• Journal of the Korean Data and Information Science Society
• /
• v.16 no.4
• /
• pp.1017-1026
• /
• 2005

We consider the problem of modeling count data where the observation period is determined by the survival time of the individual under study. We assume random effects or frailty model to allow for a possible association between the death times and the counts. We assume that, given a random effect, the death times follow a Weibull distribution with a rate that depends on some covariates. For the counts, given the random effect, a Poisson process is assumed with the intensity depending on time and the covariates. A gamma model is assumed for the random effect. Maximum likelihood estimators of the model parameters are obtained. The model is applied to data set of patients with breast cancer who received a bone marrow transplant. A model for the time to death and the number of supportive transfusions a patient received is constructed and consequences of the model are examined.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.17 no.1
• /
• pp.46-50
• /
• 1993

The sensitivity study to evaluate the effects of parameters on the fatigue crack propagation life under the constant loadings is executed in the previous study of the authors. It is shown that the effect of the crack opening ratio is large comparatively. The purpose of this paper is to evaluate the effects of parameters on the fatigue crack propagation life under the random loadings. A new method of evaluation of the effective stress under the random loadings is developed. The sensitivity study of parameters on the fatigue crack propagation life under the random loadings is executed by using it.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.4
• /
• pp.346-352
• /
• 2011

This paper presents a system of road extraction for traffic images from a single camera. The road in the images is subject to large changes in appearance because of environmental effects. The proposed system is based on the integration of color correlograms and random forest. The color correlogram depicts the color properties of an image properly. Using the random forest, road extraction is formulated as a learning paradigm. The combined effects of color correlograms and random forest create a robust system capable of extracting the road in very changeable situations.

• Journal of the Korean Society for Precision Engineering
• /
• v.11 no.1
• /
• pp.166-176
• /
• 1994

The problem of reducing the vibration level of elastic plates driven by multiple random point forces is analyzed in this study. First, the analytical solution for the vibration level of finite thin plates with four simply supported edges under the action of multiple random point force is derived. By assuming the plates to be lightly damped, an approximate solution for the vibration level of the plate is obtained. A numerical study is carried out to determine an optimal spacing distance between the multiple point forces in order to produce a relative minimum in the plate's vibration level. The optimal spacing distance is shown to depend on the given excitation band. The effects of wave cancellation in the near field of the multiple point forces are discussed by using the equivalence of certain stationary random responses and deterministic pulse responese.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.2
• /
• pp.661-665
• /
• 2016

Background: Stomach cancer is the fifth most common cancer and the third leading cause of death among cancers throughout the world. Therefore, stomach cancer outcomes can affect health systems at the national and international levels. Although stomach cancer mortality and incidence rates have decreased in developed countries, these indicators have a raising trend in East Asian developing countries, particularity in Iran. In this study, we aimed to determine the time trend of age-standardized rates of stomach cancer in different districts of Iran from 2000 to 2010. Materials and Methods: Cases of cancer were registered using a pathology-based system during 2000-2007 and with a population-based system since 2008 in Iran. In this study, we collected information about the incidence of stomach cancer during a 10 year period for 31 provinces and 376 districts, with a total of 49,917 cases. We employed two statistical approaches (a random effects and a random effects Markov model) for modeling the incidence of stomach cancer in different districts of Iran during the studied period. Results: The random effects model showed that the incidence rate of stomach cancer among males and females had an increasing trend and it increased by 2.38 and 0.87 persons every year, respectively. However, after adjusting for previous responses, the random effects Markov model showed an increasing rate of 1.53 and 0.75 for males and females, respectively. Conclusions: This study revealed that there are significant differences between different areas of Iran in terms of age-standardized incidence rates of stomach cancer. Our study suggests that a random effects Markov model can adjust for effects of previous responses.

• The Korean Journal of Applied Statistics
• /
• v.30 no.6
• /
• pp.957-981
• /
• 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.

• Communications for Statistical Applications and Methods
• /
• v.7 no.2
• /
• pp.403-414
• /
• 2000

A diagnostic tool for testing homogeneity for random effects is proposed in unbalanced linear mixed model based on score statistic. The finite sample behavior of the test statistic is examined using Monte Carlo experiments examine the chi-square approximation of the test statistic under the null hypothesis.

• Communications for Statistical Applications and Methods
• /
• v.8 no.3
• /
• pp.603-613
• /
• 2001

A test based on score statistic is derived for detecting homoscedasticity of errors in unbalanced random effects linear model. A small simulation study is performed to investigate the finite sample behaviour of the test statistic which is known to have an asymptotic chi-square distribution under the null hypothesis.

• The Korean Journal of Applied Statistics
• /
• v.28 no.2
• /
• pp.211-219
• /
• 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.

• The Korean Journal of Applied Statistics
• /
• v.28 no.2
• /
• pp.123-136
• /
• 2015

Science has developed with great achievements after Galileo's discovery of the law depicting a relationship between observable variables. However, many natural phenomena have been better explained by models including unobservable random effects. A mixed effect model was the first statistical model that included unobservable random effects. The importance of the mixed effect models is growing along with the advancement of computational technologies to infer complicated phenomena; subsequently mixed effect models have extended to various statistical models such as hierarchical generalized linear models. Hierarchical likelihood has been suggested to estimate unobservable random effects. Our special issue about mixed effect models shows how they can be used in statistical problems as well as discusses important needs for future developments. Frequentist and Bayesian approaches are also investigated.