• The Korean Journal of Applied Statistics
• /
• v.21 no.1
• /
• pp.169-176
• /
• 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
• /
• v.13 no.2
• /
• pp.541-562
• /
• 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.

• Communications for Statistical Applications and Methods
• /
• v.16 no.1
• /
• pp.21-29
• /
• 2009

Engle and Russell (1998) proposed the ACD(Autoregressive Conditional Duration) model to explain the relationship between the prices and the duration times of the stocks. In this paper, we first introduce the various types of the ACD models such as the linear ACD, log ACD and Box-Cox ACD models and we evaluate the performance of the models for analysing the transaction data of the stocks in Korea.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.3
• /
• pp.475-483
• /
• 2009

In this paper we introduced a class of nonlinear time series models to analyse KOSPI data. We introduce the Generalized Power-Transformation TGARCH (GPT-TGARCH) model and the model includes Zakoian (1993) and Li and Li (1996) models as the special cases. We showed the effectiveness and efficiency of the new model based on KOSPI data.

• The Korean Journal of Applied Statistics
• /
• v.17 no.2
• /
• pp.337-346
• /
• 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.

• Communications for Statistical Applications and Methods
• /
• v.16 no.2
• /
• pp.265-275
• /
• 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
• /
• v.28 no.2
• /
• pp.335-342
• /
• 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.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.1
• /
• pp.21-32
• /
• 2010

Graphical regression is a paradigm for obtaining regression information using plots without model assumptions. The general goal of this approach is to find lowdimensional sufficient summary plots without loss of important information. Model assessments using residual plots are less likely to be successful in models that are not linear. As an alternative approach, marginal model plots provide a general graphical method for assessing the model. We apply the methods of graphical regression and model assessment using marginal model plots to the logistic regression model.

• Journal of the Korean Data and Information Science Society
• /
• v.8 no.2
• /
• pp.195-209
• /
• 1997

The joint modelling of mean and dispersion in quasi-likelihood models which greatly extend the scope of generalized linear models, is required in case that the dispersion parameter, the variance component of response variables, is not constant but changes by depending on any covariates. In this paper, by using statistical package GENSTAT(release 5.3.2, 1996) which makes a easily analyze real data through this joint modelling, we mention necessities that must consider this joint modelling rather than existing mean models through model checking based on graphic methods for esterase assay data introduced by Carrol and Ruppert(1987, pp.46-47), and then study methods finding reasonable joint model of mean and dispersion for this data.

• The Korean Journal of Applied Statistics
• /
• v.24 no.1
• /
• pp.207-216
• /
• 2011

Bootstrap is a resampling technique to find an estimate of parameters or to evaluate the estimate. This technique has been used in estimating parameters in linear model(LM) and generalized linear model(GLM). In this paper, we explore the possibility of applying Bootstrapping Residuals, Pairs, and an Estimating Equation that are most widely used in LM and GLM to the generalized estimating equation(GEE) algorithm for modelling repeatedly measured regression data sets. We compared three bootstrapping methods with coefficient and standard error estimates of GEE models from one simulated and one real data set. Overall, the estimates obtained from bootstrap methods are quite comparable, except that estimates from bootstrapping pairs are somewhat different from others. We conjecture that the strange behavior of estimates from bootstrapping pairs comes from the inconsistency of those estimates. However, we need a more thorough simulation study to generalize it since those results are coming from only two small data sets.