Confidence sets based on the positive part James–Stein estimator with the asymptotically constant coverage probability
Syed Ejaz Ahmed, Iskander Amirovich Kareev, Sujitta Suraphee, Andrei Igorevich Volodin, Игорь Николаевич Володин · Journal of Statistical Computation and Simulation · 2014
The asymptotic expansions for the coverage probability of a confidence set centred at the James–Stein estimator presented in our previous publications show that this probability depends on the non-centrality parameter τ2 (the sum of the squares of the means of normal distributions). In this paper we establish how these expansions can be used for a construction of confidence region with constant confidence level, which is asymptotically (the same formula for both case τ→0 and τ→∞) equal to some fixed value 1−α. We establish the shrinkage rate for the confidence region according to the growth of the dimension p and also the value of τ for which we observe quick decreasing of the coverage probability to the nominal level 1−α. When p→∞ this value of τ increases as O(p1/4). The accuracy of the results obtained is shown by the Monte-Carlo statistical simulations.