Improved family-wise error rate control in multiple equivalence testing

Leday, Gwenaël G. R.; Hemerik, Jesse; Engel, Jasper; Voet, Hilko van der

doi:10.1016/j.fct.2023.113928

Cited by 2 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Normality Assumptionmentioning

confidence: 99%

“…The FWER is the probability of one or more type I errors occurring in a family of tests under the null hypothesis, and the FDR is the expected proportion of the ratio: number of falsepositive tests to the number of tests with the null hypothesis rejected [47]. There are a variety of methods for controlling FWER (Bonferroni, Holm, Hochberg, Hommel, and adaptive Bonferroni) and FDR (two-stage linear set-up procedure of Benjamini, Krieger and Yekutieli, Benjamini and Hochberg, and Storey Tibshirani) [48][49][50]. The most representative methods for FWER and FDR are Bonferroni and Benjamini-Hochberg, BH correction method: Sorting the p-values in ascending order, ranking the p-values (the smallest p-value has rank 1), calculating the critical BH value for each p-value using the formula (i/m)Q, where i is the rank of a particular p-value, m is the total number of tests, and Q is the false discovery rate chosen by the user.…”

Section: Normality Assumptionmentioning

confidence: 99%

See 1 more Smart Citation

Broken Rotor Bar Detection Based on Steady-State Stray Flux Signals Using Triaxial Sensor with Random Positioning

Zubčić,

Pavić,

Matić

et al. 2024

Sensors

View full text Add to dashboard Cite

This paper investigates the detection of broken rotor bar in squirrel cage induction motors using a novel approach of randomly positioning a triaxial sensor over the motor surface. This study is conducted on two motors under laboratory conditions, where one motor is kept in a healthy state, and the other is subjected to a broken rotor bar (BRB) fault. The induced electromotive force of the triaxial coils, recorded over ten days with 100 measurements per day, is statistically analyzed. Normality tests and graphical interpretation methods are used to evaluate the data distribution. Parametric and non-parametric approaches are used to analyze the data. Both approaches show that the measurement method is valid and consistent over time and statistically distinguishes healthy motors from those with BRB defects when a reference or threshold value is specified. While the comparison between healthy motors shows a discrepancy, the quantitative analysis shows a smaller estimated difference in mean values between healthy motors than comparing healthy and BRB motors.

show abstract

Section: Normality Assumptionmentioning

confidence: 99%

Section: Normality Assumptionmentioning

confidence: 99%

Broken Rotor Bar Detection Based on Steady-State Stray Flux Signals Using Triaxial Sensor with Random Positioning

Zubčić,

Pavić,

Matić

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Multiple testing of interval composite null hypotheses using randomized p-values

Ochieng

2024

Stat Papers

View full text Add to dashboard Cite

Equivalence tests are statistical hypothesis testing procedures that aim to establish practical equivalence rather than the usual statistical significant difference. These testing procedures are frequent in “bioequivalence studies," where one would wish to show that, for example, an existing drug and a new one under development have comparable therapeutic effects. In this article, we propose a two-stage randomized (RAND2) p-value that depends on a uniformly most powerful (UMP) p-value and an arbitrary tuning parameter $$c\in [0,1]$$ c ∈ [ 0 , 1 ] for testing an interval composite null hypothesis. We investigate the behavior of the distribution function of the two p-values under the null hypothesis and alternative hypothesis for a fixed significance level $$t\in (0,1)$$ t ∈ ( 0 , 1 ) and varying sample sizes. We evaluate the performance of the two p-values in estimating the proportion of true null hypotheses in multiple testing. We conduct a family-wise error rate control using an adaptive Bonferroni procedure with a plug-in estimator to account for the multiplicity that arises from our multiple hypotheses under consideration. The various claims in this research are verified using a simulation study and real-world data analysis.

show abstract

Improved family-wise error rate control in multiple equivalence testing

Cited by 2 publications

References 26 publications

Broken Rotor Bar Detection Based on Steady-State Stray Flux Signals Using Triaxial Sensor with Random Positioning

Broken Rotor Bar Detection Based on Steady-State Stray Flux Signals Using Triaxial Sensor with Random Positioning

Multiple testing of interval composite null hypotheses using randomized p-values

Contact Info

Product

Resources

About