The Annals of Mathematical Statistics

Minimax Estimation of a Random Probability Whose First $N$ Moments are Known

Morris Skibinsky

Full-text: Open access


Let $N$ be a positive integer. In Section 2 an expository account in terms of moment space dependence is given of the Bayes estimate of a random probability $\Theta$, relative to squared difference loss, from an observable $X$ which given $\Theta$ is conditionally binomial $(N, \Theta)$. The risk and Bayes envelope functional are also considered in these terms. In Section 3 an explicit formulation is given for the minimax estimate of $\Theta$ when its first $N$ moments are known. Theorem 2 characterizes the condition that a Bayes estimate have constant risk over the class of all "priors" which yield these moments. In Section 4, a transformation is introduced which puts the interior of the space of the first $N$ moments for distributions on $\lbrack 0, 1\rbrack$ in one-one correspondence with the interior of the $N$-dimensional unit cube. This transformation is used to show that the supremum of the difference between minimax and Bayes risks over the class of all prior distributions is bounded above by $2^{-N}$. Examples for $N = 1, 2$, and 3 in terms of the above transformation are considered in Section 5.

Article information

Ann. Math. Statist., Volume 39, Number 2 (1968), 492-501.

First available in Project Euclid: 27 April 2007

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier



Skibinsky, Morris. Minimax Estimation of a Random Probability Whose First $N$ Moments are Known. Ann. Math. Statist. 39 (1968), no. 2, 492--501. doi:10.1214/aoms/1177698412.

Export citation