Asset Details
Do you wish to reserve the book?
Empirical investigations into Kruskal-Wallis power studies utilizing Bernstein fits, simulations and medical study datasets
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Empirical investigations into Kruskal-Wallis power studies utilizing Bernstein fits, simulations and medical study datasets
Do you wish to request the book?
Empirical investigations into Kruskal-Wallis power studies utilizing Bernstein fits, simulations and medical study datasets
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Empirical investigations into Kruskal-Wallis power studies utilizing Bernstein fits, simulations and medical study datasets
2023
Overview
Bernstein fits implemented into R allow another route for Kruskal-Wallis power-study tool development. Monte-Carlo Kruskal-Wallis power studies were compared with measured power, a Monte-Carlo ANOVA equivalent and with an analytical method, with or without normalization, using four simulated runs, each with 60–100 populations (each population with N = 30,000 from a set of Pearson-type ranges): random selection gave 6300 samples analyzed for predictive power. Three medical-study datasets (Dialysis/systolic blood pressure; Diabetes/sleep-hours; Marital-status/high-density-lipoprotein cholesterol) were also analyzed. In three from four simulated runs (run_one, run_one_relaxed, and run_three) with Pearson types pooled, Monte-Carlo Kruskal-Wallis gave predicted sample sizes significantly slightly lower than measured but more accurate than with ANOVA methods; the latter gave high sample-size predictions. Populations (run_one_relaxed) with ANOVA assumptions invalid gave Kruskal-Wallis predictions similar to those measured. In two from three medical studies, Kruskal-Wallis predictions (Dialysis: similar predictions; Marital: higher than measured) were more accurate than ANOVA (both higher than measured) but in one (Diabetes) the reverse was found (Kruskal-Wallis: lower; Monte-Carlo ANOVA: similar to measured). These preliminary studies appear to show that Monte-Carlo Kruskal-Wallis power studies, based on Bernstein fits, might perform better than ANOVA equivalents in many settings (and provide reasonable results when ANOVA cannot be used); and both Monte-Carlo methods appeared to be considerably more accurate than the analytical version analyzed.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
