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www.jstor.org/stable/2236284
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be essentially complete relative to the class of decision rules with bounded risk functions. The decision rule 6 c A if and only if after taking n observations ...
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www.jstor.org/stable/2958858
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4o is an essentially complete class for each y e SY for the conditional decision problem. Let _#* be the set of decision rules vj: MA X. (,%? x Y) -+ [0, 1] such that ...
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www.jstor.org/stable/10.2307/4615975
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Key words: monotone likelihood ratio, essentially complete class, monotone decision problem, monotone symmetric decision rules. 1. Introduction. Let Xl, . , X ...
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projecteuclid.org/euclid.aos/1176344255
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Suppose that a set of decision rules $\mathscr{M}_0$ is an essentially complete class for each $y \in \mathscr{Y}$ for the conditional decision problem.
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projecteuclid.org/euclid.aoms/1177728974
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Under certain assumptions the following class $A$ is shown to be essentially complete relative to the class of decision rules with bounded risk functions.
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www.mathunion.org/ICM/ICM1950.1/Main/icm1950.1.0231.024...
www.mathunion.org/ICM/ICM1950.1/Main/icm1950.1.0231.0243.ocr.pdf
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the construction of complete or essentially complete classes of decision rules is of great importance in any statistical decision problem. The first result concerning ...
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www.math.ucla.edu/~tom/MathematicalStatistics/Heading.h...
www.math.ucla.edu/~tom/MathematicalStatistics/Heading.html
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Essential Completeness of the Class of Nonrandomized Decision Rules ... Essentially Complete Classes of Rules Based on Sufficient Statistics; Section 3.5.
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citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.49.71...
citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.49.7149&rep=rep1&type=pdf
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Consequently, the class of monotone decision rules is essentially complete, Berger (1985), page 535. Further, if F is strictly. MLR, then it follows from Brown et al.
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digitool.library.mcgill.ca/thesisfile46518.pdf
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by restricting consideration to an (essentially) complete class of decision rules. We observe the following immediate results of the above definitions: Lemma 2.1.
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