Author(s)

L. Jiang, A. Wong

Department of Mathematics and Statistics, York University, Toronto, Canada.

Fisher [1] proposed a simple method to combine p-values from independent investigations without using detailed information of the original data. In recent years, likelihood-based asymptotic methods have been developed to produce highly accurate p-values. These likelihood-based methods generally required the likelihood function and the standardized maximum likelihood estimates departure calculated in the canonical parameter scale. In this paper, a method is proposed to obtain a p-value by combining the likelihood functions and the standardized maximum likelihood estimates departure of independent investigations for testing a scalar parameter of interest. Examples are presented to illustrate the application of the proposed method and simulation studies are performed to compare the accuracy of the proposed method with Fisher’s method.

Canonical Parameter, Fisher’s Expected Information, Modified Signed Log-Likelihood Ratio Statistic, Standardized Maximum Likelihood Estimate Departure

Jiang, L. and Wong, A. (2015) Combining Likelihood Information from Independent Investigations. Open Journal of Statistics, 5, 51-59. doi: 10.4236/ojs.2015.51007.