Title of article :
Exponential Models: Approximations for Probabilities
Author/Authors :
Fraser, D. A. S. University of Toronto - Department of Statistics, Canada , Fraser, D. A. S. University of Western Ontario - Department of Statistical and Actuarial Sciences, Canada , Naderi, A. isfahan university of technology - Department of Mathematical Sciences, اصفهان, ايران , Ji, Kexin University of Toronto - Department of Statistics, Canada , Wei, Lin University of Toronto - Department of Statistics, Canada , Jie, Su University of Toronto - Department of Statistics, Canada
Abstract :
Welch Peers (1963) used a root-information prior to obtain posterior probabilities for a scalar parameter exponential model and showed that these Bayes probabilities had the confidence property to second order asymptotically. An important undercurrent of this indicates that the constant information reparameterization provides location model structure, for which the confidence property was and is well known. This paper examines the role of the scalar-parameter exponential model for obtaining approximate probabilities and approximate confidence levels, and then addresses the extension for the vector-parameter exponential model
Keywords :
Approximation , approximate probabilities , asymptotics , exponential model , likelihood , likelihood ratio , maximum likelihood departure , p , value
Journal title :
Journal of the Iranian Statistical Society (JIRSS)
Journal title :
Journal of the Iranian Statistical Society (JIRSS)
