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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
How to Improve Classical Estimators via Linear Bayes Method?
Wang, Lichun ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 531~542
DOI : 10.5351/CSAM.2015.22.6.531
In this survey, we use the normal linear model to demonstrate the use of the linear Bayes method. The superiorities of linear Bayes estimator (LBE) over the classical UMVUE and MLE are established in terms of the mean squared error matrix (MSEM) criterion. Compared with the usual Bayes estimator (obtained by the MCMC method) the proposed LBE is simple and easy to use with numerical results presented to illustrate its performance. We also examine the applications of linear Bayes method to some other distributions including two-parameter exponential family, uniform distribution and inverse Gaussian distribution, and finally make some remarks.
Robustness, Data Analysis, and Statistical Modeling: The First 50 Years and Beyond
Barrios, Erniel B. ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 543~556
DOI : 10.5351/CSAM.2015.22.6.543
We present a survey of contributions that defined the nature and extent of robust statistics for the last 50 years. From the pioneering work of Tukey, Huber, and Hampel that focused on robust location parameter estimation, we presented various generalizations of these estimation procedures that cover a wide variety of models and data analysis methods. Among these extensions, we present linear models, clustered and dependent observations, times series data, binary and discrete data, models for spatial data, nonparametric methods, and forward search methods for outliers. We also present the current interest in robust statistics and conclude with suggestions on the possible future direction of this area for statistical science.
Gibbs Sampling for Double Seasonal Autoregressive Models
Amin, Ayman A. ; Ismail, Mohamed A. ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 557~573
DOI : 10.5351/CSAM.2015.22.6.557
In this paper we develop a Bayesian inference for a multiplicative double seasonal autoregressive (DSAR) model by implementing a fast, easy and accurate Gibbs sampling algorithm. We apply the Gibbs sampling to approximate empirically the marginal posterior distributions after showing that the conditional posterior distribution of the model parameters and the variance are multivariate normal and inverse gamma, respectively. The proposed Bayesian methodology is illustrated using simulated examples and real-world time series data.
Multivariate Procedure for Variable Selection and Classification of High Dimensional Heterogeneous Data
Mehmood, Tahir ; Rasheed, Zahid ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 575~587
DOI : 10.5351/CSAM.2015.22.6.575
The development in data collection techniques results in high dimensional data sets, where discrimination is an important and commonly encountered problem that are crucial to resolve when high dimensional data is heterogeneous (non-common variance covariance structure for classes). An example of this is to classify microbial habitat preferences based on codon/bi-codon usage. Habitat preference is important to study for evolutionary genetic relationships and may help industry produce specific enzymes. Most classification procedures assume homogeneity (common variance covariance structure for all classes), which is not guaranteed in most high dimensional data sets. We have introduced regularized elimination in partial least square coupled with QDA (rePLS-QDA) for the parsimonious variable selection and classification of high dimensional heterogeneous data sets based on recently introduced regularized elimination for variable selection in partial least square (rePLS) and heterogeneous classification procedure quadratic discriminant analysis (QDA). A comparison of proposed and existing methods is conducted over the simulated data set; in addition, the proposed procedure is implemented to classify microbial habitat preferences by their codon/bi-codon usage. Five bacterial habitats (Aquatic, Host Associated, Multiple, Specialized and Terrestrial) are modeled. The classification accuracy of each habitat is satisfactory and ranges from 89.1% to 100% on test data. Interesting codon/bi-codons usage, their mutual interactions influential for respective habitat preference are identified. The proposed method also produced results that concurred with known biological characteristics that will help researchers better understand divergence of species.
Bayesian Pattern Mixture Model for Longitudinal Binary Data with Nonignorable Missingness
Kyoung, Yujung ; Lee, Keunbaik ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 589~598
DOI : 10.5351/CSAM.2015.22.6.589
In longitudinal studies missing data are common and require a complicated analysis. There are two popular modeling frameworks, pattern mixture model (PMM) and selection models (SM) to analyze the missing data. We focus on the PMM and we also propose Bayesian pattern mixture models using generalized linear mixed models (GLMMs) for longitudinal binary data. Sensitivity analysis is used under the missing not at random assumption.
Estimation of Conditional Kendall's Tau for Bivariate Interval Censored Data
Kim, Yang-Jin ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 599~604
DOI : 10.5351/CSAM.2015.22.6.599
Kendall's tau statistic has been applied to test an association of bivariate random variables. However, incomplete bivariate data with a truncation and a censoring results in incomparable or unorderable pairs. With such a partial information, Tsai (1990) suggested a conditional tau statistic and a test procedure for a quasi independence that was extended to more diverse cases such as double truncation and a semi-competing risk data. In this paper, we also employed a conditional tau statistic to estimate an association of bivariate interval censored data. The suggested method shows a better result in simulation studies than Betensky and Finkelstein's multiple imputation method except a case in cases with strong associations. The association of incubation time and infection time from an AIDS cohort study is estimated as a real data example.
Statistical Assessment of Biosimilarity based on the Relative Distance between Follow-on Biologics in the (k + 1)-Arm Parallel Design
Kang, Seung-Ho ; Shin, Wooyoung ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 605~613
DOI : 10.5351/CSAM.2015.22.6.605
A three-arm parallel design has been proposed to assess the biosimilarity between a biological product and a reference product using relative distance (Kang and Chow, 2013). The three-arm parallel design consists of two arms for the reference product and one arm for the biosimilar product. This paper extended the three-arm parallel design to a (k + 1)-arm parallel design composed of k (
) arms for the reference product and one arm for the biosimilar product. A new relative distance was defined based on Euclidean distance; consequently, a corresponding test procedure was developed based on asymptotic distribution. Type I error rates and powers were investigated both theoretically and empirically.
Estimation of Seasonal Cointegration under Conditional Heteroskedasticity
Seong, Byeongchan ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 615~624
DOI : 10.5351/CSAM.2015.22.6.615
We consider the estimation of seasonal cointegration in the presence of conditional heteroskedasticity (CH) using a feasible generalized least squares method. We capture cointegrating relationships and time-varying volatility for long-run and short-run dynamics in the same model. This procedure can be easily implemented using common methods such as ordinary least squares and generalized least squares. The maximum likelihood (ML) estimation method is computationally difficult and may not be feasible for larger models. The simulation results indicate that the proposed method is superior to the ML method when CH exists. In order to illustrate the proposed method, an empirical example is presented to model a seasonally cointegrated times series under CH.
Dirichlet Process Mixtures of Linear Mixed Regressions
Kyung, Minjung ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 625~637
DOI : 10.5351/CSAM.2015.22.6.625
We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.
Simultaneous Tests with Combining Functions under Normality
Park, Hyo-Il ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 639~646
DOI : 10.5351/CSAM.2015.22.6.639
We propose simultaneous tests for mean and variance under the normality assumption. After formulating the null hypothesis and its alternative, we construct test statistics based on the individual p-values for the partial tests with combining functions and derive the null distributions for the combining functions. We then illustrate our procedure with industrial data and compare the efficiency among the combining functions with individual partial ones by obtaining empirical powers through a simulation study. A discussion then follows on the intersection-union test with a combining function and simultaneous confidence region as a simultaneous inference; in addition, we discuss weighted functions and applications to the statistical quality control. Finally we comment on nonparametric simultaneous tests.
A Note on the Dependence Conditions for Stationary Normal Sequences
Choi, Hyemi ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 647~653
DOI : 10.5351/CSAM.2015.22.6.647
Extreme value theory concerns the distributional properties of the maximum of a random sample; subsequently, it has been significantly extended to stationary random sequences satisfying weak dependence restrictions. We focus on distributional mixing condition
and the Berman condition based on covariance among weak dependence restrictions. The former is assumed for general stationary sequences and the latter for stationary normal processes; however, both imply the same distributional limit of the maximum of the normal process. In this paper
condition is shown weaker than Berman's covariance condition. Examples are given where the Berman condition is satisfied but the distributional mixing is not.
Nonresponse Adjusted Raking Ratio Estimation
Park, Mingue ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 655~664
DOI : 10.5351/CSAM.2015.22.6.655
A nonresponse adjusted raking ratio estimator that consists of weighting adjustment using estimated response probability and raking procedure is often used to reduce the nonresponse bias and keep the calibration property of the estimator. We investigated asymptotic properties of nonresponse adjusted raking ratio estimator and proposed a variance estimator. A simulation study is used to examine the performance of suggested estimators.
ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients
Kim, Seongho ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 665~674
DOI : 10.5351/CSAM.2015.22.6.665
Lack of a general matrix formula hampers implementation of the semi-partial correlation, also known as part correlation, to the higher-order coefficient. This is because the higher-order semi-partial correlation calculation using a recursive formula requires an enormous number of recursive calculations to obtain the correlation coefficients. To resolve this difficulty, we derive a general matrix formula of the semi-partial correlation for fast computation. The semi-partial correlations are then implemented on an R package ppcor along with the partial correlation. Owing to the general matrix formulas, users can readily calculate the coefficients of both partial and semi-partial correlations without computational burden. The package ppcor further provides users with the level of the statistical significance with its test statistic.
A Study on the Comparison of Electricity Forecasting Models: Korea and China
Zheng, Xueyan ; Kim, Sahm ;
Communications for Statistical Applications and Methods, volume 22, issue 6, 2015, Pages 675~683
DOI : 10.5351/CSAM.2015.22.6.675
In the 21st century, we now face the serious problems of the enormous consumption of the energy resources. Depending on the power consumption increases, both China and South Korea face a reduction in available resources. This paper considers the regression models and time-series models to compare the performance of the forecasting accuracy based on Mean Absolute Percentage Error (MAPE) in order to forecast the electricity demand accurately on the short-term period (68 months) data in Northeast China and find the relationship with Korea. Among the models the support vector regression (SVR) model shows superior performance than time-series models for the short-term period data and the time-series models show similar results with the SVR model when we use long-term period data.