• Title/Summary/Keyword: 일반화 선형모형

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Comparing the efficiency of dispersion parameter estimators in gamma generalized linear models (감마 일반화 선형 모형에서의 산포 모수 추정량에 대한 효율성 연구)

  • Jo, Seongil;Lee, Woojoo
    • The Korean Journal of Applied Statistics
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    • v.30 no.1
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    • pp.95-102
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    • 2017
  • Gamma generalized linear models have received less attention than Poisson and binomial generalized linear models. Therefore, many old-established statistical techniques are still used in gamma generalized linear models. In particular, existing literature and textbooks still use approximate estimates for the dispersion parameter. In this paper we study the efficiency of various dispersion parameter estimators in gamma generalized linear models and perform numerical simulations. Numerical studies show that the maximum likelihood estimator and Cox-Reid adjusted maximum likelihood estimator are recommended and that approximate estimates should be avoided in practice.

Generalized linear models versus data transformation for the analysis of taguchi experiment (다구찌 실험분석에 있어서 일반화선형모형 대 자료변환)

  • 이영조
    • The Korean Journal of Applied Statistics
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    • v.6 no.2
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    • pp.253-263
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    • 1993
  • Recent interest in Taguchi's methods have led to developments of joint modelling of the mean and dispersion in generalized linear models. Since a single data transformation cannot produce all the necessary conditions for an analysis, for the analysis of the Taguchi data, the use of the generalized linear models is preferred to a commonly used data transformation method. In this paper, we will illustrate this point and provide GLIM macros to implement the joint modelling of the mean and dispersion in generalized linear models.

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Estimation of nonlinear GARCH-M model (비선형 평균 일반화 이분산 자기회귀모형의 추정)

  • Shim, Joo-Yong;Lee, Jang-Taek
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.5
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    • pp.831-839
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    • 2010
  • Least squares support vector machine (LS-SVM) is a kernel trick gaining a lot of popularities in the regression and classification problems. We use LS-SVM to propose a iterative algorithm for a nonlinear generalized autoregressive conditional heteroscedasticity model in the mean (GARCH-M) model to estimate the mean and the conditional volatility of stock market returns. The proposed method combines a weighted LS-SVM for the mean and unweighted LS-SVM for the conditional volatility. In this paper, we show that nonlinear GARCH-M models have a higher performance than the linear GARCH model and the linear GARCH-M model via real data estimations.

Comparing the performance of likelihood ratio test and F-test for gamma generalized linear models (감마 일반화 선형 모형에서의 가능도비 검정과 F-검정 비교연구)

  • Jo, Seongil;Han, Jeongseop;Lee, Woojoo
    • The Korean Journal of Applied Statistics
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    • v.31 no.4
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    • pp.475-484
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    • 2018
  • Gamma generalized linear models are useful for non-negative and skewed responses. However, these models have received less attention than Poisson and binomial generalized linear models. In particular, hypothesis testing for the significance of regression coefficients has not been thoroughly studied. In this paper we assess the performance of various test statistics for gamma generalized linear models based on numerical studies. Our results show that the likelihood ratio test and F-type test are generally recommended and that the partial deviance test should be avoided in practice.

일반화 선형모형을 이용한 냉음극 형광램프의 휘도 측정 시 온도 및 습도의 영향에 대한 연구

  • 윤양기;길영수
    • Proceedings of the Korean Reliability Society Conference
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    • 2005.06a
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    • pp.281-286
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    • 2005
  • 휘도(Luminance)는 냉음극 형광램프(Cold Cathode Fluorescent Lamp : CCFL)의 신뢰성을 평가하는데 있어 중요한 항목으로 사용되고 있다. 본 연구에서는 휘도 측정시 주위 온도 및 습도에 따라 측정감이 어떻게 변화하는가를 일반화 선형모형(Generalize Linear Model)을 이용하여 알아보고, 측정시의 환경조건 및 측정 오차에 대한 지침을 제시할 수 있게 된다.

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Generalized Weighted Linear Models Based on Distribution Functions - A Frequentist Perspective (분포함수를 기초로 일반화가중선형모형)

  • 여인권
    • The Korean Journal of Applied Statistics
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    • v.17 no.3
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    • pp.489-498
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    • 2004
  • In this paper, a new form of linear models referred to as generalized weighted linear models is proposed. The proposed models assume that the relationship between the response variable and explanatory variables can be modelled by a distribution function of the response mean and a weighted linear combination of distribution functions of covariates. This form addresses a structural problem of the link function in the generalized linear models in which the parameter space may not be consistent with the space derived from linear predictors. The maximum likelihood estimation with Lagrange's undetermined multipliers is used to estimate the parameters and resampling method is applied to compute confidence intervals and to test hypotheses.

Review of Spatial Linear Mixed Models for Non-Gaussian Outcomes (공간적 상관관계가 존재하는 이산형 자료를 위한 일반화된 공간선형 모형 개관)

  • Park, Jincheol
    • The Korean Journal of Applied Statistics
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    • v.28 no.2
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    • pp.353-360
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    • 2015
  • Various statistical models have been proposed over the last decade for spatially correlated Gaussian outcomes. The spatial linear mixed model (SLMM), which incorporates a spatial effect as a random component to the linear model, is the one of the most widely used approaches in various application contexts. Employing link functions, SLMM can be naturally extended to spatial generalized linear mixed model for non-Gaussian outcomes (SGLMM). We review popular SGLMMs on non-Gaussian spatial outcomes and demonstrate their applications with available public data.

Maximum likelihood estimation of Logistic random effects model (로지스틱 임의선형 혼합모형의 최대우도 추정법)

  • Kim, Minah;Kyung, Minjung
    • 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.

A Ridge-type Estimator For Generalized Linear Models (일반화 선형모형에서의 능형형태의 추정량)

  • Byoung Jin Ahn
    • The Korean Journal of Applied Statistics
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    • v.7 no.1
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    • pp.75-82
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    • 1994
  • It is known that collinearity among the explanatory variables in generalized linear models inflates the variance of maximum likelihood estimators. A ridge-type estimator is presented using penalized likelihood. A method for choosing a shrinkage parameter is discussed and this method is based on a prediction-oriented criterion, which is Mallow's $C_L$ statistic in a linear regression setting.

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Three Dimensional CERES Plot in Generalized Linear Models (일반화선형모형에서의 3차원 CERES그림)

  • Kahng, Myung-Wook;Kim, Bu-Yong;Jeon, Jin-Young
    • 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.