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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 22, Issue 6 - Nov 2015
Volume 22, Issue 5 - Sep 2015
Volume 22, Issue 4 - Jul 2015
Volume 22, Issue 3 - May 2015
Volume 22, Issue 2 - Mar 2015
Volume 22, Issue 1 - Jan 2015
Selecting the target year
A Comparison Study of the Test for Right Censored and Grouped Data
Park, Hyo-Il ;
Communications for Statistical Applications and Methods, volume 22, issue 4, 2015, Pages 313~320
DOI : 10.5351/CSAM.2015.22.4.313
In this research, we compare the efficiency of two test procedures proposed by Prentice and Gloeckler (1978) and Park and Hong (2009) for grouped data with possible right censored observations. Both test statistics were derived using the likelihood ratio principle, but under different semi-parametric models. We review the two statistics with asymptotic normality and consider obtaining empirical powers through a simulation study. The simulation study considers two types of models the location translation model and the scale model. We discuss some interesting features related to the grouped data and obtain null distribution functions with a re-sampling method. Finally we indicate topics for future research.
Geodesic Clustering for Covariance Matrices
Lee, Haesung ; Ahn, Hyun-Jung ; Kim, Kwang-Rae ; Kim, Peter T. ; Koo, Ja-Yong ;
Communications for Statistical Applications and Methods, volume 22, issue 4, 2015, Pages 321~331
DOI : 10.5351/CSAM.2015.22.4.321
The K-means clustering algorithm is a popular and widely used method for clustering. For covariance matrices, we consider a geodesic clustering algorithm based on the K-means clustering framework in consideration of symmetric positive definite matrices as a Riemannian (non-Euclidean) manifold. This paper considers a geodesic clustering algorithm for data consisting of symmetric positive definite (SPD) matrices, utilizing the Riemannian geometric structure for SPD matrices and the idea of a K-means clustering algorithm. A K-means clustering algorithm is divided into two main steps for which we need a dissimilarity measure between two matrix data points and a way of computing centroids for observations in clusters. In order to use the Riemannian structure, we adopt the geodesic distance and the intrinsic mean for symmetric positive definite matrices. We demonstrate our proposed method through simulations as well as application to real financial data.
On the Exponentiated Generalized Modified Weibull Distribution
Aryal, Gokarna ; Elbatal, Ibrahim ;
Communications for Statistical Applications and Methods, volume 22, issue 4, 2015, Pages 333~348
DOI : 10.5351/CSAM.2015.22.4.333
In this paper, we study a generalization of the modified Weibull distribution. The generalization follows the recent work of Cordeiro et al. (2013) and is based on a class of exponentiated generalized distributions that can be interpreted as a double construction of Lehmann. We introduce a class of exponentiated generalized modified Weibull (EGMW) distribution and provide a list of some well-known distributions embedded within the proposed distribution. We derive some mathematical properties of this class that include ordinary moments, generating function and order statistics. We propose a maximum likelihood method to estimate model parameters and provide simulation results to assess the model performance. Real data is used to illustrate the usefulness of the proposed distribution for modeling reliability data.
Bayesian Curve-Fitting in Semiparametric Small Area Models with Measurement Errors
Hwang, Jinseub ; Kim, Dal Ho ;
Communications for Statistical Applications and Methods, volume 22, issue 4, 2015, Pages 349~359
DOI : 10.5351/CSAM.2015.22.4.349
We study a semiparametric Bayesian approach to small area estimation under a nested error linear regression model with area level covariate subject to measurement error. Consideration is given to radial basis functions for the regression spline and knots on a grid of equally spaced sample quantiles of covariate with measurement errors in the nested error linear regression model setup. We conduct a hierarchical Bayesian structural measurement error model for small areas and prove the propriety of the joint posterior based on a given hierarchical Bayesian framework since some priors are defined non-informative improper priors that uses Markov Chain Monte Carlo methods to fit it. Our methodology is illustrated using numerical examples to compare possible models based on model adequacy criteria; in addition, analysis is conducted based on real data.
Tests Based on Skewness and Kurtosis for Multivariate Normality
Kim, Namhyun ;
Communications for Statistical Applications and Methods, volume 22, issue 4, 2015, Pages 361~375
DOI : 10.5351/CSAM.2015.22.4.361
A measure of skewness and kurtosis is proposed to test multivariate normality. It is based on an empirical standardization using the scaled residuals of the observations. First, we consider the statistics that take the skewness or the kurtosis for each coordinate of the scaled residuals. The null distributions of the statistics converge very slowly to the asymptotic distributions; therefore, we apply a transformation of the skewness or the kurtosis to univariate normality for each coordinate. Size and power are investigated through simulation; consequently, the null distributions of the statistics from the transformed ones are quite well approximated to asymptotic distributions. A simulation study also shows that the combined statistics of skewness and kurtosis have moderate sensitivity of all alternatives under study, and they might be candidates for an omnibus test.
Test Statistics for Volume under the ROC Surface and Hypervolume under the ROC Manifold
Hong, Chong Sun ; Cho, Min Ho ;
Communications for Statistical Applications and Methods, volume 22, issue 4, 2015, Pages 377~387
DOI : 10.5351/CSAM.2015.22.4.377
The area under the ROC curve can be represented by both Mann-Whitney and Wilcoxon rank sum statistics. Consider an ROC surface and manifold equal to three dimensions or more. This paper finds that the volume under the ROC surface (VUS) and the hypervolume under the ROC manifold (HUM) could be derived as functions of both conditional Mann-Whitney statistics and conditional Wilcoxon rank sum statistics. The nullhypothesis equal to three distribution functions or more are identical can be tested using VUS and HUM statistics based on the asymptotic large sample theory of Wilcoxon rank sum statistics. Illustrative examples with three and four random samples show that two approaches give the same VUS and
. The equivalence of several distribution functions is also tested with VUS and
in terms of conditional Wilcoxon rank sum statistics.
Sample Size Calculations for the Development of Biosimilar Products Based on Binary Endpoints
Kang, Seung-Ho ; Jung, Ji-Yong ; Baik, Seon-Hye ;
Communications for Statistical Applications and Methods, volume 22, issue 4, 2015, Pages 389~399
DOI : 10.5351/CSAM.2015.22.4.389
It is important not to overcalculate sample sizes for clinical trials due to economic, ethical, and scientific reasons. Kang and Kim (2014) investigated the accuracy of a well-known sample size calculation formula based on the approximate power for continuous endpoints in equivalence trials, which has been widely used for Development of Biosimilar Products. They concluded that this formula is overly conservative and that sample size should be calculated based on an exact power. This paper extends these results to binary endpoints for three popular metrics: the risk difference, the log of the relative risk, and the log of the odds ratio. We conclude that the sample size formulae based on the approximate power for binary endpoints in equivalence trials are overly conservative. In many cases, sample sizes to achieve 80% power based on approximate powers have 90% exact power. We propose that sample size should be computed numerically based on the exact power.