• Title/Summary/Keyword: 일반화 선형 혼합 모형

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Testing Independence in Contingency Tables with Clustered Data (집락자료의 분할표에서 독립성검정)

  • 정광모;이현영
    • The Korean Journal of Applied Statistics
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    • v.17 no.2
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    • pp.337-346
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    • 2004
  • The Pearson chi-square goodness-of-fit test and the likelihood ratio tests are usually used for testing independence in two-way contingency tables under random sampling. But both of these tests may provide false results for the contingency table with clustered observations. In this case we consider the generalized linear mixed model which includes random effects of clustering in addition to the fixed effects of covariates. Both the heterogeneity between clusters and the dependency within a cluster can be explained via generalized linear mixed model. In this paper we introduce several types of generalized linear mixed model for testing independence in contingency tables with clustered observations. We also discuss the fitting of these models through a real dataset.

A Study on Spatial and Temporal Distribution of a Pest via Generalized Linear Mixed Models (일반화선형혼합모형을 통한 해충밀도의 시공간분포 연구)

  • 박흥선;조기종
    • The Korean Journal of Applied Statistics
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    • v.17 no.2
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    • pp.185-196
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    • 2004
  • It is an important research area in Integrated Pest Management System to estimate the pest density within plants, because the artificial controls such as spraying pesticides or biological enemies depend on the information of pest density. This paper studies the population density distribution of two-spotted spider mite in glasshouse roses. As the data were collected repeatedly on the same subject, Subject-Specific and Population Averaged approaches are used and compared.

A Study for Recent Development of Generalized Linear Mixed Model (일반화된 선형 혼합 모형(GENERALIZED LINEAR MIXED MODEL: GLMM)에 관한 최근의 연구 동향)

  • 이준영
    • The Korean Journal of Applied Statistics
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    • v.13 no.2
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    • pp.541-562
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    • 2000
  • The generalized linear mixed model framework is for handling count-type categorical data as well as for clustered or overdispersed non-Gaussian data, or for non-linear model data. In this study, we review its general formulation and estimation methods, based on quasi-likelihood and Monte-Carlo techniques. The current research areas and topics for further development are also mentioned.

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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.

Generalized Linear Mixed Model for Multivariate Multilevel Binomial Data (다변량 다수준 이항자료에 대한 일반화선형혼합모형)

  • Lim, Hwa-Kyung;Song, Seuck-Heun;Song, Ju-Won;Cheon, Soo-Young
    • The Korean Journal of Applied Statistics
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    • v.21 no.6
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    • pp.923-932
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    • 2008
  • We are likely to face complex multivariate data which can be characterized by having a non-trivial correlation structure. For instance, omitted covariates may simultaneously affect more than one count in clustered data; hence, the modeling of the correlation structure is important for the efficiency of the estimator and the computation of correct standard errors, i.e., valid inference. A standard way to insert dependence among counts is to assume that they share some common unobservable variables. For this assumption, we fitted correlated random effect models considering multilevel model. Estimation was carried out by adopting the semiparametric approach through a finite mixture EM algorithm without parametric assumptions upon the random coefficients distribution.

Survey of Models for Random Effects Covariance Matrix in Generalized Linear Mixed Model (일반화 선형혼합모형의 임의효과 공분산행렬을 위한 모형들의 조사 및 고찰)

  • Kim, Jiyeong;Lee, Keunbaik
    • The Korean Journal of Applied Statistics
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    • v.28 no.2
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    • pp.211-219
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    • 2015
  • Generalized linear mixed models are used to analyze longitudinal categorical data. Random effects specify the serial dependence of repeated outcomes in these models; however, the estimation of a random effects covariance matrix is challenging because of many parameters in the matrix and the estimated covariance matrix should satisfy positive definiteness. Several approaches to model the random effects covariance matrix are proposed to overcome these restrictions: modified Cholesky decomposition, moving average Cholesky decomposition, and partial autocorrelation approaches. We review several approaches and present potential future work.

Hurdle Model for Longitudinal Zero-Inflated Count Data Analysis (영과잉 경시적 가산자료 분석을 위한 허들모형)

  • Jin, Iktae;Lee, Keunbaik
    • 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.

Cancer incidence and mortality estimations in Busan by using spatial multi-level model (공간 다수준 분석을 이용한 부산지역 암발생 및 암사망 추정)

  • Ko, Younggyu;Han, Junhee;Yoon, Taeho;Kim, Changhoon;Noh, Maengseok
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.5
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    • pp.1169-1182
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    • 2016
  • Cancer is a typical cause of death in Korea that becomes a major issue in health care. According to Cause of Death Statistics (2014) by National Statistical Office, SMRs (standardized mortality rates) in Busan were counted as the highest among all cities. In this paper, we used data of Busan Regional Cancer Center to estimate the extent of the cancer incidence rate and cancer mortality rate. The data are considered in small areas of administrative units such as Gu/Dong from years 2003 to 2009. All cancer including four major cancers (stomach cancer, colorectal cancer, lung cancer, liver cancer) have been analyzed. We carried out model selection and parameter estimation using spatial multi-level model incorporating a spatial correlation. For the spatial effects, CAR (conditional autoregressive model) has been assumed.

Multivariate Meta-Analysis Methods of Comparing the Sensitivity and Specificity of Two Diagnostic Tests (두 진단검사의 비교에 대한 민감도와 특이도의 다변량 메타분석법)

  • Nam, Seon-Young;Song, Hae-Hiang
    • Communications for Statistical Applications and Methods
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    • v.18 no.1
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    • pp.57-69
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    • 2011
  • Researchers are continuously trying to find innovative diagnostic tests and published articles are accumulating at an enormous rate in many medical fields. Meta-analysis enables previously published study results to be reviewed and summarized; therefore, an objective assessment of diagnostic tests can be done with a meta-analysis of sensitivities and specificities. Data obtained by applying two diagnostic tests to a well-defined group of diseased patients produce a pair of sensitivity and by applying the same medical tests to a group of non-diseased subjects produce a pair of specificity. The statistical tests in the meta-analysis need to consider the correlatedness of the results from two diagnostic tests applied to the same diseased and non-diseased subjects. The associations between two diagnostic test results are often found to be unequal for the diseased and non-diseased subjects. In this paper, multivariate meta-analytic methods are studied by taking into account the different associations between correlated variables. On the basis of Monte Carlo simulations, we evaluate the performance of the multivariate meta-analysis methods proposed in this paper.

Predicting claim size in the auto insurance with relative error: a panel data approach (상대오차예측을 이용한 자동차 보험의 손해액 예측: 패널자료를 이용한 연구)

  • Park, Heungsun
    • The Korean Journal of Applied Statistics
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    • v.34 no.5
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    • pp.697-710
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    • 2021
  • Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.