By Dasgupta A. (ed.)
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Additional info for A festschrift for Herman Rubin
Consider X1 , . . , Xn that are independent and identically distributed random vectors in Rp with a density f (x − θ) (with respect to Lebesgue measure). Thus Θ = Rp and the model for X = (X1 , . . , Xn ) is n P (dx|θ) = f (xi − θ)dxi i=1 on the sample space X = Rpn . With dx as Lebesgue measure on X , the density of P (dx|θ) with respect to dx is n p(x|θ) = f (xi − θ). i=1 Next take ν(dθ) = dθ on Θ = Rp and assume, for simplicity, that m(x) = p(x|θ)dθ Rp is in (0, ∞) for all x. Then a version of “Q(dθ|x)” is Q(dθ|x) = p(x|θ) dθ.
Anal. Appl. 1, 314–330. MR126872  Meyn, S. P. and Tweedie, R. L. (1993). Markov Chains and Stochastic Stability. New York: Springer-Verlag. MR1287609  Revuz, D. (1984). Markov Chains, 2nd Edition. North Holland, Amsterdam. MR415773  Smith, J. Q. (1994). The inadmissibility of certain Bayes decision rules under vague priors in location problems. J. R. S. S. - B. 56, 543–548. MR1278226  Srinivasan, C. (1981). Admissible generalized Bayes estimators and exterior boundary value problems.
Statist. 41, 446–456. MR261742 A Festschrift for Herman Rubin Institute of Mathematical Statistics Lecture Notes – Monograph Series Vol. 45 (2004) 21–44 c Institute of Mathematical Statistics, 2004 Estimation in restricted parameter spaces: A review Eric Marchand∗1 and William E. Strawderman2 University of New Brunswick Rutgers University Abstract: In this review of estimation problems in restricted parameter spaces, we focus through a series of illustrations on a number of methods that have proven to be successful.
A festschrift for Herman Rubin by Dasgupta A. (ed.)