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Space-filling designs represent the most widely used class of procedures in this application field. The reason lies in their simplicity and the fact that it is often important to obtain information from the entire design space, which has to be filled in a uniform way. There are several techniques to define what it means to spread points, especially in the multidimensional setting, and these lead to a variety of designs within the space filling class, such as the well-known Latin hypercube designs (for recent literature see [9]).

N, then the (3 × 3) Fisher information associated with a design ξ is given by: I (β, θ; ξ ) = F t C −1 F 0t 0 1 2 tr C −1 ∂C ∂θ 2 , 32 A. Baldi Antognini and M. Zagoraiou (see for instance [11]), where F = ( f (x 1 ), . . , f (x n ))t and C = C(θ) represents the correlation matrix of the observations Y1 , . . , Yn ; from (4), C is an (n × n) symmetric matrix whose (i, j )-th element is e−θ|xi −x j | , which depends on the corresponding design points only through their distance. Firstly, we give a simplification of the Fisher information matrix.

The dot-dashed line is the parametric AFT regression line under Weibull errors, and the triangle is the corresponding estimate of the median or smaller than ti , and then we take the minimum. Here L(dβ1 , dθi |dat a) denotes the joint posterior of β1 and the latent θi associated with ti (check notation at the beginning of the Appendix). 1. Both models provide a good fit; the “unusual” observations under the parametric mixed-effects model are 18, but they are only 4 in our setting. As for the interval estimates, we explain the good performance of our p-values by the heavy-tailed error distributions.

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A Large Low-Pressure AHTR [sm article]

by Kenneth

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