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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Korean Journal of Applied Statistics
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The Korean Statistical Society
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Volume & Issues
Volume 28, Issue 6 - Dec 2015
Volume 28, Issue 5 - Oct 2015
Volume 28, Issue 4 - Aug 2015
Volume 28, Issue 3 - Jun 2015
Volume 28, Issue 2 - Apr 2015
Volume 28, Issue 1 - Feb 2015
Selecting the target year
Review of Mixed-Effect Models
Lee, Youngjo ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 123~136
DOI : 10.5351/KJAS.2015.28.2.123
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.
A Hierarchical Bayesian Modeling of Temporal Trends in Return Levels for Extreme Precipitations
Kim, Yongku ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 137~149
DOI : 10.5351/KJAS.2015.28.2.137
Flood planning needs to recognize trends for extreme precipitation events. Especially, the r-year return level is a common measure for extreme events. In this paper, we present a nonstationary temporal model for precipitation return levels using a hierarchical Bayesian modeling. For intensity, we model annual maximum daily precipitation measured in Korea with a generalized extreme value (GEV). The temporal dependence among the return levels is incorporated to the model for GEV model parameters and a linear model with autoregressive error terms. We apply the proposed model to precipitation data collected from various stations in Korea from 1973 to 2011.
Interblock Information from BIBD Mixed Effects
Choi, Jaesung ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 151~158
DOI : 10.5351/KJAS.2015.28.2.151
This paper discusses how to use projections for the analysis of data from balanced incomplete block designs. A model is suggested as a matrix form for the interblock analysis. A second set of treatment effects can be found by projections from the suggested interblock model. The variance and covariance matrix of two estimated vectors of treatment effects is derived. The uncorrelation of two estimated vectors can be verified from their covaraince structure. The fitting constants method is employed for the calculation of block sum of squares adjusted for treatment effects.
Variational Mode Decomposition with Missing Data
Choi, Guebin ; Oh, Hee-Seok ; Lee, Youngjo ; Kim, Donghoh ; Yu, Kyungsang ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 159~174
DOI : 10.5351/KJAS.2015.28.2.159
Dragomiretskiy and Zosso (2014) developed a new decomposition method, termed variational mode decomposition (VMD), which is efficient for handling the tone detection and separation of signals. However, VMD may be inefficient in the presence of missing data since it is based on a fast Fourier transform (FFT) algorithm. To overcome this problem, we propose a new approach based on a novel combination of VMD and hierarchical (or h)-likelihood method. The h-likelihood provides an effective imputation methodology for missing data when VMD decomposes the signal into several meaningful modes. A simulation study and real data analysis demonstrates that the proposed method can produce substantially effective results.
Estimation Methods for Population Pharmacokinetic Models using Stochastic Sampling Approach
Kim, Kwang-Hee ; Yoon, Jeong-Hwa ; Lee, Eun-Kyung ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 175~188
DOI : 10.5351/KJAS.2015.28.2.175
This study is about estimation methods for the population pharmacokinetic and pharmacodymic model. This is a nonlinear mixed effect model, and it is difficult to find estimates of parameters because of nonlinearity. In this study, we examined theoretical background of various estimation methods provided by NONMEM, which is the most widely used software in the pharmacometrics area. We focused on estimation methods using a stochastic sampling approach - IMP, IMPMAP, SAEM and BAYES. The SAEM method showed the best performance among methods, and IMPMAP and BAYES methods showed slightly less performance than SAEM. The major obstacle to a stochastic sampling approach is the running time to find solution. We propose new approach to find more precise initial values using an ITS method to shorten the running time.
A Bootstrap Lagrangian Multiplier Test for Market Microstructure Noise in Financial Assets
Kim, Hyo Jin ; Shin, Dong Wan ; Park, Jonghun ; Lee, Sang-Goo ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 189~200
DOI : 10.5351/KJAS.2015.28.2.189
Stationary bootstrapping is applied to a Lagrangian multiplier (LM) test to test market microstructure noise (MMN) in financial asset prices. A Monte-Carlo experiment shows that the bootstrapping method improves the size of the original LM test which has some size distortion for conditional heteroscedastic models. The proposed test is illustrated for real data sets like KOSPI index and Won-Dollar exchange rate.
Assessing Correlation between Two Variables in Repeated Measurements using Mixed Effect Models
Han, Kyunghwa ; Jung, Inkyung ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 201~210
DOI : 10.5351/KJAS.2015.28.2.201
Repeated measurements on each variables of interest often arise in bioscience or medical research. We need to account for correlations among repeated measurements to assess the correlation between two variables in the presence of replication. This paper reviews methods to estimate a correlation coefficient between two variables in repeated measurements using the variance-covariance matrix of linear mixed effect models. We analyze acoustic radiation force impulse imaging (ARFI) data to assess correlation between three shear wave velocity (SWV) measurements in liver or spleen and spleen length by ultrasonography. We present how to obtain parameter estimates for the variance-covariance matrix and correlations in mixed effects models using PROC MIXED in SAS.
Survey of Models for Random Effects Covariance Matrix in Generalized Linear Mixed Model
Kim, Jiyeong ; Lee, Keunbaik ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 211~219
DOI : 10.5351/KJAS.2015.28.2.211
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.
Gamma Mixed Model to Improve Sib-Pair Linkage Analysis
Kim, Jeonghwan ; Suh, Young Ju ; Won, Sungho ; Nah, Jeung Weon ; Lee, Woojoo ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 221~230
DOI : 10.5351/KJAS.2015.28.2.221
Traditionally, sib-pair linkage analysis with repeated measures has employed linear mixed models, but it suffers from the lack of power to find genetic marker loci associated with a phenotype of interest. In this paper, we use a gamma mixed model to improve sib-pair linkage analysis and compare it with a linear mixed model in terms of power and Type I error. We illustrate that the use of gamma mixed model can achieve higher power than linear mixed model with Genetic Analysis Workshop 13 data.
Zero In ated Poisson Model for Spatial Data
Han, Junhee ; Kim, Changhoon ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 231~239
DOI : 10.5351/KJAS.2015.28.2.231
A Poisson model is the first choice for counts data. Quasi Poisson or negative binomial models are usually used in cases of over (or under) dispersed data. However, these models might be unsuitable if the data consist of excessive number of zeros (zero inflated data). For zero inflated counts data, Zero Inflated Poisson (ZIP) or Zero Inflated Negative Binomial (ZINB) models are recommended to address the issue. In this paper, we further considered a situation where zero inflated data are spatially correlated. A mixed effect model with random effects that account for spatial autocorrelation is used to fit the data.
Rank Tracking Probabilities using Linear Mixed Effect Models
Kwak, Minjung ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 241~250
DOI : 10.5351/KJAS.2015.28.2.241
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.
Introduction to the Indian Buffet Process: Theory and Applications
Lee, Youngseon ; Lee, Kyoungjae ; Lee, Kwangmin ; Lee, Jaeyong ; Seo, Jinwook ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 251~267
DOI : 10.5351/KJAS.2015.28.2.251
The Indian Buffet Process is a stochastic process on equivalence classes of binary matrices having finite rows and infinite columns. The Indian Buffet Process can be imposed as the prior distribution on the binary matrix in an infinite feature model. We describe the derivation of the Indian buffet process from a finite feature model, and briefly explain the relation between the Indian buffet process and the beta process. Using a Gaussian linear model, we describe three algorithms: Gibbs sampling algorithm, Stick-breaking algorithm and variational method, with application for finding features in image data. We also illustrate the use of the Indian Buffet Process in various type of analysis such as dyadic data analysis, network data analysis and independent component analysis.
Likelihood-Based Inference of Random Effects and Application in Logistic Regression
Kim, Gwangsu ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 269~279
DOI : 10.5351/KJAS.2015.28.2.269
This paper considers inferences of random effects. We show that the proposed confidence distribution (CD) performs well in logistic regression for random intercepts with small samples. Real data analyses are also done to identify the subject effects clearly.
Sparse Matrix Computation in Mixed Effects Model
Son, Won ; Park, Yong-Tae ; Kim, Yu Kyeong ; Lim, Johan ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 281~288
DOI : 10.5351/KJAS.2015.28.2.281
In this paper, we study an approximate procedure to evaluate a penalized maximum likelihood estimator (MLE) for a mixed effects model. The procedure approximates the Hessian matrix of the penalized MLE with a structured sparse matrix or an arrowhead type matrix to speed its computation. In this paper, we numerically investigate the gain in computation time as well as approximation error from the considered approximation procedure.
Bio-Equivalence Analysis using Linear Mixed Model
An, Hyungmi ; Lee, Youngjo ; Yu, Kyung-Sang ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 289~294
DOI : 10.5351/KJAS.2015.28.2.289
Linear mixed models are commonly used in the clinical pharmaceutical studies to analyze repeated measures such as the crossover study data of bioequivalence studies. In these models, random effects describe the correlation between repeated outcomes and variance-covariance matrix explain within-subject variabilities. Bioequivalence analysis verifies whether a 90% confidence interval for geometric mean ratio of Cmax and AUC between reference drug and test drug is included in the bioequivalence margin [0.8, 1.25] performed using linear mixed models with period, sequence and treatment effects as fixed and sequence nested subject effects as random. A Levofloxacin study is referred to for an example of real data analysis.
Linear Mixed Models in Genetic Epidemiological Studies and Applications
Lim, Jeongmin ; Won, Sungho ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 295~308
DOI : 10.5351/KJAS.2015.28.2.295
We have experienced a substantial improvement in and cost-drop for genotyping that enables genetic epidemiological studies with large-scale genetic data. Genome-wide association studies have identified more than ten thousand causal variants. Many statistical methods based on linear mixed models have been developed for various goals such as estimating heritability and identifying disease susceptibility locus. Empirical results also repeatedly stress the importance of linear mixed models. Therefore, we review the statistical methods related with to linear mixed models and illustrate the meaning of their estimates.
Survival Analysis using SRC-Stat Statistical Package
Ha, Il Do ; Noh, Maengseok ; Lee, Youngjo ; Lim, Johan ; Lee, Jaeyong ; Oh, Heeseok ; Shin, Dongwan ; Lee, Sanggoo ; Seo, Jinuk ; Park, Yonhtae ; Cho, Sungzoon ; Park, Jonghun ; Kim, Youkyung ; You, Kyungsang ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 309~324
DOI : 10.5351/KJAS.2015.28.2.309
In this paper we introduce how to analyze survival data via a SRC-Stat statistical package. This provides classical survival analysis (e.g. Cox's proportional hazards models for univariate survival data) as well as advanced survival analysis such as shared and nested frailty models for multivariate survival data. We illustrate the use of our package with practical data sets.
Bayesian Analysis and Mapping of Elderly Korean Suicide Rates
Lee, Jayoun ; Kim, Dal Ho ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 325~334
DOI : 10.5351/KJAS.2015.28.2.325
Elderly suicide rates tend to be high in Korea. Suicide by the elderly is no longer a personal problem; consequently, further research on risk and regional factors is necessary. Disease mapping in epidemiology estimates spatial patterns for disease risk over a geographical region. In this study, we use a simultaneous conditional autoregressive model for spatial correlations between neighboring areas to estimate standard mortality ratios and mapping. The method is illustrated with cause of death data from 2006 and 2010 to analyze regional patterns of elderly suicide in Korea. By considering spatial correlations, the Bayesian spatial models, mean educational attainment and percentage of the elderly who live alone was the significant regional characteristic for elderly suicide. Gibbs sampling and grid method are used for computation.
The Use of Joint Hierarchical Generalized Linear Models: Application to Multivariate Longitudinal Data
Lee, Donghwan ; Yoo, Jae Keun ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 335~342
DOI : 10.5351/KJAS.2015.28.2.335
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.
SRC-Stat Package for Fitting Double Hierarchical Generalized Linear Models
Noh, Maengseok ; Ha, Il Do ; Lee, Youngjo ; Lim, Johan ; Lee, Jaeyong ; Oh, Heeseok ; Shin, Dongwan ; Lee, Sanggoo ; Seo, Jinuk ; Park, Yonhtae ; Cho, Sungzoon ; Park, Jonghun ; Kim, Youkyung ; You, Kyungsang ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 343~351
DOI : 10.5351/KJAS.2015.28.2.343
We introduce how to fit random effects models via a SRC-Stat statistical package. This package has been developed to fit double hierarchical generalized linear models where mean and dispersion parameters for the variance of random effects and residual variance (overdispersion) can be modeled as random-effect models. The estimates of fixed effects, random effects and variances are calculated by a hierarchical likelihood method. We illustrate the use of our package with practical data-sets.
Review of Spatial Linear Mixed Models for Non-Gaussian Outcomes
Park, Jincheol ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 353~360
DOI : 10.5351/KJAS.2015.28.2.353
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.
Analysis of Field Test Data using Robust Linear Mixed-Effects Model
Hong, Eun Hee ; Lee, Youngjo ; Ok, You Jin ; Na, Myung Hwan ; Noh, Maengseok ; Ha, Il Do ;
Korean Journal of Applied Statistics, volume 28, issue 2, 2015, Pages 361~369
DOI : 10.5351/KJAS.2015.28.2.361
A general linear mixed-effects model is often used to analyze repeated measurement experiment data of a continuous response variable. However, a general linear mixed-effects model can give improper analysis results when simultaneously detecting heteroscedasticity and the non-normality of population distribution. To achieve a more robust estimation, we used a heavy-tailed linear mixed-effects model for a more exact and reliable analysis conclusion than a general linear mixed-effects model. We also provide reliability analysis results for further research.