The highest confidence density region and its usage for joint inferences about constrained parameters.
Suppose that we are interested in making joint inferences about a set of constrained parameters. Confidence regions for these parameters are often constructed via a normal approximation of the distribution of a consistent estimator for a transformation of the parameters. In this article, we utilize the confidence distribution, a frequentist counterpart to the posterior distribution in Bayesian statistics, to obtain optimal confidence regions for the parameters. Members of such a region can be generated efficiently via a standard Markov chain Monte Carlo algorithm. We then apply this technique to draw inferences about the temporal profile of the survival function with censored observations. We illustrate the new proposal with the survival data from the well-known Mayo primary biliary cirrhosis study and show that the volume of the new 0.95 confidence region is only one thirty-fourth of that of the conventional confidence band.
Tian
Lu
L
Department of Health Policy and Research, Stanford University School of Medicine, Stanford, California 94305, USA. lutian@stanford.edu
Wang
Rui
R
Cai
Tianxi
T
Wei
Lee-Jen
LJ
D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D016001:Confidence Intervals; D008105:Liver Cirrhosis, Biliary / Q000401:mortality; D008390:Markov Chains; D009010:Monte Carlo Method; D016008:Statistical Distributions; D016019:Survival Analysis