• Journal of the Korean Statistical Society
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• 제31권1호
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• pp.93-107
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• 2002

Random effects generalised linear models are useful for analysing clustered count data in which responses are usually correlated. We propose a Bayesian approach to parameter estimation and variable selection in random effects generalised linear models for count data. A simple Gibbs sampling algorithm for parameter estimation is presented and a simple and efficient variable selection is done by using the Gibbs outputs. An illustrative example is provided.

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.349-360
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• 1995

This paper deals with the robustness properties of the minimum disparity estimation in linear regression models. The estimators defined as statistical quantities whcih minimize the blended weight Hellinger distance between a weighted kernel density estimator of the residuals and a smoothed model density of the residuals. It is shown that if the weights of the density estimator are appropriately chosen, the estimates of the regression parameters are robust.

• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.519-527
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• 2004

Relationship between the partial likelihood ratio statistics for logisitic models and the partial goodness-of-fit statistics for corresponding log-linear models is discussed. This paper shows how definitions of suppression in logistic model can be adapted for log-linear model and how they are related to confounding in terms of collapsibility for categorical data. Several $2{times}2{times}2$ contingency tables are illustrated.

• 한국수자원학회논문집
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• 제30권3호
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• pp.279-291
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• 1997

본 연구는 중소하천 유역에서 강우-유출과정의 모의절 위한 정도 높은 개념적 모형을 결정하기 위하여 기존의 개념적 선형 모형인 Clark 모형, Nash 모형과 개념적 비선형 모형인 Laurenson 모형과 WBN 모형을 위천 유역을 대상으로 적용 및 비교검토를 하였다. 선형 모형에서 산정된 매개변수들의 변동성은 비선형 모형의 매개변수의 변동성 보다 크게 나타났으며, 계측유역에서 4개 개념적 모형으로부터 합성한 수문곡선을 분석한 결과 첨두유량은 4개 모형간에 큰 차이가 없으나 첨두시간은 비선형 모형인 Laurenson 모형과 WBN 모형이 선형 모형보다 적합도가 높은 것으로 나타났다. 순위검정에 위한 Friedman의 이원분산분석결과 첨두유량을 예측하기 위한 4개 모형의 가능성간에는 모든 유역에서 어떤 유의한 차이도 나타내고 있지 않으나 유출수문곡선 전체의 재현성에서 비선형모형들이 선형 모형들 보다 다소 우월한 것으로 나타났다.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2009년도 춘계학술대회
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• pp.185-192
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• 2009

The research contributes extending and reviewing of restricted (constrained) and unrestricted (unconstrained) models in GLM(Generalized Linear Models). The paper includes the methodology for finding EMS(Expected Mean Square) and $F_0$ ratio. The results can be applied to the gauge R&R(Reproducibility and Repeatability) in MSA(Measurement System Analysis).

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.755-764
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• 2006

Most graphical methods for categorical data can describe the structure of data and represent a measure of association among categorical variables. Among them the polyhedron plot represents sequential relationships among hierarchical log-linear models for a multidimensional contingency table. This kind of plot could be explored to describe the differences among sequential models. In this paper we suggest graphical methods, containing all the information, that reflect the relationship among all log-linear models in a certain hierarchical structure. We use the ideas of a correlation diagram.

• 응용통계연구
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• 제6권2호
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• pp.253-263
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• 1993

최근 다구찌 실험에 대한 관심이 고조되어 일반화 선형모형에서 평균과 분산의 동시모형화가 연구되고 있다. 하나의 자료 변환만으로는 자료분석에 필요한 모든 조건들을 만족시킬 수 없기 때문에 다구찌 품질실험의 자료들을 일반화 선형모형으로 분석하는 것이 바람직하다. 본 논문에서는 이 자료변환법과 일반선형모형을 이용한 분석법을 소개, 비교하고 일반화 선형모형을 다구찌 자료에 적용할 수 있는 GLIM 프로그램을 제시한다.

• Journal of the Korean Statistical Society
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• 제19권2호
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• pp.171-175
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• 1990

A diagnostic test for detecting nonconstant variance in mixed linear models based on the score statistic is derived through the technique of model expansion, and compared to the log likelihood ratio test.

• 한국기후변화학회지
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• 제34권20호
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• pp.8257-8271
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• 2021

This study presents the ability of seasonal forecast models to represent the observed midlatitude teleconnection associated with El Niño-Southern Oscillation (ENSO) events over the North American region for the winter months of December, January, and February. Further, the impacts of the associated errors on regional forecast performance for winter temperatures are evaluated, with a focus on 1-month-lead-time forecasts. In most models, there exists a strong linear relationship of temperature anomalies with ENSO, and, thus, a clear anomaly sign separation between both ENSO phases persists throughout the winter, whereas linear relationships are weak in observations. This leads to a difference in the temperature forecast performance between the two ENSO phases. Forecast verification scores show that the winter-season warming events during El Niño in northern North America are more correctly forecast in the models than the cooling events during La Niña and that the winter-season cooling events during El Niño in southern North America are also more correctly forecast in the models than warming events during La Niña. One possible reason for this result is that the remote atmospheric teleconnection pattern in the models is almost linear or symmetric between the El Niño and La Niña phases. The strong linear atmospheric teleconnection appears to be associated with the models' failure in simulating the westward shift of the tropical Pacific Ocean rainfall response for the La Niña phase as compared with that for the El Niño phase, which is attributed to the warmer central tropical Pacific in the models. This study highlights that understanding how the predictive performance of climate models varies according to El Niño or La Niña phases is very important when utilizing predictive information from seasonal forecast models.

• Communications for Statistical Applications and Methods
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• 제30권5호
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• pp.437-451
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• 2023

Generalized linear models and generalized linear mixed models (GLMMs) are fundamental tools for predictive analyses. In insurance, GLMMs are particularly important, because they provide not only a tool for prediction but also a theoretical justification for setting premiums. Although thousands of resources are available for introducing GLMMs as a classical and fundamental tool in statistical analysis, few resources seem to be available for the insurance industry. This study targets insurance professionals already familiar with basic actuarial mathematics and explains GLMMs and their linkage with classical actuarial pricing tools, such as the Buhlmann premium method. Focus of the study is mainly on the modeling aspect of GLMMs and their application to pricing, while avoiding technical issues related to statistical estimation, which can be automatically handled by most statistical software.