• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.169-176
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• 2008

We explore the structure and usefulness of three dimensional CERES plot as a basic tool for dealing with curvature as a function of the new predictors in generalized linear models. If predictors have nonlinear effects and there are nonlinear relationships among the predictors, the partial residual plot is not able to display the correct functional form of the predictors. Unlike this plots, the CERES plot can show the correct form. This is illustrated by simulated data.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.147-160
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• 2020

In this paper, we study an algorithm that automatically determines the orders of past observations and conditional mean values that play an important role in count time series models. Based on the orders of the ARIMA model, the algorithm constitutes the order candidates group for time series generalized linear models and selects the final model based on information criterion among the combinations of the order candidates group. To evaluate the proposed algorithm, we perform small simulations and empirical analysis according to underlying models and time series as well as compare forecasting performances with the ARIMA model. The results of the comparison confirm that the time series generalized linear model offers better performance than the ARIMA model for the count time series analysis. In addition, the empirical analysis shows better performance in mid and long term forecasting than the ARIMA model.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.265-275
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• 2009

This paper considers a linear regression model with a spatial autoregressive disturbance with ill-posed data and proposes the generalized maximum entropy(GME) estimator of regression coefficients. The performance of this estimator is investigated via Monte Carlo experiments. The results show that the GME estimator provides efficient and robust estimate for the unknown parameter.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.335-342
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• 2015

Joint hierarchical generalized linear models proposed by Molas et al. (2013) extend the simple longitudinal model into multiple models fitted jointly. It can easily handle the correlation of multivariate longitudinal data. In this paper, we apply this method to analyze KoGES cohort dataset. Fixed unknown parameters, random effects and variance components are estimated based on a standard framework of h-likelihood theory. Furthermore, based on the conditional Akaike information criterion the correlated covariance structure of random-effect model is selected rather than an independent structure.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.923-932
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• 2014

The Hurdle model can to analyze zero-inflated count data. This model is a mixed model of the logit model for a binary component and a truncated Poisson model of a truncated count component. We propose a new hurdle model with a general heterogeneous random effects covariance matrix to analyze longitudinal zero-inflated count data using modified Cholesky decomposition. This decomposition factors the random effects covariance matrix into generalized autoregressive parameters and innovation variance. The parameters are modeled using (generalized) linear models and estimated with a Bayesian method. We use these methods to carefully analyze a real dataset.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.857-871
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• 2013

Export credit insurance is a policy tool for export growth. In the era of free trade under the governance of WTO, export credit insurance is still allowed as one of the few instruments to increase exports. This paper, using data on short-term export insurance contracts issued to foreign subsidiaries of Korean companies, calculates the expected loss per exposure by combining the effect of risk factors (credit rate of foreign importers, size of mother company, and payment period) on loss frequency and loss severity in different levels. We, applying generalized linear models (GLM), first fit loss frequency and loss severity to negative binomial and lognormal distribution, respectively, and then estimate the loss frequency rate per contract and the ratio of loss severity to coverage amount. Finally, we calculate the expected loss per exposure for each level of risk factors by combining these two rates. Based on the result of statistical analysis, we present the implication for the current premium rate of export insurance.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.27-41
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• 2008

A graphical method of checking the adequacy of a generalized linear model is proposed. The graph helps to assess the assumption that the link function of mean can be expressed as a linear combination of explanatory variables in the generalized linear model. For the graph the boosting technique is applied to estimate nonparametrically the relationship between the link function of the mean and the explanatory variables, though any other nonparametric regression methods can be applied. Through simulation studies with normal and binary data, the effectiveness of the graph is demonstrated. And we list some limitations and technical details of the graph.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.1-9
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• 1996

본 논문에서는 일반화 회귀모형의 회귀모수${\beta}$에 대한 사전정보의 형태에 따른 각 추정량들에 대하여 연구하였다. 먼저 사전정보가 ${\beta}$에 대한 사전분포로 주어지는 경우에 해당하는 베이지안 회귀추정량을 제시하였고, 다른 하나는 ${\beta}$에 대한 사전정보모형으로 선형회귀모형식이 주어진 경우의 일반화 혼합회귀추정량에 대하여 연구하였다. 두가지 경우로부터 얻어진 각 추정량의 정도를 알아보기 위하여 각 추정량의 공분산행렬을 이 용하여 서로 비교하여 보았다. 각 추정량의 분산비들을 이용하여 일반적으로 일반화 혼합회귀추정량이 베이지안 회귀추정량들보다 비교적 작은 분산값을 가진다는 결론을 얻었다.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.413-420
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• 2013

Stochastic weather generator is a commonly used tool to simulate daily weather time series. Recently, a generalized linear model(GLM) has been proposed as a convenient approach to tting these weather generators. In the present paper, a stochastic weather generator is considered to model the time series of daily temperatures for Seoul South Korea. As a covariate, precipitation occurrence is introduced to a relate short-term predictor to short-term predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate a time series of seasonal mean temperatures in the GLM weather generator as a covariate.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.57-57
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• 2019

최근 수문기상학 분야에서 레이더 강수량을 활용한 응용연구가 활발하게 진행되고 있다. 하지만 레이더 강수량은 경험적으로 설정된 레이더 반사도-강우강도 관계식을 활용하여 레이더 강수량을 산정하기 때문에 실제지상에 도달하는 강수량과 정량적인 오차가 필연적으로 발생한다. 따라서 고해상도의 레이더 강수량을 활용한 신뢰도 높은 수문해석을 위하여 레이더 강수량의 편의보정이 필수적으로 선행되어야한다. 본 연구에서는 불확실성을 고려한 레이더 강수량 편의보정을 위하여 Bayesian 추론기법과 일반화 선형모형(generalized linear model)을 연계하여 레이더 강수량 편의보정 매개변수를 산정하였다. 일반화 선형모형을 적용한 레이더 강수량 편의보정 결과는 현재 널리 사용되고 있는 평균보정(mean field bias) 기법에 비하여 통계지표가 개선된 레이더 강수량 편의보정 결과를 도출하였다. 추가적으로 지형학적 특성에 따른 레이더 강수량 편의보정 매개변수의 변동성을 분석하여 고도 및 이격거리에 따른 편의보정 매개변수의 지역화 공식을 제시하였다. 본 연구를 통하여 개발된 레이더 강수량 편의보정 매개변수 산정 및 지역화 연구는 레이더 관측전략 수립과정에 유용한 기초자료로 활용될 것으로 판단된다.