The generalized inference on the sign testing problem about the normal variances

Wu, Wei; Wu, Wei-Hwa; Hsieh, Hsin-Neng; Lee, Meng-Chih

doi:10.1080/02664763.2017.1325857

Cited by 1 publication

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subsequently, Weerahandi [11] provided the generalized pivotal quantities (GPQs) to construct the generalized confidence intervals (GCIs) of specific parameters which are contained the nuisance parameters. The GPVs has been successfully applied to various hypothesis testing topics, such as in research comparing accuracy by examining receiver operating characteristic curves in gold standard situations [12,13], research on the tolerance interval for random effects models [14,15], research on evaluation of dissolution profile similarity [16], research on GCIs in a linear measurement error model [17], research comparing accuracy by examining receiver operating characteristic curves when a gold standard situation does not exist [18], and research applying a delta-lognormal distribution to trawl survey data [19], and applying the generalized inference on the sign testing problem about the normal variance [20], etc.…”

Section: Introductionmentioning

confidence: 99%

The generalized inference on the ratio of mean differences for fraction retention noninferiority hypothesis

Hsieh¹,

Lu²

2020

PLoS ONE

Self Cite

View full text Add to dashboard Cite

The fraction retention non-inferiority hypothesis is often measured for the ratio of the effects of a new treatment to those of the control in medical research. However, the fraction retention non-inferiority test that the new treatment maintains the efficacy of control can be affected by the nuisance parameters. Herein, a heuristic procedure for testing the fraction retention non-inferiority hypothesis is proposed based on the generalized p-value (GPV) under normality assumption and heteroskedasticity. Through the simulation study, it is demonstrated that, the performance of the GPV-based method not only adequately controls the type I error rate at the nominal level but also is uniformly more powerful than the ratio test, Rothmann's and Wang's tests, the comparable extant methods. Finally, we illustrate the proposed method by employing a real example.

show abstract