Sisvar: a Guide for its Bootstrap procedures in multiple comparisons
By Daniel Furtado Ferreira. 2014
1,432 citations recorded by the source corpus.
Sisvar is a statistical analysis system with a large usage by the scientific community to produce statistical analyses and to produce scientific results and conclusions. The large use of the statistical procedures of Sisvar by the scientific community is due to it being accurate, precise, simple and robust. With many options of analysis, Sisvar has a not so largely used analysis that is the multiple comparison procedures using bootstrap approaches. This paper aims to review this subject and to show some advantages of using Sisvar to perform such analysis to compare treatments means. Tests like Dunnett, Tukey, Student-Newman-Keuls and Scott-Knott are performed alternatively by bootstrap methods and show greater power and better controls of experimentwise type I error rates under non-normal, asymmetric, platykurtic or leptokurtic distributions.
Subfields: Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/c2e4dec1bb4cf46428e1c9a54a8d404832b307de
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In Management Science and Operations Research
In Statistics and Probability
Citations in the corpus
- Deep Learning Based Vulnerability Detection: Are We There Yet? (2020, result): “differ merely by random noises. Effect sizes tells us whether two series of samples differ by more than just a trivial amount. To assert statistically sound comparisons, following previous approaches [67]–[71], we use a non-parametric bootstrap hypothesis test [72] in conjunction with the A12 effect”
- SISVAR: A COMPUTER ANALYSIS SYSTEM TO FIXED EFFECTS SPLIT PLOT TYPE DESIGNS (2019, background): “As Sisvar has being developed by only one researcher, it took approximately two and a half years to have the first version (FERREIRA, 2011, 2014).”