• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.1-10
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• 2017

Longitudinal data of Korean child academic achievement have been used to find the significant exploratory variables under the assumption of independent repeated measured data. Using the exploratory variables in previous research works, we analyze the linear mixed model incorporating the fixed and random effects for child academic achievement to detect the significant exploratory variables. Korea welfare panel study data observed three times between 2006 and 2012 by additional survey for children. The child academic achievement is evaluated by the sum of academic achievements of Korean, English and Mathematics. We also investigate the multicollinearity and the missing mechanism and select some popular correlation matrices to analyze the linear mixed model.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.203-213
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• 2017

We study estimation and inference of joint conditional distributions of bivariate longitudinal outcomes using regression models and copulas. We consider a class of time-varying transformation models and combine the two marginal models using Gaussian copulas to estimate the joint models. Our models and estimation method can be applied in many situations where the conditional mean-based models are inadequate. Gaussian copulas combined with time-varying transformation models may allow convenient and easy-to-interpret modeling for the joint conditional distributions for bivariate longitudinal data. We apply our method to an epidemiological study of repeatedly measured bivariate cholesterol data.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.689-700
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• 2016

We study estimation and inference of the joint conditional distributions of bivariate longitudinal outcomes using regression models and copulas. For the estimation of marginal models we consider a class of time-varying transformation models and combine the two marginal models using nonparametric empirical copulas. Regression parameters in the transformation model can be obtained as the solution of estimating equations and our models and estimation method can be applied in many situations where the conditional mean-based models are not good enough. Nonparametric copulas combined with time-varying transformation models may allow quite flexible modeling for the joint conditional distributions for bivariate longitudinal data. We apply our method to an epidemiological study of repeatedly measured bivariate cholesterol data.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.311-325
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• 2022

For count responses, the situation of excess zeros often occurs in various research fields. Zero-inflated model is a common choice for modeling such count data. Bayesian inference for the zero-inflated model has long been recognized as a hard problem because the form of conditional posterior distribution is not in closed form. Recently, however, Pillow and Scott (2012) and Polson et al. (2013) proposed a Pólya-Gamma data-augmentation strategy for logistic and negative binomial models, facilitating Bayesian inference for the zero-inflated model. We apply Bayesian zero-inflated negative binomial regression model to longitudinal pharmaceutical data which have been previously analyzed by Min and Agresti (2005). To facilitate posterior sampling for longitudinal zero-inflated model, we use the Pólya-Gamma data-augmentation strategy.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.103-117
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• 2017

In longitudinal data analysis, the serial correlation of repeated outcomes must be taken into account using covariance matrix. Modeling of the covariance matrix is important to estimate the effect of covariates properly. However, It is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcome the restrictions, several Cholesky decomposition approaches for the covariance matrix were proposed: modified autoregressive (AR), moving average (MA), ARMA Cholesky decompositions. In this paper we review them and compare the performance of the approaches using simulation studies.

• 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.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.241-250
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• 2015

An important scientific objective of longitudinal studies involves tracking the probability of a subject having certain health condition over the course of the study. Proper definitions and estimates of disease risk tracking have important implications in the design and analysis of long-term biomedical studies and in developing guidelines for disease prevention and intervention. We study in this paper a class of rank-tracking probabilities to describe a subject's conditional probabilities of having certain health outcomes at two different time points. Linear mixed effects models are considered to estimate the tracking probabilities and their ratios of interest. We apply our methods to an epidemiological study of childhood cardiovascular risk factors.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.85-94
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• 2013

As the financial market becomes larger, the loss increases due to the failure of the credit risk managements from the poor management of the customer information or poor decision-making. Thus, the credit risk management also becomes more important and it is essential to develop a credit scoring model, which is a fundamental tool used to minimize the credit risk. Credit scoring models have been studied and developed only for binary target variables. In this paper, we consider other types of target variables such as ordinal multinomial data or longitudinal binary data and suggest credit scoring models. We then apply our developed models to real data and random data, and investigate their performance through Kolmogorov-Smirnov statistic.

• 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.