• Communications for Statistical Applications and Methods
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• 제2권1호
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• pp.74-88
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• 1995

This article introduces a unified method of choosing the most explanatory and significant multiway partitions for classification tree design and analysis. The method is derived on the impurity reduction (IR) measure of divergence, which is proposed to extend the proportional-reduction-in-error (PRE) measure in the decision-theory context. For the method derivation, the IR measure is analyzed to characterize its statistical properties which are used to consistently handle the subjects of feature formation, feature selection, and feature deletion required in the associated classification tree construction. A numerical example is considered to illustrate the proposed approach.

• 한국해양공학회지
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• 제20권4호
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• pp.70-75
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• 2006

A numerical procedure is described for estimating the effects of the multi-directional irregular waves on the motion responses and tension variations of the ISSC-TLP. The numerical approach is based on a three-dimensional source distribution method and a spectral analysis technique of directional waves. The spectral description for the linear system of ISSC-TLP in the frequency domain is sufficient to completely define the motion responses and tension variations. This is because both the wave inputs and responses are stationary Gaussian random processes, of which the statistical properties in the amplitude domain are well known. The numerical results for the linear motion responses and tension variations in regular waves are compared with the experimental and numerical ones, which are obtained in the literature. The results of comparison confirmed the validity of the proposed approach.

• Journal of the Korean Statistical Society
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• 제25권4호
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• pp.589-601
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• 1996

Geometric approach to extend the classical two-set theory of canonical correlation analysis to three or more sets is considered. It provides statistical graphs to represent the data in a low dimensional space. Procedures are developed for computing the canonical variables and the corresponding properties are investigated. The solution is equivalent to that of the usual problem in the case of two sets. Goodness-of-fit of the proposed plots is studied and a numerical example is included.

• Communications for Statistical Applications and Methods
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• 제20권4호
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• pp.291-300
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• 2013

In this paper, we propose method-free permutation predictor hypothesis tests in the context of sufficient dimension reduction. Different from an existing method-free bootstrap approach, predictor hypotheses are evaluated based on p-values; therefore, usual statistical practitioners should have a potential preference. Numerical studies validate the developed theories, and real data application is provided.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.607-617
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• 2003

This paper offers a practical comparison of efficiency between local likelihood approach and conventional kernel approach in density estimation. The local likelihood estimation procedure maximizes a kernel smoothed log-likelihood function with respect to a polynomial approximation of the log likelihood function. We use two types of data driven bandwidths for each method and compare the mean integrated squares for several densities. Numerical results reveal that local log-linear approach with simple plug-in bandwidth shows better performance comparing to the standard kernel approach in heavy tailed distribution. For normal mixture density cases, standard kernel estimator with the bandwidth in Sheather and Jones(1991) dominates the others in moderately large sample size.

• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.21-31
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• 2002

This paper considers a Bayesian approach to determine an optimal replacement policy for a repairable system with warranty period. The mathematical formula of the expected cost rate per unit time is obtained for two cases : RFRW(renewing free-replacement warranty) and RPRW(renewing pro-rata warranty). When the failure time is Weibull distribution with uncertain parameters, a Bayesian approach is established to formally express and update the uncertain parameters for determining an optimal replacement policy. Some numerical examples are presented for illustrative purpose.

• 한국연소학회지
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• 제2권1호
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• pp.39-51
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• 1997

A review of probability density function(PDF) methodology and direct numerical simulation for the purpose of modeling turbulent combustion are presented in this study where particular attention is focused on the modeling problem of turbulent molecular mixing term appearing in PDF transport equation. Existing mixing models results were compared to those evaluated by direct numerical simulation in a turbulent premixed medium with finite rate chemistry in which the initial scalar field is composed of pockets of partially burnt gases to simulate autoignition. Two traditional mixing models, the least mean square estimations(LMSE) and Curl#s model are examined to see their prediction capability as well as their modeling approach. Test calculations report that the stochastically based Curl#s approach, though qualitatively demonstrates some unphysical behaviors, predicts scalar evolutions which are found to be in good agreement with statistical data of direct numerical simulation.

• Structural Engineering and Mechanics
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• 제31권6호
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• pp.737-749
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• 2009

The problem of estimating the dynamic response of a distributed parameter system excited by a moving vehicle with random initial velocity and random vehicle body mass is investigated. By adopting the Galerkin's method and modal analysis, a set of approximate governing equations of motion possessing time-dependent uncertain coefficients and forcing function is obtained, and then the dynamic response of the coupled system can be calculated in deterministic sense. The statistical characteristics of the responses of the system are computed by using improved perturbation approach with respect to mean value. This method is simple and useful to gather the stochastic structural response due to the vehicle-passenger-bridge interaction. Furthermore, some of the statistical numerical results calculated from the perturbation technique are checked by Monte Carlo simulation.

• Communications for Statistical Applications and Methods
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• 제23권4호
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• pp.355-362
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• 2016

Ensemble methods often help increase prediction ability in various predictive models by combining multiple weak learners and reducing the variability of the final predictive model. In this work, we demonstrate that ensemble methods also enhance the accuracy of prediction under kernel ridge regression and kernel logistic regression classification. Here we apply bagging and random forests to two kernel-based predictive models; and present the procedure of how bagging and random forests can be embedded in kernel-based predictive models. Our proposals are tested under numerous synthetic and real datasets; subsequently, they are compared with plain kernel-based predictive models and their subsampling approach. Numerical studies demonstrate that ensemble approach outperforms plain kernel-based predictive models.

• Communications for Statistical Applications and Methods
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• 제14권3호
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• pp.507-516
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• 2007

Multinomial logistic regression is probably the most popular representative of probabilistic discriminative classifiers for multiclass classification problems. In this paper, a kernel variant of multinomial logistic regression is proposed by combining a Newton's method with a bound optimization approach. This formulation allows us to apply highly efficient approximation methods that effectively overcomes conceptual and numerical problems of standard multiclass kernel classifiers. We also provide the approximate cross validation (ACV) method for choosing the hyperparameters which affect the performance of the proposed approach. Experimental results are then presented to indicate the performance of the proposed procedure.