• International Journal of Reliability and Applications
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• 제11권1호
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• pp.1-16
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• 2010

In this paper we will discuss the stochastic analysis of a three state semi-Markov reliability model. Maximum likelihood procedure will be used to obtain the estimators of the parameters included in this reliability model. Based on the assumption that the lifetime and repair time of the system units are generalized linear failure rate random variables, the reliability function of this system is obtained. Also, the distribution of the first passage time of this system will be derived. Some important special cases are discussed.

• Journal of the Korean Data and Information Science Society
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• 제28권4호
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• pp.927-936
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• 2017

To analyze longitudinal count data, Poisson linear mixed models are commonly used. In the models the random effects covariance matrix explains both within-subject variation and serial correlation of repeated count outcomes. When the random effects covariance matrix is assumed to be misspecified, the estimates of covariates effects can be biased. Therefore, we propose reasonable and flexible structures of the covariance matrix using autoregressive and moving average Cholesky decomposition (ARMACD). The ARMACD factors the covariance matrix into generalized autoregressive parameters (GARPs), generalized moving average parameters (GMAPs) and innovation variances (IVs). Positive IVs guarantee the positive-definiteness of the covariance matrix. In this paper, we use the ARMACD to model the random effects covariance matrix in Poisson loglinear mixed models. We analyze epileptic seizure data using our proposed model.

• 시스템엔지니어링학술지
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• 제16권1호
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• pp.84-97
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• 2020

This paper aims to develop an optimized inventory management method for safety critical Instrument and Control (I&C) components. In this regard, the paper focuses on estimating the consumption rate of I&C components using demand forecasting methods. The target component for this paper is the Foxboro SPEC-200 controller. This component was chosen because it has highest consumption rate among the safety critical I&C components in Korean OPR-1000 NPPs. Three analytical methods were chosen in order to develop the demand forecasting methods; Poisson, Generalized Linear Model (GLM) and Bootstrapping. The results show that the GLM gives better accuracy than the other analytical methods. This is because the GLM considers the maintenance level of the component by discriminating between corrective and preventive.

• 응용통계연구
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• 제17권3호
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• pp.431-443
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• 2004

본 연구에서는 질병지도작성(disease mapping)을 위해 인접지역의 정보를 효과적으로 활용할 수 있는 EB(empirical Bayes) 추정 법과 HGLM(hierarchial generalized linear model)을 기초로 한 추정법을 다룬다. 사례연구로 이 추정방법들을 이용하여 2000년 사망원인통계자료를 이용해 경상도 및 전라도의 112개 시$.$군$.$구 단위 행정자치구역별 45세 이상 폐암 사망률을 산출하고, 경상도 및 전라도 지역 폐암 사망률 지도를 작성한다. 아울러 제시된 방법들에 위해 얻어진 추정치들의 변동과 3년간 평균 사망률을 기준으로 구한 MSD(mean square deviation)를 이용하여 추정방법들의 특성을 비교 분석한다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.249-249
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• 2023

Spring drought forecasting in South Korea is essential due to the sknewness of rainfall which could lead to water shortage especially in spring when managed without prediction. Therefore, drought forecasting over South Korea was performed in the current study by thoroughly searching appropriate predictors from the lagged global climate variable, mean sea level pressure(MSLP), specifically in winter season for forecasting time lag. The target predictand defined as accumulated spring precipitation(ASP) was driven by the median of 93 weather stations in South Korea. Then, it was found that a number of points of the MSLP data were significantly cross-correlated with the ASP, and the points with high correlation were regionally grouped. The grouped variables with three regions: the Arctic Ocean (R1), South Pacific (R2), and South Africa (R3) were determined. The generalized linear model(GLM) was further applied for skewed marginal distribution in drought prediction. It was shown that the applied GLM presents reasonable performance in forecasting ASP. The results concluded that the presented regionalization of the climate variable, MSLP can be a good alternative in forecasting spring drought.

• Communications for Statistical Applications and Methods
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• 제1권1호
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• pp.112-118
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• 1994

The generalized least squares estimator in the linear regression model is equivalent to difference estimator irrespective of the particular form of the regressor matrix when the disturbances are generated by a seasonally autoregressive provess and autocorrelation is closed to unity.

• Communications for Statistical Applications and Methods
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• 제4권1호
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• pp.193-205
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• 1997

The multiprocess dynamic model provides a good framework for the modeling and analysis of the time series that contains outliers and is subject to abrupt changes in pattern. In this paper we consider the multiprocess discount generalized model with parameters having a dependent non-linear structure. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt change of pattern in parameters.

• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.583-595
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• 2004

For the Korean Economically Active Population Survey(EAPS), we consider the composite estimator based on logistic regression model to estimate the unemployment rate for small areas(Si/Gun). Also, small area estimation technique based on hierarchical generalized linear model is proposed to include the random effect which reflect the characteristic of the small areas. The proposed estimation techniques are applied to real domestic data which is from the Korean EAPS of Choongbuk. The MSE of these estimators are estimated by Jackknife method, and the efficiencies of small area estimators are evaluated by the RRMSE. As a result, the composite estimator based on logistic model is much more efficient than others and it turns out that the composite estimator can produce the reliable estimates under the current EAPS system.

• 한국통신학회논문지
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• 제25권7A호
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• pp.967-977
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• 2000

본 논문은 도립전자 시스템을 LFR(Linear Fractional Representation)로 표현하여 얻어진 일반화 제어대상에 대하여 혼합 ${H_2}/H_{\infty}$ 제어기법을 적용한다. 먼저, 일반화 제어대상을 얻기 위하여, LFR로 표현한 도립진자의 선형 모델을 유도한다. LFR에서 고려한 구체적인 불확실성은 3개의 비선형 성분과 1개의 진자질량 불확실성이다. 유도된 선형모델에 하중함수를 더하여 LFR 모델을 확대함으로써 일반화된 제어대상을 얻는다. 다음으로, 이 일반화 제어대상에 대하여 혼합 ${H_2}/H_{\infty}$ 제어기를 설계한다. 혼합 ${H_2}/H_{\infty}$ 제어기 설계를 위해서 LMI(Linear Matrix Inequalities) 기법을 이요한다. 설계된 혼합 ${H_2}/H_{\infty}$ 제어기의 제어성능과 강건 안정성을 평가하기 위해서 모의실험과 실물실험을 통하여 $H_{\infty}$ 제어기와 비교한다. 실험결과, $H_{\infty}$ 제어때 보다 적은 피드백 정보만으로도 혼합 ${H_2}/H_{\infty}$ 제어기는 도립진자의 진자각도 측면에서 $H_{\infty}$ 제어기보다 나은 강건 안정성과 제어 성능을 보인다.

• Journal of the Korean Data and Information Science Society
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• 제27권5호
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• pp.1215-1224
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• 2016

고해상도 격자 단위 기후정보는 농업, 관광학, 생태학, 질병학 등 다양한 분야의 현상을 설명하는 중요 요인이다. 고해상도 기후정보는 동적 모형과 통계적 모형을 통해 얻을 수 있다. 통계적 모형은 동적 모형에 비해 계산 시간이 저렴하여 시공간 해상도가 높은 기후자료 생성에 주로 이용한다. 본 연구에서는 2003년부터 2012년까지 1월에 관측된 일 평균기온자료를 토대로 통계적 모형의 일 평균 기온을 생성하였다. 통계적 모형으로 선형모형을 기반으로한 일반선형모형, 일반화가법모형, 공간선형모형, 베이지안공간선형모형을 고려하였다. 예측성능평가를 위해 60개소의 지상관측소에서 관측된 일 평균기온을 모형적합 자료로 사용하여 352개소의 자동기상관측의 일 평균기온을 검증하였다. 평균제곱오차와 상관계수를 보면 베이지안공간모형의 예측성능이 다른 모형에 비해 상대적으로 우수하였다. 최종적으로 $1km{\times}1km$ 격자 단위 일 평균기온 지도를 생성하였다.