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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Communications for Statistical Applications and Methods
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Journal DOI :
The Korean Statistical Society
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Volume & Issues
Volume 20, Issue 6 - Nov 2013
Volume 20, Issue 5 - Sep 2013
Volume 20, Issue 4 - Jul 2013
Volume 20, Issue 3 - May 2013
Volume 20, Issue 2 - Mar 2013
Volume 20, Issue 1 - Jan 2013
Selecting the target year
ROC Curve for Multivariate Random Variables
Hong, Chong Sun ;
Communications for Statistical Applications and Methods, volume 20, issue 3, 2013, Pages 169~174
DOI : 10.5351/CSAM.2013.20.3.169
The ROC curve is drawn with two conditional cumulative distribution functions (or survival functions) of the univariate random variable. In this work, we consider joint cumulative distribution functions of k random variables, and suggest a ROC curve for multivariate random variables. With regard to the values on the line, which passes through two mean vectors of dichotomous states, a joint cumulative distribution function can be regarded as a function of the univariate variable. After this function is modified to satisfy the properties of the cumulative distribution function, a ROC curve might be derived; moreover, some illustrative examples are demonstrated.
Arrow Diagrams for Kernel Principal Component Analysis
Huh, Myung-Hoe ;
Communications for Statistical Applications and Methods, volume 20, issue 3, 2013, Pages 175~184
DOI : 10.5351/CSAM.2013.20.3.175
Kernel principal component analysis(PCA) maps observations in nonlinear feature space to a reduced dimensional plane of principal components. We do not need to specify the feature space explicitly because the procedure uses the kernel trick. In this paper, we propose a graphical scheme to represent variables in the kernel principal component analysis. In addition, we propose an index for individual variables to measure the importance in the principal component plane.
On Asymptotic Properties of a Maximum Likelihood Estimator of Stochastically Ordered Distribution Function
Oh, Myongsik ;
Communications for Statistical Applications and Methods, volume 20, issue 3, 2013, Pages 185~191
DOI : 10.5351/CSAM.2013.20.3.185
Kiefer (1961) studied asymptotic behavior of empirical distribution using the law of the iterated logarithm. Robertson and Wright (1974a) discussed whether this type of result would hold for a maximum likelihood estimator of a stochastically ordered distribution function; however, we show that this cannot be achieved. We provide only a partial answer to this problem. The result is applicable to both estimation and testing problems under the restriction of stochastic ordering.
Almost Sure Central Limit Theorems for Stationary Bootstrap Mean
Hwang, Eunju ; Shin, Dong Wan ;
Communications for Statistical Applications and Methods, volume 20, issue 3, 2013, Pages 193~197
DOI : 10.5351/CSAM.2013.20.3.193
Almost sure central limit theorems are established for a stationary bootstrap sample mean of strong mixing processes. Both weak and strong consistencies are obtained.
Size Refinement of Empirical Likelihood Tests in Time Series Models using Sieve Bootstraps
Lee, Jin ;
Communications for Statistical Applications and Methods, volume 20, issue 3, 2013, Pages 199~205
DOI : 10.5351/CSAM.2013.20.3.199
We employ sieve bootstraps for empirical likelihood tests in time series models because their null distributions are often vulnerable to the presence of serial dependence. We found a significant size refinement of the bootstrapped versions of a Lagrangian Multiplier type test statistic regardless of the bandwidth choice required by long-run variance estimations.
An Empirical Study on Explosive Volatility Test with Possibly Nonstationary GARCH(1, 1) Models
Lee, Sangyeol ; Noh, Jungsik ;
Communications for Statistical Applications and Methods, volume 20, issue 3, 2013, Pages 207~215
DOI : 10.5351/CSAM.2013.20.3.207
In this paper, we implement an empirical study to test whether the time series of daily returns in stock and Won/USD exchange markets is strictly stationary or explosive. The results indicate that only a few series show nonstationary volatility when dramatic events erupted; in addition, this nonstationary behavior occurs more often in the Won/USD exchange market than in the stock market.
Influence Analysis of the Common Mean Problem
Kim, Myung Geun ;
Communications for Statistical Applications and Methods, volume 20, issue 3, 2013, Pages 217~223
DOI : 10.5351/CSAM.2013.20.3.217
Two influence diagnostic methods for the common mean model are proposed. First, an investigation of the influence of observations according to minor perturbations of the common mean model is made by adapting the local influence method which is based on the likelihood displacement. It is well known that the maximum likelihood estimates are in general sensitive to influential observations. Case-deletions can be a candidate for detecting influential observations. However, the maximum likelihood estimators are iteratively computed and therefore case-deletions involve an enormous amount of computations. An approximation by Newton's method to the maximum likelihood estimator obtained after a single observation was deleted can reduce much of computational burden, which will be treated in this work. A numerical example is given for illustration and it shows that the proposed diagnostic methods can be useful tools.
A Note on Linear SVM in Gaussian Classes
Jeon, Yongho ;
Communications for Statistical Applications and Methods, volume 20, issue 3, 2013, Pages 225~233
DOI : 10.5351/CSAM.2013.20.3.225
The linear support vector machine(SVM) is motivated by the maximal margin separating hyperplane and is a popular tool for binary classification tasks. Many studies exist on the consistency properties of SVM; however, it is unknown whether the linear SVM is consistent for estimating the optimal classification boundary even in the simple case of two Gaussian classes with a common covariance, where the optimal classification boundary is linear. In this paper we show that the linear SVM can be inconsistent in the univariate Gaussian classification problem with a common variance, even when the best tuning parameter is used.
Bayesian Modeling of Random Effects Covariance Matrix for Generalized Linear Mixed Models
Lee, Keunbaik ;
Communications for Statistical Applications and Methods, volume 20, issue 3, 2013, Pages 235~240
DOI : 10.5351/CSAM.2013.20.3.235
Generalized linear mixed models(GLMMs) are frequently used for the analysis of longitudinal categorical data when the subject-specific effects is of interest. In GLMMs, the structure of the random effects covariance matrix is important for the estimation of fixed effects and to explain subject and time variations. The estimation of the matrix is not simple because of the high dimension and the positive definiteness; subsequently, we practically use the simple structure of the covariance matrix such as AR(1). However, this strong assumption can result in biased estimates of the fixed effects. In this paper, we introduce Bayesian modeling approaches for the random effects covariance matrix using a modified Cholesky decomposition. The modified Cholesky decomposition approach has been used to explain a heterogenous random effects covariance matrix and the subsequent estimated covariance matrix will be positive definite. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using these methods.