Titlebook: Advances in Ranking and Selection, Multiple Comparisons, and Reliability; Methodology and Appl N. Balakrishnan,H. N. Nagaraja,N. Kannan Boo

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包租车船

Cengiz Kahraman,Cafer Erhan Bozdağing the subsets considered to be those defined based on half-intervals of a covariate. Rather than treating this as a family of hypothesis tests, we instead choose the minimum p-value from the group of hypothesis tests as our test statistic. Simulation is employed to find an approximate critical val

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Michael Doumpos,Constantin Zopounidislikelihoods to discriminate between these two distributions. We obtain the asymptotic distributions of the test statistics and it is observed that they are independent of the unknown parameters. When the underlying distribution is normal, the asymptotic distribution works quite well even when the sa

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https://doi.org/10.1007/978-3-540-72821-4select a non-empty subset of the . populations whose size is at most . (1 ≤ . ≤ . − t) so that at least one of the populations associated with the . (1 ≤ . ≤ . − 1) largest means is included in the selected subset with a minimum guaranteed probability .*, whenever μ. − μ. ≥ δ* where .* and δ* are sp

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Studies in Computational Intelligencen (pdf) belonging to the exponential family .(θ..., . ∈ ℝ (the real line), .. ∈ Ω ⊆ ℝ. Unknown populations associated with .. = min... and .. = max... are called the lower extreme population (LEP) and the upper extreme population (UEP), respectively. This chapter deals with optimal decision (or sele

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