Go to the main menu
Skip to content
Go to bottom
REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
Korean Journal of Applied Statistics
Journal Basic Information
Journal DOI :
The Korean Statistical Society
Editor in Chief :
Volume & Issues
Volume 27, Issue 7 - Dec 2014
Volume 27, Issue 6 - Dec 2014
Volume 27, Issue 5 - Oct 2014
Volume 27, Issue 4 - Aug 2014
Volume 27, Issue 3 - Jun 2014
Volume 27, Issue 2 - Apr 2014
Volume 27, Issue 1 - Feb 2014
Selecting the target year
History and Future of Bayesian Statistics
Lee, Jaeyong ; Lee, Kyoungjae ; Leea, Youngseon ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 855~863
DOI : 10.5351/KJAS.2014.27.6.855
The recent computational revolution of Bayesian statistics has expanded use of the Bayesian statistics significantly; however, Bayesian statistics face a new set of challenges in the era of information technology. We survey the history of Bayesian statistics briefly and its expansion in the modern times. We then take a prospective future view of statistics and list challenges that the statistics community faces.
Discussion of: "History and Future of Bayesian Statistics"
Jang, Woncheol ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 865~866
DOI : 10.5351/KJAS.2015.27.6.865
Nonparametric Bayesian Statistical Models in Biomedical Research
Noh, Heesang ; Park, Jinsu ; Sim, Gyuseok ; Yu, Jae-Eun ; Chung, Yeonseung ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 867~889
DOI : 10.5351/KJAS.2014.27.6.867
Nonparametric Bayesian (np Bayes) statistical models are popularly used in a variety of research areas because of their flexibility and computational convenience. This paper reviews the np Bayes models focusing on biomedical research applications. We review key probability models for np Bayes inference while illustrating how each of the models is used to answer different types of research questions using biomedical examples. The examples are chosen to highlight the problems that are challenging for standard parametric inference but can be solved using nonparametric inference. We discuss np Bayes inference in four topics: (1) density estimation, (2) clustering, (3) random effects distribution, and (4) regression.
Beta Processes and Survival Analysis
Kim, Yongdai ; Chae, Minwoo ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 891~907
DOI : 10.5351/KJAS.2015.27.6.891
This article is concerned with one of the most important prior distributions for Bayesian analysis of survival and event history data, called Beta processes, proposed in Hjort (1990). We review the current state of the art of beta processes and their application to survival analysis. Relevant methodological and practical areas of research that we touch on relate to constructions, posterior distributions, large-sample properties, Bayesian computations, and mixtures of Beta processes.
Bayesian Inference with Inequality Constraints
Oh, Man-Suk ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 909~922
DOI : 10.5351/KJAS.2014.27.6.909
This paper reviews Bayesian inference with inequality constraints. It focuses on ⅰ) comparison of models with various inequality/equality constraints on parameters, ⅱ) multiple tests on equalities of parameters when parameters are under inequality constraints, ⅲ) multiple test on equalities of score parameters in models for contingency tables with ordinal categorical variables.
Hurdle Model for Longitudinal Zero-Inflated Count Data Analysis
Jin, Iktae ; Lee, Keunbaik ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 923~932
DOI : 10.5351/KJAS.2014.27.6.923
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.
Bayesian Spatiotemporal Modeling in Epidemiology: Hepatitis A Incidence Data in Korea
Choi, Jungsoon ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 933~945
DOI : 10.5351/KJAS.2014.27.6.933
Bayesian spatiotemporal analysis is of considerable interest to epidemiological applications because health data is collected over space-time with complicated dependency structures. A basic concept in spatiotemporal modeling is introduced in this paper to analyze space-time disease data. The paper reviews a range of Bayesian spatiotemporal models and analyzes Hepatitis A data in Korea.
A Bayesian Analysis of Return Level for Extreme Precipitation in Korea
Lee, Jeong Jin ; Kim, Nam Hee ; Kwon, Hye Ji ; Kim, Yongku ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 947~958
DOI : 10.5351/KJAS.2014.27.6.947
Understanding extreme precipitation events is very important for flood planning purposes. Especially, the r-year return level is a common measure of extreme events. In this paper, we present a spatial analysis of precipitation return level using hierarchical Bayesian modeling. For intensity, we model annual maximum daily precipitations and daily precipitation above a high threshold at 62 stations in Korea with generalized extreme value(GEV) and generalized Pareto distribution(GPD), respectively. The spatial dependence among return levels is incorporated to the model through a latent Gaussian process of the GEV and GPD model parameters. We apply the proposed model to precipitation data collected at 62 stations in Korea from 1973 to 2011.
Efficient Bayesian Inference on Asymmetric Jump-Diffusion Models
Park, Taeyoung ; Lee, Youngeun ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 959~973
DOI : 10.5351/KJAS.2014.27.6.959
Asset pricing models that account for asymmetric volatility in asset prices have been recently proposed. This article presents an efficient Bayesian method to analyze asset-pricing models. The method is developed by devising a partially collapsed Gibbs sampler that capitalizes on the functional incompatibility of conditional distributions without complicating the updates of model components. The proposed method is illustrated using simulated data and applied to daily S&P 500 data observed from September 1980 to August 2014.
Comparison of Laplace and Double Pareto Penalty: LASSO and Elastic Net
Kyung, Minjung ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 975~989
DOI : 10.5351/KJAS.2014.27.6.975
Lasso (Tibshirani, 1996) and Elastic Net (Zou and Hastie, 2005) have been widely used in various fields for simultaneous variable selection and coefficient estimation. Bayesian methods using a conditional Laplace and a double Pareto prior specification have been discussed in the form of hierarchical specification. Full conditional posterior distributions with each priors have been derived. We compare the performance of Bayesian lassos with Laplace prior and the performance with double Pareto prior using simulations. We also apply the proposed Bayesian hierarchical models to real data sets to predict the collapse of governments in Asia.
A Comparison of Bayesian and Maximum Likelihood Estimations in a SUR Tobit Regression Model
Lee, Seung-Chun ; Choi, Byongsu ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 991~1002
DOI : 10.5351/KJAS.2014.27.6.991
Both Bayesian and maximum likelihood methods are efficient for the estimation of regression coefficients of various Tobit regression models (see. e.g. Chib, 1992; Greene, 1990; Lee and Choi, 2013); however, some researchers recognized that the maximum likelihood method tends to underestimate the disturbance variance, which has implications for the estimation of marginal effects and the asymptotic standard error of estimates. The underestimation of the maximum likelihood estimate in a seemingly unrelated Tobit regression model is examined. A Bayesian method based on an objective noninformative prior is shown to provide proper estimates of the disturbance variance as well as other regression parameters
Analysis of Missing Data Using an Empirical Bayesian Method
Yoon, Yong Hwa ; Choi, Boseung ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 1003~1016
DOI : 10.5351/KJAS.2014.27.6.1003
Proper missing data imputation is an important procedure to obtain superior results for data analysis based on survey data. This paper deals with both a model based imputation method and model estimation method. We utilized a Bayesian method to solve a boundary solution problem in which we applied a maximum likelihood estimation method. We also deal with a missing mechanism model selection problem using forecasting results and a comparison between model accuracies. We utilized MWPE(modified within precinct error) (Bautista et al., 2007) to measure prediction correctness. We applied proposed ML and Bayesian methods to the Korean presidential election exit poll data of 2012. Based on the analysis, the results under the missing at random mechanism showed superior prediction results than under the missing not at random mechanism.
Bayes Risk Comparison for Non-Life Insurance Risk Estimation
Kim, Myung Joon ; Woo, Ho Young ; Kim, Yeong-Hwa ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 1017~1028
DOI : 10.5351/KJAS.2014.27.6.1017
Well-known Bayes and empirical Bayes estimators have a disadvantage in respecting to overshink the parameter estimator error; therefore, a constrained Bayes estimator is suggested by matching the first two moments. Also traditional loss function such as mean square error loss function only considers the precision of estimation and to consider both precision and goodness of fit, balanced loss function is suggested. With these reasons, constrained Bayes estimators under balanced loss function is recommended for non-life insurance pricing.; however, most studies focus on the performance of estimation since Bayes risk of newly suggested estimators such as constrained Bayes and constrained empirical Bayes estimators under specific loss function is difficult to derive. This study compares the Bayes risk of several Bayes estimators under two different loss functions for estimating the risk in the auto insurance business and indicates the effectiveness of the newly suggested Bayes estimators with regards to Bayes risk perspective through auto insurance real data analysis.
Understanding Bayesian Experimental Design with Its Applications
Lee, Gunhee ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 1029~1038
DOI : 10.5351/KJAS.2014.27.6.1029
Bayesian experimental design is a useful concept in applied statistics for the design of efficient experiments especially if prior knowledge in the experiment is available. However, a theoretical or numerical approach is not simple to implement. We review the concept of a Bayesian experiment approach for linear and nonlinear statistical models. We investigate relationships between prior knowledge and optimal design to identify Bayesian experimental design process characteristics. A balanced design is important if we do not have prior knowledge; however, prior knowledge is important in design and expert opinions should reflect an efficient analysis. Care should be taken if we set a small sample size with a vague improper prior since both Bayesian design and non-Bayesian design provide incorrect solutions.
Bayesian Inference for Autoregressive Models with Skewed Exponential Power Errors
Ryu, Hyunnam ; Kim, Dal Ho ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 1039~1047
DOI : 10.5351/KJAS.2014.27.6.1039
An autoregressive model with normal errors is a natural model that attempts to fit time series data. More flexible models that include normal distribution as a special case are necessary because they can cover normality to non-normality models. The skewed exponential power distribution is a possible candidate for autoregressive models errors that may have tails lighter(platykurtic) or heavier(leptokurtic) than normal and skewness; in addition, the use of skewed exponential power distribution can reduce the influence of outliers and consequently increases the robustness of the analysis. We use SIR algorithm and grid method for an efficient Bayesian estimation.
A Comparison Study of Bayesian Methods for a Threshold Autoregressive Model with Regime-Switching
Roh, Taeyoung ; Jo, Seongil ; Lee, Ryounghwa ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 1049~1068
DOI : 10.5351/KJAS.2014.27.6.1049
Autoregressive models are used to analyze an univariate time series data; however, these methods can be inappropriate when a structural break appears in a time series since they assume that a trend is consistent. Threshold autoregressive models (popular regime-switching models) have been proposed to address this problem. Recently, the models have been extended to two regime-switching models with delay parameter. We discuss two regime-switching threshold autoregressive models from a Bayesian point of view. For a Bayesian analysis, we consider a parametric threshold autoregressive model and a nonparametric threshold autoregressive model using Dirichlet process prior. The posterior distributions are derived and the posterior inferences is performed via Markov chain Monte Carlo method and based on two Bayesian threshold autoregressive models. We present a simulation study to compare the performance of the models. We also apply models to gross domestic product data of U.S.A and South Korea.
A Bayesian Prediction of the Generalized Pareto Model
Huh, Pan ; Sohn, Joong Kweon ;
Korean Journal of Applied Statistics, volume 27, issue 6, 2014, Pages 1069~1076
DOI : 10.5351/KJAS.2014.27.6.1069
Rainfall weather patterns have changed due to global warming and sudden heavy rainfalls have become more frequent. Economic loss due to heavy rainfall has increased. We study the generalized Pareto distribution for modelling rainfall in Seoul based on data from 1973 to 2008. We use several priors including Jeffrey's noninformative prior and Gibbs sampling method to derive Bayesian posterior predictive distributions. The probability of heavy rainfall has increased over the last ten years based on estimated posterior predictive distribution.