• Asia Marketing Journal
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• v.6 no.2
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• pp.55-68
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• 2004

After Nakanishi and Cooper(1982) suggested a way of transforming the complicated nonlinear MCI model into a simple linear form, the application of MCI model has been increased. However, the use of MCI model in Korea is quite limited. The goal of this paper is to demonstrate the practical application of MCI(Multiplicative Competitive Interaction) model to a consumer goods industry. MCI model is a form of the attraction model explaining the relation between marketing mix variables and market share. In this study, multiple sources of empirical data are incorporated in the model formulation stage. In the estimation process, the equity estimation is applied to solve the possible multi-collinearity problem among marketing mix variables. Results from the fitted model suggest meaningful managerial implications for the management of brand equity and the allocation of resources among marketing mix variables.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.324-333
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• 2001

In this paper we present a novel color image segmentation algorithm based on a Gaussian Mixture Model(GMM). It is introduced a Deterministic Annealing Expectation Maximization(DAEM) algorithm which is developed using the principle of maximum entropy to overcome the local maxima problem associated with the standard EM algorithm. In our approach, the GMM is used to represent the multi-colored objects statistically and its parameters are estimated by DAEM algorithm. We also develop the automatic determination method of the number of components in Gaussian mixtures models. The segmentation of image is based on the maximum posterior probability distribution which is calculated by using the GMM. The experimental results show that the proposed DAEM can estimate the parameters more accurately than the standard EM and the determination method of the number of mixture models is very efficient. When tested on two natural images, the proposed algorithm performs much better than the traditional algorithm in segmenting the image fields.

• The Korean Journal of Financial Management
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• v.10 no.2
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• pp.117-136
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• 1993

1980년부터 1992년까지 12년간의 거시경제변수(巨視經濟變數)와 주식수익율자료(株式收益率資料)를 이용하여 한국증권시장(韓國證券市場)에서 생산기저모형(生産基底模型)에 대한 실증적(實證的) 검증(檢證)과 아울러 CAPM, APM 그리고 소비기저모형(消費基底模型)을 검증함으로써 이 모형들의 현실적인 설명력에 대한 비교 분석을 하고자 하였다. 검증에 사용된 모형은 Cochrane(1991,1992)과 BCM(1990) 및 Sharathchandra(1991)등에 의하여 유도된 생산기저모형을 기초로 하였다. 그리고 모수추정(母數推定)과 모형의 타당성(妥當性) 검증(檢證)을 위하여 수단변수(手段變數)를 사용하지않는 무조건부모형(無條件附模型)에서는 ML방법(maximum likelihood method)을 이용하였으며 수단변수를 사용한 조건부모형(條件附模型)의 경우에는 GMM의 추정방법에 의하였다. 검증결과, 실물자산(實物資産)의 투자수익률(投資收益率)이 주식수익률의 움직임과 관계가 높아 자산가격결정모형(資産價格決定模型)으로써 생산기저모형(生産基底模型)이 조건부모형에서나 무조건부모형에서 모두 의미가 있는 것으로 나타나 한국증권시장(韓國證券市場)에 대한 현실적(現實的) 설명력(說明力)이 높은 것으로 나타났다. 한편 CAPM과 APM은 자산가격결정모형으로써 타당성이 있었으나 소비기저모형(消費基底模型)은 모형의 추정계수인 상대위험회피계수(相對危險回避係數)가 비유의적(非有意的)으로 나타났으며 모형의 적합성이 기각(棄却)되었다.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.305-317
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• 2006

Outliers adversely affect model identification, parameter estimation, and forecast in time series data. In particular, when outliers consist of a patch of additive outliers, the current outlier detection procedures suffer from the masking and swamping effects which make them inefficient. In this paper, we propose new outlier detection procedure based on high breakdown estimators, called as the dual robust filtering. Empirical and simulation studies in the autoregressive model with orders p show that the proposed procedure is effective.

• Journal of Digital Convergence
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• v.11 no.9
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• pp.159-166
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• 2013

In this paper, make a study decision problem called an optimal release policies after testing a software system in development phase and transfer it to the user. When correcting or modifying the software, finite failure non-homogeneous Poisson process model, presented and propose release policies of the life distribution, half-logistic property model which used to an area of reliability because of various shape and scale parameter. In this paper, discuss optimal software release policies which minimize a total average software cost of development and maintenance under the constraint of satisfying a software reliability requirement. In a numerical example, the parameters estimation using maximum likelihood estimation of failure time data, make out estimating software optimal release time. Software release time is used as prior information, potential security damages should be reduced.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.357-361
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• 2010

미래의 연별 최대 강수량 예측의 정확성을 향상시키는데 역사적 자료가 도움이 된다는 많은 연구 결과가 있었다. 관측의 오차와 자료의 손실로 역사자료를 이용한 강수 예측 방법은 절단자료의 분석을 중심으로 연구되었다. 대표적인 역사자료의 이용방법으로 조건부 적률을 이용한 B17B [Interagency Committee in Water Data, 1982], 조건부적률과적률 관계식을 이용한 Expected Moment Algorithm(EMA) [Cohn et al.;1997], 조건부 확률가중적률을 이용한 Partial Probability Weighted Moment (PPWM)[Wang ; 1991] 방법이 있다. 본 연구에서는 역사적 자료를 반영하는 방법에 있어 B17B와 EMA의 관계를 밝히고 그러한 관계가 PPWM에 동일하게 적용할 수 있음을 보였다. 우리는 B17B와 EMA의 관계를 적률방정식으로 표현하였고 PPWM에서 확률가중 적률 방정식을 정의함으로써 PPWM을 확장하였다. 본 연구에서 제안한 새로운 역사 자료를 이용한 강수예측 방법론을 Expected Probability Weighted Momemt (EPWM) 방법이라고 부르고 그 예측 방법의 성능을 다른 예측방법과 시뮬레이션 결과를 통해 비교하였다. 역사 자료 방법론의 비교는 Generalized Extreme Value (GEV) 분포를 이용하여 이루어졌으며, 각 방법론은 GEV분포의 형태모수(shape parameter)따라 다른 특성을 나타난다는 것을 보였다. 뿐만 아니라 여기서 제안한 EPWM 방법은 대부분의 경우에 좋은 추정량을 준다는 것을 보였다.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.865-877
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• 2009

While characterized initially as an urban-scale pollutant, ozone has increasingly been recognized as a regional and even global-scale phenomenon. The complexity of environmental data dynamics often requires models covering non-linearity. This study deals with modeling ozone with meteorology in Seoul area. The relationships are used to construct a nonlinear regression model relating ozone to meteorology. The model can be used to estimate that part of the trend in ozone levels that cannot be accounted for by trends in meteorology.

• 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.30 no.6
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• pp.957-981
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• 2017

A generalized linear mixed model is an extension of a generalized linear model that allows random effect as well as provides flexibility in developing a suitable model when observations are correlated or when there are other underlying phenomena that contribute to resulting variability. We describe maximum likelihood estimation methods for logistic regression models that include random effects - the Laplace approximation, Gauss-Hermite quadrature, adaptive Gauss-Hermite quadrature, and pseudo-likelihood. Applications are provided with social science problems by analyzing the effect of mental health and life satisfaction on volunteer activities from Korean welfare panel data; in addition, we observe that the inclusion of random effects in the model leads to improved analyses with more reasonable inferences.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.561-571
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• 2007

Local linear regression estimator is the most widely used nonparametric regression estimator which has a number of advantages over the traditional kernel estimators. It is well known that local linear estimator can produce erratic result in sparse regions in the realization of the design and the interpolation method of Hall and Turlach (1997) is the very efficient way to resolve this problem. However, it has been never pointed out that Hall and Turlach's interpolation method is very sensitive to outliers. In this paper, we propose the robust version of the interpolation method for adapting to sparse design. The finite sample properties of the method is compared with Hall and Turlach's method by the simulation study.