Titlebook: Advances in Stochastic Simulation Methods; N. Balakrishnan,V. B. Melas,S. Ermakov Book 2000 Springer Science+Business Media New York 2000

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Bias Constrained Minimax Robust Designs for Misspecified Regression Modelsch is to minimize the maximum integrated mean squared error of the fitted values, subject to an unbiasedness constraint. The maxima are taken over broad classes of departures from the `‘ideal’ model. The methods yield particularly simple treatments of otherwise intractable design problems. This poin

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On the Criteria for Experimental Design in Nonlinear Error-In-Variables Models influence on the design. Different strategies for selecting the set up of the experiment will be presented. The goal is to obtain consistent estimators with minimal covariance matrix of their asymptotic distribution.

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On Generating and Classifying All qn-m-l Regularly Blocked Factional Designsesigns (for . prime or a power of a prime), in the conventional sense of such designs as defined, for example, by Finney (1960, p73) or as displayed in the classic set of NBS tables (., .). Following a standard notation, we refer to these as .. designs, implying a division of the selected fraction i

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Locally Optimal Designs in Non-Linear Regression: A Case Study of the Michaelis-Menten Functionlues for . and are therefore . optimal. An exact optimal design can be seen as a choice of . points ..,…, .. out of a set .. Two sets will be considered in this paper: an interval. = .. = [.., ..]and a set. = .. ={..,…, ..}which consists of a finite number of candidate points. If the set .. is used

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In this paper, we first derive exact explicitn. Also, we present recurrence relations for single, double, triple and quadruple moments of order statistics from the power function distribution. These relations will enable one to find all moments (of order up to four) of order statistics for all sample sizes in a simple recursive manner. We then

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Selecting from Normal Populations the One with the Largest Absolute Mean: Common Unknown Variance Ca-zone formulation of Bechhofer (.), assuming that the populations have a common known variance. When the common variance is unknown, a single-stage procedure that guarantees a minimum probability of a correct selection does not exist. In this paper, a non-eliminating two-stage procedure is proposed

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