• Journal of the Korean Statistical Society
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• v.31 no.1
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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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• v.36 no.4
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• pp.523-542
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• 2007

In this paper we consider semiparametric random effect panel models that contain AR(p) disturbances. We derive the efficient score function and the information bound for estimating the slope parameters. We make minimal assumptions on the distribution of the random errors, effects, and the regressors, and provide semiparametric efficient estimates of the slope parameters. The present paper extends the previous work of Park et al.(2003) where AR(1) errors were considered.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.203-209
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• 2009

Frailty models are useful for the analysis of correlated and/or heterogeneous survival data. However, the inferences of fixed parameters, rather than random effects, have been mainly studied. The prediction (or estimation) of random effects is also practically useful to investigate the heterogeneity of the hospital or patient effects. In this paper we propose how to extend the prediction method for random effects in HGLMs (hierarchical generalized linear models) to log-normal semiparametric frailty models with nonparametric baseline hazard. The proposed method is demonstrated by a simulation study.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.541-550
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• 2001

This paper considers a panel data regression model in which the disturbances follow a nested error components with serial correlation. Given this model, this paper derives several Lagrange Multiplier(LM) testis for the presence of serial correlation as well as random individual effects, nested effects, and for existence of serial correlation given random individual and nested effects.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.19-25
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• 2001

Random effects models which describe the dependence via random effects in various correlated data have recently received considerable attention in the biomedical literature. They include mixed linear models (MLMs), generatized linear mixed models (GLMMS) and hierarchical generalized linear models (HGLMs). For the inference Lee and Nelder (2000) proposed the first-and second-order REML (restricted maximum likelihood) methods based on hierarchical-likelihood of tee and Welder (1996). In this paper, for Poisson-gamma HGLMs the new methods are theoretically compared with marginal likelihood methods and both methods are illustrated by two practical examples.

• Korean Journal of Child Studies
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• v.22 no.4
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• pp.167-188
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• 2001

This study investigated the relationship of daily life activity experiences to violent behaviors and their moderating effects on the relationship between stress and violent behaviors in early adolescence. A sample of 134 second year middle school students carried electronic watches for one week and provided reports on their objective activity situation and subjective states when signalled at random times. Stress was positively correlated with violent behaviors. Daily activity experiences were correlated with violent behaviors and moderated the relationship between stress and violent behaviors. More time spent in socializing and passive leisure, and negative emotional states during schoolwork and active leisure were correlated with higher violent behaviors. Lower motivational states during schoolwork were correlated with higher violent behaviors. Lower cognition of importance and attention states during schoolwork and higher cognition of importance and attention states during active leisure and maintenance activities were correlated with higher violent behaviors. Finally, the moderating effects of negative emotion during active leisure, motivation and attention states during schoolwork on the relationship of stress with violent behaviors were found among girls only.

• Journal of Labour Economics
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• v.43 no.4
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• pp.179-203
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• 2020

This study aims to compare and analyze the impact of labor unions on job training programs using two different longitudinal data, Workplace Panel Survey (WPS) and Korean Labor and Inocme Panel Study (KLIPS). By applying the Correlated Random Effect (CRE) model to both individual-level and establishment-level data, we observe that labor unions increase the likelihood that establishments provide employees with job training programs and workers participate in the trainings. Our results shed light on the role of labor unions to increase the opportunities of job training programs for workers.

• 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.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.287-301
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• 2018

It is common to encounter count data with excess zeros in various research fields such as the social sciences, natural sciences, medical science or engineering. Such count data have been explained mainly by zero-inflated Poisson model and extended models. Zero-inflated count data are also often correlated or clustered, in which random effects should be taken into account in the model. Frequentist approaches have been commonly used to fit such data. However, a Bayesian approach has advantages of prior information, avoidance of asymptotic approximations and practical estimation of the functions of parameters. We consider a Bayesian zero-inflated Poisson regression model with random effects for correlated zero-inflated count data. We conducted simulation studies to check the performance of the proposed model. We also applied the proposed model to smoking behavior data from the Regional Health Survey (2015) of the Korea Centers for disease control and prevention.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.969-976
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• 2008

Mixed linear models have been widely used in various correlated data including multivariate survival data. In this paper we extend hierarchical-likelihood(h-likelihood) approach for mixed linear models with right censored data to that for left censored data. We also allow a general random-effect structure and propose the estimation procedure. The proposed method is illustrated using a numerical data set and is also compared with marginal likelihood method.