correlation and skipped-correlations and shows that robust methods provide better estimates of the true association with accurate false positive control and without loss of power.
•Book
Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy
Rand R. Wilcox
- 13 Apr 2011
TL;DR: In this article, the Genesis of a Science is discussed and a method for promoting normality is proposed to deal with the problem of non-normality in a small sample set.
543
Modern robust data analysis methods: measures of central tendency.
Rand R. Wilcox,H. J. Keselman +1 more
TL;DR: Some of the more fundamental problems with conventional methods based on means are reviewed; some indication of why recent advances, based on robust measures of location, have practical value are provided; and why modern investigations dealing with nonnormality find practical problems when comparing means.