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Iyer, H.-K. and Paterson, P.-D. (2002) A Recipe for Constructing Generalized Pivotal Quantities and Generalized Con_dence Intervals. Technical Report 2002/10, Department of Statistics, Colorado State University, Fort Collins.

has been cited by the following article:

• TITLE: On Analysis of the Behrens-Fisher Problem Based on Bayesian Evidence

  AUTHORS: Nengak Emmanuel Goltong, Sani Ibrahim Doguwa

  KEYWORDS: Behrens-Fisher Problem, Lindley's Paradox, Metropolis-Hastings Algorithm, Pareto Prior, t Distribution

  JOURNAL NAME: Open Journal of Statistics, Vol.9 No.1, January 21, 2019

  ABSTRACT: In this paper we have demonstrated the ability of the new Bayesian measure of evidence of Yin (2012, Computational Statistics, 27: 237-249) to solve both the Behrens-Fisher problem and Lindley's paradox. We have provided a general proof that for any prior which yields a linear combination of two independent t random variables as posterior distribution of the dierence of means, the new Bayesian measure of evidence given that prior will solve Lindleys' paradox thereby serving as a general proof for the works of Yin and Li (2014, Journal of Applied Mathematics, 2014(978691)) and Goltongand Doguwa (2018, Open Journal of Statistics, 8: 902-914).Using the Pareto prior as an example, we have shown by the use ofsimulation results that the new Bayesian measure of evidence solvesLindley's paradox.