• Journal of the Korean Statistical Society
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• v.13 no.1
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• pp.25-31
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• 1984

A technique which has been used for the evaluation of certain kinds of multiple integrals, viz., the technique of imcomplete gamma function operators, is employed and extended to the case where the parameters and arguments are non-equal and non-integer for the probability integral of the inverted Dirichlet distribution. Several types of recurrence formulas have been developed for the tail probabilities and a subset selection procedure in ranking variances is discussed as an application.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.973-981
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• 2010

Quantile regression investigates the quantiles of the conditional distribution of a response variable given a set of covariates. M-quantile regression extends this idea by a "quantile-like" generalization of regression based on influence functions. In this paper we propose a new method of estimating M-quantile regression functions, which uses kernel machine technique. Simulation studies are presented that show the finite sample properties of the proposed M-quantile regression.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.499-505
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• 2006

Regularlization approach to regression can be easily found in Statistics and Information Science literature. The technique of regularlization was introduced as a way of controlling the smoothness properties of regression function. In this paper, we have presented a new method to evaluate linear and non-linear fuzzy regression model based on Tanaka's model using the idea of regularlization technique. Especially this method is a very attractive approach to model non -linear fuzzy data.

• Journal of applied mathematics & informatics
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• v.7 no.3
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• pp.813-831
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• 2000

In recent years, the theory of Wiener-Hopf equations has emerged as a novel and innovative technique for developing efficient and powerful numerical methods for solving variational inequalities and complementarity problems. In this paper, we provide an account of some of the fundamental aspects of the Wiener-Hopf equations with major emphasis on the formulation, computational algorithms, various generalizations and their applications. We also suggest some open problems for further research with sufficient information and references.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.167-175
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• 2006

It has long been recognized that cancer is a genetic disease. To find this measures of genetic instability, stain cells with chromosome specific probes using chromosome in-situ hybridization technique is adopted. Even though in-situ hybridization technique is powerful, truncation of nuclei often results in under-representation of chromosome copies in slides due to the sectioning of tissue blocks. Because of this problem we suggest three different methods to analyze the cervical cancer data set. We observe that genetic instability is an increasing function of histology and our suggested model is the best in detecting genetic instability of tumorigenesis processes.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1345-1352
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• 2008

An inventory with constant demand is studied. We adopt a renewal argument to obtain the transient and stationary distribution of the level of the inventory. We show that the stationary distribution can be also derived by making use of either the level crossing technique or the renewal reward theorem. After assigning several managing costs to the inventory, we calculate the long-run average cost per unit time. A numerical example is illustrated to show how we optimize the inventory.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.151-154
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• 2000

We propose a statically motivated scene change detection algorithm. As the difference between the neighboring frames will generate peaks at scene boundaries, the problem of detecting fast scene changes is equivalent to detecting peaks in a given sequence. In this paper, the peak detection is performed via several statistics, namely the sample means and variances. For eliminating flash lights as well as detecting fast scene changes within a small number of frames, we have opted to use a two-stage process for computing the necessary statistics. The results indicate superiority of necessary statistics. The results indicate superiority of the proposed algorithm over the previously reported algorithm.

• International Journal of Quality Innovation
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• v.5 no.1
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• pp.1-9
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• 2004

The nonhomogeneous poisson process (NHPP) is often used to model repairable systems that are subject to a minimal repair strategy, with negligible repair times. In this situation, the system can be characterized by its intensity function. There have been many NHPP models according to intensity functions. However, the intensity function of system in use can be changed because of repair or its aging. We consider the single change point model as the modification of the power law process. The shape parameter of its intensity function is changed before and after the change point. We detect the presence of the change point using Bayesian methodology. Some numerical results are also presented.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.415-422
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• 2012

For statistical microarray data analysis, clustering analysis is a useful exploratory technique and offers the promise of simultaneously studying the variation of many genes. However, most of the proposed clustering methods are not rigorously solved for a time-course microarray data cluster and for a fitting time covariate; therefore, a statistical method is needed to form a cluster and represent a linear trend of each cluster for each gene. In this research, we developed a modified hierarchical mixture of an experts model to suggest clustering data and characterize each cluster using a linear mixed effect model. The feasibility of the proposed method is illustrated by an application to the human fibroblast data suggested by Iyer et al. (1999).

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.210-212
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• 1996

Random drift rate (i.e., random drift in angle rate) of a gyro represents the major error source of inertial navigation systems that are required to operate over long time intervals. It is uncorrectable and leads to an increase in the error with the passage of time. In this paper a technique is presented for analyzing random process from experimental data and the results are presented. The problem of estimating the a priori statistics of a random process is considered using time averages of experimental data. Time averages are calculated and used in the optimal data-processing techniques to determine the statistics of the random process. Therefore the contribution each component to the gyro drift process can be quantitatively measured by its statistics. The above techniques will be applied to actual gyro drift rate data with satisfactory results.