• Communications for Statistical Applications and Methods
• /
• v.13 no.3
• /
• pp.701-718
• /
• 2006

In cancer microarray experiments, the experimenter or patient which is nested in each experimenter often shows quite heterogeneous error variability, which should be estimated for identifying a source of variation. Our study describes a Bayesian method which utilizes clinical information for identifying a set of DE genes for the class of subtypes as well as assesses and examines the experimenter effect and patient effect which is nested in each experimenter as a source of variation. We propose a Bayesian multilevel mixed effect model based on analysis of covariance (ANACOVA). The Bayesian multilevel mixed effect model is a combination of the multilevel mixed effect model and the Bayesian hierarchical model, which provides a flexible way of defining a suitable correlation structure among genes.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.2
• /
• pp.363-369
• /
• 2010

In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.2
• /
• pp.379-386
• /
• 2004

This paper deals with a mixed logit model for vaccination data. The effect of a newly developed vaccine for a certain chicken disease can be evaluated by a noninfection rate after injecting chicken with the disease vaccine. But there are a lot of factors that might affect the noninfecton rate. Some of these are fixed and others are random. Random factors are sometimes coming from the sampling scheme for choosing experimental units. This paper suggests a mixed model when some fixed factors need to have different experimental sizes by an experimental design and illustrates how to estimate parameters in a suggested model.

• Communications for Statistical Applications and Methods
• /
• v.4 no.1
• /
• pp.33-39
• /
• 1997

Tsutakawa(1988) proposed a mixed model for using empirical Bayes method to study the geographic variability in mortality rates of a disease. In particular cases of the analysis in mortality rate, we need to consider the effect of time. If observed data are collected annually for the time period, then time effect will be emphasized. Here, an extended model for estimating the geographic effect and the mortality rates of the disease with time effect is proposed.

• Communications for Statistical Applications and Methods
• /
• v.30 no.1
• /
• pp.65-73
• /
• 2023

Contemporary biomedical data often involve an ill-posed problem owing to small sample size and large number of multi-collinear variables. Partial least squares (PLS) method could be a plausible alternative to an ill-conditioned ordinary least squares. However, in the case of a PLS model that includes a random-effect, how to deal with a random-effect or mixed effects remains a widely open question worth further investigation. In the present study, we propose a modified multivariate PLS method implementing mixed-effect model (PLSM). The advantage of PLSM is its versatility in handling serial longitudinal data or its ability for taking a randomeffect into account. We conduct simulations to investigate statistical properties of PLSM, and showcase its real clinical application to predict treatment outcome of esthetic surgical procedures of human faces. The proposed PLSM seemed to be particularly beneficial 1) when random-effect is conspicuous; 2) the number of predictors is relatively large compared to the sample size; 3) the multicollinearity is weak or moderate; and/or 4) the random error is considerable.

• Geomechanics and Engineering
• /
• v.29 no.3
• /
• pp.301-310
• /
• 2022

Accurate prediction of mixed ground conditions ahead of a tunnel face is of vital importance for safe excavation using tunnel boring machines (TBMs). Previous studies have primarily focused on electrical resistivity surveys from the ground surface for geotechnical investigation. In this study, an FE (finite element) numerical model was developed to simulate electrical resistivity surveys for the prediction of risky mixed ground conditions in front of a tunnel face. The proposed FE model is validated by comparing with the apparent electrical resistivity values obtained from the analytical solution corresponding to a vertical fault on the ground surface (i.e., a simplified model). A series of parametric studies was performed with the FE model to analyze the effect of geological and sensor geometric conditions on the electrical resistivity survey. The parametric study revealed that the interface slope between two different ground formations affects the electrical resistivity measurements during TBM excavation. In addition, a large difference in electrical resistivity between two different ground formations represented the dramatic effect of the mixed ground conditions on the electrical resistivity values. The parametric studies of the electrode array showed that the proper selection of the electrode spacing and the location of the electrode array on the tunnel face of TBM is very important. Thus, it is concluded that the developed FE numerical model can successfully predict the presence of a mixed ground zone, which enables optimal management of potential risks.

• 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.

• The Korean Journal of Applied Statistics
• /
• v.28 no.2
• /
• pp.353-360
• /
• 2015

Various statistical models have been proposed over the last decade for spatially correlated Gaussian outcomes. The spatial linear mixed model (SLMM), which incorporates a spatial effect as a random component to the linear model, is the one of the most widely used approaches in various application contexts. Employing link functions, SLMM can be naturally extended to spatial generalized linear mixed model for non-Gaussian outcomes (SGLMM). We review popular SGLMMs on non-Gaussian spatial outcomes and demonstrate their applications with available public data.

• The Korean Journal of Applied Statistics
• /
• v.23 no.6
• /
• pp.1169-1178
• /
• 2010

This article consider a performance model selection based on AIC under unbalanced deign in linear mixed effect models. Vaida and Balanchard (2005) proposed conditional AIC for model selection in linear mixed effect models when the prediction of random effects is of primary interest. Theoretical properties of cAIC and related criteria have been investigated by Liang et al. (2008) and Greven and Kneib (2010). However, all of the simulation studies were performed under a balanced design. Even though functional form of AIC remain same even under the unbalanced deign, it is worthwhile to investigate performance of AIC based model selection criteria under the unbalanced design. The simulation study in this article shows how unbalancedness affects model selection in linear mixed effect models.

• The Korean Journal of Applied Statistics
• /
• v.25 no.3
• /
• pp.457-464
• /
• 2012

Small area estimation is a statistical inference method to overcome the large variance due to the small sample size allocated in a small area. Recently some nonparametric estimators have been applied to small area estimation. In this study, we suggest a nonparametric mixed effect small area estimator using kernel smoothing and compare the small area estimators using labor statistics.