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By Dasgupta A. (ed.)

ISBN-10: 0940600617

ISBN-13: 9780940600614

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Additional info for A festschrift for Herman Rubin

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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 [21] Meyn, S. P. and Tweedie, R. L. (1993). Markov Chains and Stochastic Stability. New York: Springer-Verlag. MR1287609 [22] Revuz, D. (1984). Markov Chains, 2nd Edition. North Holland, Amsterdam. MR415773 [23] 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 [24] 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.

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A festschrift for Herman Rubin by Dasgupta A. (ed.)

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