10 citations · 19 across the 6 of their papers we have counts for
6 papers
Significance testing without truth
William Perkins, Mark Tygert, Rachel Ward
A popular approach to significance testing proposes to decide whether the given hypothesized statistical model is likely to be true (or false). Statistical decision theory provides…
Computing the asymptotic power of a Euclidean-distance test for goodness-of-fit
William Perkins, Gary Simon, Mark Tygert
A natural (yet unconventional) test for goodness-of-fit measures the discrepancy between the model and empirical distributions via their Euclidean distance (or, equivalently, via i…
An introduction to how chi-square and classical exact tests often wildly misreport significance and how the remedy lies in computers
William Perkins, Mark Tygert, Rachel Ward
Goodness-of-fit tests based on the Euclidean distance often outperform chi-square and other classical tests (including the standard exact tests) by at least an order of magnitude w…
Chi-square and classical exact tests often wildly misreport significance; the remedy lies in computers
William Perkins, Mark Tygert, Rachel Ward
If a discrete probability distribution in a model being tested for goodness-of-fit is not close to uniform, then forming the Pearson chi-square statistic can involve division by ne…
Computing the confidence levels for a root-mean-square test of goodness-of-fit, II
William Perkins, Mark Tygert, Rachel Ward
This paper extends our earlier article, "Computing the confidence levels for a root-mean-square test of goodness-of-fit;" unlike in the earlier article, the models in the present p…
Computing the confidence levels for a root-mean-square test of goodness-of-fit
William Perkins, Mark Tygert, Rachel Ward
The classic chi-squared statistic for testing goodness-of-fit has long been a cornerstone of modern statistical practice. The statistic consists of a sum in which each summand invo…