• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.127-130
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• 2004

혼합정규분포에서 얻어진 히스토그램 자료에서 모수의 추정은 EM 알고리즘 혹은 스프라인 방법이 흔히 이용되고 있다. 본 논문에서는 히스토그램 자료를 비선형회귀모형으로 적합하는 방법을 제시하고, 시뮬레이션으로 제시된 방법과 EM 알고리즘 방법을 비교하였다.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.837-849
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• 2011

A self-organizing network is designed to estimate parameters of normal mixtures. SOMN achieves fast convergence and low possibility of divergence even when sample sizes are small, while PMLE eliminate unnecessary components. The proposed network effectively combines the good properties of SOMN and PMLE. Simulation verifies that the proposed network eliminates unnecessary components in normal mixtures when sample sizes are relatively small.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.251-260
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• 2017

The option pricing of Black와 Scholes (1973) and Merton (1973) has been widely reported to fail to reflect the time varying volatility of financial time series in many real applications. For example, Duan (1995) proposed GARCH option pricing method through Monte Carlo simulation. However, financial time series is known to follow a fat-tailed and leptokurtic probability distribution, which is not explained by Duan (1995). In this paper, in order to overcome such defects, we proposed the option pricing method based on GARCH models with normal mixture errors. According to the analysis of KOSPI200 option price data, the option pricing based on GARCH models with normal mixture errors outperformed the option pricing based on GARCH models with normal errors in the unstable period with high volatility.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.821-834
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• 2013

Law et al. (2004) proposed a normal distribution based salient mixture model for variable selection in clustering. However, this model has substantial problems such as the unidentifiability of components an the inaccurate selection of informative variables in the case of a small cluster size. We propose an alternative method to overcome problems and demonstrate a good performance through experiments on simulated data and real data.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.513-523
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• 2016

Fitting a mixture of multivariate skew normal distribution (MSNMix) with multiple skewness parameter vectors via EM algorithm often requires a highly expensive computational cost to calculate the moments and probabilities of multivariate truncated normal distribution in E-step. Subsequently, it is common to fit an asymmetric data set with MSNMix with a simple skewness parameter vector since it allows us to compute them in E-step in an univariate manner that guarantees a cheap computational cost. However, the adaptation of a simple skewness parameter is unrealistic in many situations. This paper proposes an approximate estimation for the MSNMix with multiple skewness parameter vectors that also allows us to treat them in an univariate manner. We additionally provide some experiments to show its effectiveness.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.459-469
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• 2012

This article proposes a couple of parallel implementations of the self-organizing network for normal mixtures. In principle, self-organizing networks should be able to be implemented in a parallel computing environment without issue. However, the network for normal mixtures has inherent problem in being operated parallel in pure sense due to estimating conditional expectations of the mixing proportion in each iteration. This article shows the result of the parallel implementations of the network using Java. According to the results, both of the implementations achieved a faster execution without any performance degradation.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.219-224
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• 2015

This research estimates the Mixture of Normals of households saving rate in Korea. Our sample is MDSS, micro-data in 2014 and Gibbs algorithm is used to estimate the Mixture of Normals. Evidences say some results. First, Gibbs algorithm works very well in estimating the Mixture of Normals. Second, Saving rate data has at least two components, one with mean zero and the other with mean 29.4%. It might be that households would be separated into high saving group and low saving group. Third, analysis of Mixture of Normals cannot answer that question and we find that income level and age cannot explain our results.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.361-369
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• 2015

A general linear mixed-effects model is often used to analyze repeated measurement experiment data of a continuous response variable. However, a general linear mixed-effects model can give improper analysis results when simultaneously detecting heteroscedasticity and the non-normality of population distribution. To achieve a more robust estimation, we used a heavy-tailed linear mixed-effects model for a more exact and reliable analysis conclusion than a general linear mixed-effects model. We also provide reliability analysis results for further research.

• The Korean Journal of Applied Statistics
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• v.21 no.5
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• pp.825-833
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• 2008

Discriminant analysis based on Gaussian mixture models, an useful tool for multi-class classifications, can be extended to semi-supervised learning. We consider a model selection problem for a Gaussian mixture model in semi-supervised learning. More specifically, we adopt Bayesian information criterion to determine the number of subclasses in the mixture model. Through simulations, we illustrate the usefulness of the criterion.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.547-557
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• 2011

In this paper we developed a statistical model for traffic volume data which collected from a spot of specific local state road. One peculiar property of daily traffic data is that it has bimodal shape which have two peaks on times of both going to office and coming back to home. So, various mixture models of circular distribution are suggested for bimodal traffic data and EM algorithms are applied to estimate the parameters of the suggested models. To compare the accuracy of the suggested models, classical regressions with dummy variables are also considered. The suggested models for traffic volumn data can be effectively used to estimate missing values due to measuring instrument disorder.