• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1041-1044
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• 2005

Recently, Zhang et al.(Fuzzy Sets and Systems 147(2004) 475-485) proved Fatou's lemma and Lebesgue dominated convergence theorem under some conditions of fuzzy measure. In this note, we show that these conditions of fuzzy measure is essential to prove Fatou's lemma and Lebesgue dominated convergence theorem by examples

• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.453-456
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• 2005

It is known that the classical Fatou's lemma and Lebesgue convergence theorem do not require the assumption that J1. is finite. In this note, we show that the assumption $\mu$(X) < $\infty$ cannot be replaced with a weaker assumption to prove Fatou's lemma and Lebesgue convergence theorem for a sequence of set-valued measurable function suggested by Zhang and Wang (Fuzzy Sets and Systems 56(1993) 237-241).

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.4 no.1
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• pp.39-48
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• 2000

In this paper we define T-fuzzy integrals of set-valued mappings, which are extensions of fuzzy integrals of the single-valued functions defined by Sugeno. And we discuss their properties.

• Journal of the Chungcheong Mathematical Society
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• v.25 no.3
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• pp.393-400
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• 2012

In this paper, we study the process of McShane delta integrals on time scales and discuss the relation between McShane delta integral and Henstock delta integral. We also prove the mono- tone convergence theorem, Fatou's Lemma and the dominated con- vergence theorems for the McShane delta integral.

• Communications for Statistical Applications and Methods
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• v.31 no.4
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• pp.427-440
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• 2024

The problem addressed is that of sequentially estimating the difference between the means of two populations with respect to the squared error loss, where each population distribution is a member of the one-parameter exponential family. A Bayesian approach is adopted in which the population means are estimated by the posterior means at each stage of the sampling process and the prior distributions are not specified but have twice continuously differentiable density functions. The main result determines an asymptotic second-order lower bound, as t → ∞, for the Bayes risk of a sequential procedure that takes M observations from the first population and t - M from the second population, where M is determined according to a sequential design, and t denotes the total number of observations sampled from both populations.