• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.895-902
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• 2011

This paper is mainly concerned about providing confidence prediction interval for exchange rate during economic crisis. Our proposed method is to use block bootstrap method for prediction interval for next day. It is shown that block bootstrap method is particularly effective for interval prediction of exchange rate during economic crisis.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.867-877
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• 2004

The sequential sampling inspection method is an extension of the multiple-sampling methods, and its theory is based on the sequential probability ratio test (SPRT) of Wald. In this paper, the characteristics of SPRT for testing the number of defects are approximated by using the estimated excess over the boundaries. The use of the estimated excess shows good performances in estimating the operating characteristic function and the average sample number of SPRT compared to the method by neglecting the excess. It also makes it possible to determine the boundary values which satisfy the desired error probabilities.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.7
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• pp.3229-3232
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• 2012

In this study, cancer incidence data were assessed to provide various rates of five year age groups for a given year, lying between two census years. The individual exponential growth rate method is most useful in both population-based and non-population cased cancer registries in India to estimate the population by five yearly age groups and also find the rates of crude rates, age standard rates and cumulative rates. This method has been shown to endure from bias and often results sacrificing the overall growth rate and correction factor must be needful in five year age group population to maintain it. A second method, the difference distribution method is also able to maintain the overall growth rate and overcome the bias in estimation of five yearly age group populations. From this point of view these methods serving a new technique for population estimation by five yearly age groups for inter census years.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.241-244
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• 2000

This paper addresses a new method of blind source separation (BSS) when sources are nonstationary signals. Our method requires only multiple correlation matrices of the observed data at several time-windowed data frames to estimate the mixing matrix. In contrast to most existing BSS methods where higher-order statistics is necessary, our method is based on only second-order statistics. In the framework of correlation matching, we develop a new BSS algorithm. The useful behavior of the proposed method is verified by numerical experiments.

• East Asian mathematical journal
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• v.32 no.1
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• pp.129-138
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• 2016

In this paper, we propose an efficient regularization technique (The Method of Regularized Ratios) for the reconstruction of the shape of a rigid elastic scatterer from far field measurements. The approach used is based on the factorization method and creates via Picard's condition ratios, baptized Regularized Ratios, that serve to effectively remove unwanted singular values that may lead to poor reconstructions. This is achieved through the use of a sophisticated algorithm that progressively adjusts an initially set moderate tolerance. In comparison with the well established Tikhonov-Morozov regularization techniques our new algorithm appears to be more computationally efficient as it doesn't require computation of the regularization parameter for each point in the grid.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.146-154
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• 2019

An improved principal component analysis (PCA) method is applied for sensor fault detection and isolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducing methods are combined with general PCA method to improve the model performance in practice. In data pre-processing, singular points and random fluctuations in the original data are eliminated with various techniques respectively. In fault detecting, a statistics-based method is proposed to reduce the false alarms of $T^2$ and Q statistics. Finally, the effects of the proposed data pre-processing and false alarm reducing techniques are evaluated with sensor measurements from a real NPP. They are proved to be greatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile various sensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulation results show that the proposed PCA model presents favorable performance on the FDI of sensors no matter with major or small failures.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.225-238
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• 2022

In this paper, we propose a clustering method for advanced metering infrastructure (AMI) data in Korea. As AMI data presents non-stationarity, we consider time-dependent frequency domain principal components analysis, which is a proper method for locally stationary time series data. We develop a new clustering method based on time-varying eigenvectors, and our method provides a meaningful result that is different from the clustering results obtained by employing conventional methods, such as K-means and K-centres functional clustering. Simulation study demonstrates the superiority of the proposed approach. We further apply the clustering results to the evaluation of the electricity price system in South Korea, and validate the reform of the progressive electricity tariff system.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.185-195
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• 2014

Ranking-based conjoint analysis is widely used in various fields such as marketing research. While the ranking-based conjoint affords several advantages over the rating-based or choice-based conjoint, it has a serious shortcoming that respondents have much difficulty in ranking the product profiles in order of preference when many profiles are involved. This article suggests a new method for the preference measurement to improve the response efficiency. The method employs the concept of ranking sets that let the respondent evaluate a small number of profiles at a time. Through the proposed method, preference rankings of profiles obtained from each ranking set are aggregated to generate overall rankings. The balanced incomplete block design is expanded and transformed to the dual design in order to construct well-balanced ranking sets that can accommodate a large number of profiles. The proposed method is applied to the analysis of consumer preferences for perfume-for-women.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.43-51
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• 1995

For the stress-strengh function, hierarchical Bayes estimations considered under squared error loss and entropy loss. In particular, the desired marginal postrior densities ate obtained via Gibbs sampler, an iterative Monte Carlo method, and Normal approximation (by Delta method). A simulation is presented.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.10-19
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• 1994

We study the change-point problem in the context of system reliability models. The maximum likelihood estimators are obtained based on the Jelinski and Moranda model. To find the approximate distribution of the change-point estimator, we suggest of parametric bootstrap method in which the estimators are substituted in the assumed model. Through an example we illustrate the proposed method.