论文信息 - A Parametric Bootstrap Approach for Testing Equality of Inverse Gaussian Means Under Heterogeneity

A Parametric Bootstrap Approach for Testing Equality of Inverse Gaussian Means Under Heterogeneity

The inverse Gaussian distribution provides a flexible model for analyzing positive, right-skewed data. The generalized variable test for equality of several inverse Gaussian means with unknown and arbitrary variances has satisfactory Type-I error rate when the number of samples (k) is small (Tian, 2006). However, the Type-I error rate tends to be inflated when k goes up. In this article, we propose a parametric bootstrap (PB) approach for this problem. Simulation results show that the proposed test performs very satisfactorily regardless of the number of samples and sample sizes. This method is illustrated by an example.

Chang-Xing Ma | Lili Tian | Lili Tian | Chang-Xing Ma

[1] E. Ziegel,et al. Bootstrapping: A Nonparametric Approach to Statistical Inference , 1993 .

[2] G. Whitmore,et al. The inverse Gaussian distribution as a model of hospital stay. , 1975, Health services research.

[3] G. S. Mudholkar,et al. The Inverse Gaussian Models: Analogues of Symmetry, Skewness and Kurtosis , 2002 .

[4] Samaradasa Weerahandi,et al. Generalized Confidence Intervals , 1993 .

[5] Kam-Wah Tsui,et al. Generalized p-Values in Significance Testing of Hypotheses in the Presence of Nuisance Parameters , 1989 .

[6] Lili Tian. Testing equality of inverse Gaussian means under heterogeneity, based on generalized test variable , 2006, Comput. Stat. Data Anal..

[7] Shinji Kumagai,et al. Application of inverse Gaussian distribution to occupational exposure data , 1997 .

[8] M. Kenward,et al. An Introduction to the Bootstrap , 2007 .

[9] K. Krishnamoorthy,et al. A parametric bootstrap approach for ANOVA with unequal variances: Fixed and random models , 2007, Comput. Stat. Data Anal..