• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.438-438
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• 1993

• Journal of Korean Society for Quality Management
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• v.5 no.2
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• pp.21-28
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• 1977

• Journal of the Korean Statistical Society
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• v.15 no.2
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• pp.87-96
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• 1986

Sequential estimation of parameters in a compound Poisson process whose jump sizes are one-parameter exponential class random variables is discussed. Cramer-Rao type information inequality is used as an efficiency cirterion. Unbiased estimators for certain parametric functions whose variance attain the lower bound are all characterized with the corresponding sampling plans.

• Journal of the Korean Statistical Society
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• v.11 no.1
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• pp.25-35
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• 1982

A problem of selecting the least probable cell in a multinomial distribution is studied in a Bayesian framework. We consider two loss components the cost of sampling and the difference in cell probabilities between the selected and the least probable cells. A Bayes sequential selection rule is derived with respect to a Dirichlet prior, and it is compared with the best fixed sample size selection rule. The continuation sets with respect to the vague prior are tabulated for certain cases.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.1
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• pp.29-37
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• 2009

This paper proposes a curtailed-sequential probability ratio test (SPRT) control chart. For using the conventional SPRT control chart, the number of items inspected in a sampling point should have no restriction since items in a sampling point are inspected one by one until an SPRT Is terminated. The number of observations taken in a sampling point, however, has an upper bound since sampling and testing of an item is time-consuming or expensive. When the sample size reaches the upper bound without evidence of an in-control or out-of-control state of a process, the proposed chart makes a decision using the sample mean of all observations taken in a sampling point. The properties of the Proposed chart are obtained by a Markov chain approach and the performance of the chart is compared with fixed sample size (FSS) and variable sample size (VSS) control charts. A comparative study shows that the proposed chart performs better than VSS control charts as well as conventional FSS control charts.

• Journal of Korean Society for Quality Management
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• v.10 no.2
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• pp.25-33
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• 1982

We discuss a model for acceptance/rejection decision regarding finite populations. The model is based on a beta-binomial prior distribution and additive costs -- relative sampling costs, relative sorting costs and costs of accepted defectives. A substantial part of the paper is devoted to constructing a Bayes sequential sampling acceptance plan (BSSAP) for attributes under the model. It is shown that the Bayes fixed size sampling acceptance plans (BFSAP) are better than the Hald's (1960) single sampling acceptance plans based on a uniform prior. Some tables and examples are provided for comprisons of the minimum Bayes risks of the BSSAP and those of the BFSAP based on a uniform prior and the model.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.67-74
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• 2023

A semiconductor fabrication facility(FAB) is one of the most capital-intensive and large-scale manufacturing systems which operate under complex and uncertain constraints through hundreds of fabrication steps. To improve fab performance with intuitive scheduling, practitioners have used weighted-sum scheduling. Since the determination of weights in the scheduling significantly affects fab performance, they often rely on simulation-based decision making for obtaining optimal weights. However, a large-scale and high-fidelity simulation generally is time-intensive to evaluate with an exhaustive search. In this study, we investigated three sampling methods (i.e., Optimal latin hypercube sampling(OLHS), Genetic algorithm(GA), and Decision tree based sequential search(DSS)) for the optimization. Our simulation experiments demonstrate that: (1) three methods outperform greedy heuristics in performance metrics; (2) GA and DSS can be promising tools to accelerate the decision-making process.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.453-462
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• 2006

Discovering sequential patterns is an important problem for many applications. Existing algorithms find sequential patterns in the sense that only items are included in the patterns. However, for many applications, such as business and scientific applications, quantitative attributes are often recorded in the data, which are ignored by existing algorithms but can provide useful insight to the users. In this paper, we consider the problem of mining sequential patterns with quantities. We demonstrate that naive extensions to existing algorithms for sequential patterns are inefficient, as they may enumerate the search space blindly. Thus, we propose hash filtering and quantity sampling techniques that significantly improve the performance of the naive extensions. Experimental results confirm that compared with the naive extensions, these schemes not only improve the execution time substantially but also show better scalability for sequential patterns with quantities.

• ETRI Journal
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• v.42 no.6
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• pp.976-986
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• 2020

Internet of Things (IoT) is considered the future network to support wireless communications. To realize an IoT network, sufficient spectrum should be allocated for the rapidly increasing IoT devices. Through cognitive radio, unlicensed IoT devices exploit cooperative spectrum sensing (CSS) to opportunistically access a licensed spectrum without causing harmful interference to licensed primary users (PUs), thereby effectively improving the spectrum utilization. However, an open access cognitive IoT allows abnormal IoT devices to undermine the CSS process. Herein, we first establish a hard-combining attack model according to the malicious behavior of falsifying sensing data. Subsequently, we propose a weighted sequential hypothesis test (WSHT) to increase the PU detection accuracy and decrease the sampling number, which comprises the data transmission status-trust evaluation mechanism, sensing data availability, and sequential hypothesis test. Finally, simulation results show that when various attacks are encountered, the requirements of the WSHT are less than those of the conventional WSHT for a better detection performance.