• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.933-939
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• 2009

The bivariate data in clinical research fields often has two types of failure times, which are mark variable for the first failure time and the final failure time. This paper showed how to generate bootstrap data to get Bayesian estimation for the joint distribution of bivariate survival times. The observed data was generated by Frank's family and the fake date is simulated with the Gamma prior of survival time. The bootstrap data was obtained by combining the mimic data with the observed data and the simulated fake data from the observed data.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.825-835
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• 2004

In this paper, we investigate the estimation of the value at risk(VaR) when stock prices are subjected to price limits. The mixture of probability mass functions and beta density functions is proposed to derive the distribution of asset returns. The analyses of real data show that the proposed distribution is appropriate to explain the VaR when the price limits exist in the data.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.336-338
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• 1998

This paper presents a novel neural network structure to the blind deconvolution task where the input (source) to a system is not available and the source has any type of distribution including sparse distribution. We employ multiple sensors so that spatial information plays a important role. The resulting learning algorithm is linear so that it works for both sub-and super-Gaussian source. Moreover, we can successfully deconvolve the mixture of a sparse source, while most existing algorithms [5] have difficulties in this task. Computer simulations confirm the validity and high performance of the proposed algorithm.

• Communications for Statistical Applications and Methods
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• v.24 no.4
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• pp.397-419
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• 2017

A four-parameter extended fatigue lifetime model called the odd Birnbaum-Saunders geometric distribution is proposed. This model extends the odd Birnbaum-Saunders and Birnbaum-Saunders distributions. We derive some properties of the new distribution that include expressions for the ordinary moments and generating and quantile functions. The method of maximum likelihood and a Bayesian approach are adopted to estimate the model parameters; in addition, various simulations are performed for different parameter settings and sample sizes. We propose two new models with a cure rate called the odd Birnbaum-Saunders mixture and odd Birnbaum-Saunders geometric models by assuming that the number of competing causes for the event of interest has a geometric distribution. The applicability of the new models are illustrated by means of ethylene data and melanoma data with cure fraction.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.267.2-267.2
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• 2013

In the citrate reduction method of gold nanoparticle (AuNP) synthesis, pH of the reaction mixture can have a considerable impact on the size and size distribution of AuNPs. In this work, effects of pH variation upon the size and its distribution were examined systematically. As the initial pH was change from 5.5 to 10.5, it showed an optimal pH around 7.5. At this pH, both of the size and the size distribution showed their minimum values, which was verified by transmission electron microscopy and UV-vis spectroscopy. This occurrence of optimal pH was discussed with the results of in situ monitoring pH during the reaction of AuNP synthesis.

• Earthquakes and Structures
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• v.16 no.5
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• pp.601-609
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• 2019

The effect of the porosity and its distribution shape on the normal and shear interfacial stresses of the FGM beam strengthened with FRP plate subjected to a uniformly distributed load are investigated analytically in the present paper. Basically, the governing equations of FGM beams with porosity strengthened with composite plates are identical to the ones without porosity. Nonetheless, when the effect of the porosity and its distribution shape are taken into account, the rule of mixture was reformulated to assess the material characteristics with the porosity phases and its distribution shape. This work discusses the influence of the gradient index, the porosity and its distribution shape on the interfacial stresses.

• Coupled systems mechanics
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• v.9 no.6
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• pp.575-597
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• 2020

Equilibrium equations of a porous FG plate resting on Winkler-Pasternak foundations with various boundary conditions are derived using a new refined shear deformation theory. Different types of porosity distribution rate are considered. Governing equations are obtained including the plate-foundation interaction. This new model meets the nullity of the transverse shear stress at the upper and lower surfaces of the plate. The novel rule of mixture is proposed to describe and approximate material properties of the FG plates with different distribution case of porosity. The validity of this theory is studied by comparing some of the present results with other higher-order theories reported in the literature. Effects of variation of porosity distribution rate, boundary conditions, foundation parameter, power law index, plate aspect ratio, side-to-thickness ratio on the deflections and stresses are all discussed.

• Journal of Distribution Research
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• v.12 no.4
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• pp.1-25
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• 2007

The purpose of this study is to provide the strategic implication of the Korean open air market by examining the factors affecting customer loyalty for various market segments as their competitive environment becomes more turbulent. We have undertaken empirical research that uses the methodology of a mixture regression modeling, as a way to ascertain the determinants of customer loyalty toward the Korean open air market, which should form the base of strategy for each segment. We construct a mixture regression model which uses perceived the Korean open air market value dimensions as explanatory variables, an income as a covariate variable, and a customer loyalty as a dependent variable. The analysis of results show that customers are statistically divided into four segments: 'Accessibility'(33.7%), 'Price'(29.7%), 'Shopping environment,'(22.0%), and 'Merchandising,'(14.5%) groups. The findings also showed that parameter estimates are different for each group, which indicates that the sensitivity to changes in the Korean traditional market perceived value and the income variable affecting customer loyalty vary among segments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.167-176
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• 2005

This paper describes how the state tying model based on the decision tree which is one of Acoustic models used for speech recognition optimizes the model by reducing the number of mixture Gaussians of the output probability distribution. The state tying modeling uses a finite set of questions which is possible to include the phonological knowledge and the likelihood based decision criteria. And the recognition rate can be improved by increasing the number of mixture Gaussians of the output probability distribution. In this paper, we'll reduce the number of mixture Gaussians at the highest point of recognition rate by clustering the Gaussians. Bhattacharyya and Euclidean method will be used for the distance measure needed when clustering. And after calculating the mean and variance between the pair of lowest distance, the new Gaussians are created. The parameters for the new Gaussians are derived from the parameters of the Gaussians from which it is born. Experiments have been performed using the STOCKNAME (1,680) databases. And the test results show that the proposed method using Bhattacharyya distance measure maintains their recognition rate at $97.2\%$ and reduces the ratio of the number of mixture Gaussians by $1.0\%$. And the method using Euclidean distance measure shows that it maintains the recognition rate at $96.9\%$ and reduces the ratio of the number of mixture Gaussians by $1.0\%$. Then the methods can optimize the state tying model.

• Speech Sciences
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• v.5 no.1
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• pp.7-21
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• 1999

This paper proposes an improved training procedure in speech recognition based on the continuous density of the Hidden Markov Model (CDHMM). Of the three parameters (initial state distribution probability, state transition probability, output probability density function (p.d.f.) of state) governing the CDHMM model, we focus on the third parameter and propose an efficient algorithm that determines the p.d.f. of each state. It is known that the resulting CDHMM model converges to a local maximum point of parameter estimation via the iterative Expectation Maximization procedure. Specifically, we propose two independent algorithms that can be embedded in the segmental K -means training procedure by replacing relevant key steps; the adaptation of the number of mixture Gaussian p.d.f. and the initialization using the CDHMM parameters previously estimated. The proposed adaptation algorithm searches for the optimal number of mixture Gaussian humps to ensure that the p.d.f. is consistently re-estimated, enabling the model to converge toward the global maximum point. By applying an appropriate threshold value, which measures the amount of collective changes of weighted variances, the optimized number of mixture Gaussian branch is determined. The initialization algorithm essentially exploits the CDHMM parameters previously estimated and uses them as the basis for the current initial segmentation subroutine. It captures the trend of previous training history whereas the uniform segmentation decimates it. The recognition performance of the proposed adaptation procedures along with the suggested initialization is verified to be always better than that of existing training procedure using fixed number of mixture Gaussian p.d.f.