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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 21, Issue 6 - Nov 2014
Volume 21, Issue 5 - Sep 2014
Volume 21, Issue 4 - Jul 2014
Volume 21, Issue 3 - May 2014
Volume 21, Issue 2 - Mar 2014
Volume 21, Issue 1 - Jan 2014
Selecting the target year
Estimation of Median in the Presence of Three Known Quartiles of an Auxiliary Variable
Singh, Housila P. ; Shanmugam, Ramalingam ; Singh, Sarjinder ; Kim, Jong-Min ;
Communications for Statistical Applications and Methods, volume 21, issue 5, 2014, Pages 363~386
DOI : 10.5351/CSAM.2014.21.5.363
This paper has improved several ratio type estimators of the population median including their generalization in the presence of three known quartiles of an auxiliary variable. The properties of the improved estimators are discussed and applied. Both the empirical and simulation studies confirm that our new estimators perform efficiently.
Analysis of Recurrent Gap Time Data with a Binary Time-Varying Covariate
Kim, Yang-Jin ;
Communications for Statistical Applications and Methods, volume 21, issue 5, 2014, Pages 387~393
DOI : 10.5351/CSAM.2014.21.5.387
Recurrent gap times are analyzed with diverse methods under several assumptions such as a marginal model or a frailty model. Several resampling techniques have been recently suggested to estimate the covariate effect; however, these approaches can be applied with a time-fixed covariate. According to simulation results, these methods result in biased estimates for a time-varying covariate which is often observed in a longitudinal study. In this paper, we extend a resampling method by incorporating new weights and sampling scheme. Simulation studies are performed to compare the suggested method with previous resampling methods. The proposed method is applied to estimate the effect of an educational program on traffic conviction data where a program participation occurs in the middle of the study.
Dual Generalized Maximum Entropy Estimation for Panel Data Regression Models
Lee, Jaejun ; Cheon, Sooyoung ;
Communications for Statistical Applications and Methods, volume 21, issue 5, 2014, Pages 395~409
DOI : 10.5351/CSAM.2014.21.5.395
Data limited, partial, or incomplete are known as an ill-posed problem. If the data with ill-posed problems are analyzed by traditional statistical methods, the results obviously are not reliable and lead to erroneous interpretations. To overcome these problems, we propose a dual generalized maximum entropy (dual GME) estimator for panel data regression models based on an unconstrained dual Lagrange multiplier method. Monte Carlo simulations for panel data regression models with exogeneity, endogeneity, or/and collinearity show that the dual GME estimator outperforms several other estimators such as using least squares and instruments even in small samples. We believe that our dual GME procedure developed for the panel data regression framework will be useful to analyze ill-posed and endogenous data sets.
Double-Bagging Ensemble Using WAVE
Kim, Ahhyoun ; Kim, Minji ; Kim, Hyunjoong ;
Communications for Statistical Applications and Methods, volume 21, issue 5, 2014, Pages 411~422
DOI : 10.5351/CSAM.2014.21.5.411
A classification ensemble method aggregates different classifiers obtained from training data to classify new data points. Voting algorithms are typical tools to summarize the outputs of each classifier in an ensemble. WAVE, proposed by Kim et al. (2011), is a new weight-adjusted voting algorithm for ensembles of classifiers with an optimal weight vector. In this study, when constructing an ensemble, we applied the WAVE algorithm on the double-bagging method (Hothorn and Lausen, 2003) to observe if any significant improvement can be achieved on performance. The results showed that double-bagging using WAVE algorithm performs better than other ensemble methods that employ plurality voting. In addition, double-bagging with WAVE algorithm is comparable with the random forest ensemble method when the ensemble size is large.
Cumulative Sums of Residuals in GLMM and Its Implementation
Choi, DoYeon ; Jeong, KwangMo ;
Communications for Statistical Applications and Methods, volume 21, issue 5, 2014, Pages 423~433
DOI : 10.5351/CSAM.2014.21.5.423
Test statistics using cumulative sums of residuals have been widely used in various regression models including generalized linear models(GLM). Recently, Pan and Lin (2005) extended this testing procedure to the generalized linear mixed models(GLMM) having random effects, in which we encounter difficulties in computing the marginal likelihood that is expressed as an integral of random effects distribution. The Gaussian quadrature algorithm is commonly used to approximate the marginal likelihood. Many commercial statistical packages provide an option to apply this type of goodness-of-fit test in GLMs but available programs are very rare for GLMMs. We suggest a computational algorithm to implement the testing procedure in GLMMs by a freely accessible R package, and also illustrate through practical examples.
The Bandwidth from the Density Power Divergence
Pak, Ro Jin ;
Communications for Statistical Applications and Methods, volume 21, issue 5, 2014, Pages 435~444
DOI : 10.5351/CSAM.2014.21.5.435
The most widely used optimal bandwidth is known to minimize the mean integrated squared error(MISE) of a kernel density estimator from a true density. In this article proposes, we propose a bandwidth which asymptotically minimizes the mean integrated density power divergence(MIDPD) between a true density and a corresponding kernel density estimator. An approximated form of the mean integrated density power divergence is derived and a bandwidth is obtained as a product of minimization based on the approximated form. The resulting bandwidth resembles the optimal bandwidth by Parzen (1962), but it reflects the nature of a model density more than the existing optimal bandwidths. We have one more choice of an optimal bandwidth with a firm theoretical background; in addition, an empirical study we show that the bandwidth from the mean integrated density power divergence can produce a density estimator fitting a sample better than the bandwidth from the mean integrated squared error.
Dependence Structure of Korean Financial Markets Using Copula-GARCH Model
Kim, Woohwan ;
Communications for Statistical Applications and Methods, volume 21, issue 5, 2014, Pages 445~459
DOI : 10.5351/CSAM.2014.21.5.445
This paper investigates the dependence structure of Korean financial markets (stock, foreign exchange (FX) rates and bond) using copula-GARCH and dynamic conditional correlation (DCC) models. We examine GJR-GARCH with skewed elliptical distributions and four copulas (Gaussian, Student's t, Clayton and Gumbel) to model dependence among returns, and then employ DCC model to describe system-wide correlation dynamics. We analyze the daily returns of KOSPI, FX (WON/USD) and KRX bond index (Gross Price Index) from
May 2006 to
June 2014 with 2,063 observations. Empirical result shows that there is significant asymmetry and fat-tail of individual return, and strong tail-dependence among returns, especially between KOSPI and FX returns, during the 2008 Global Financial Crisis period. Focused only on recent 30 months, we find that the correlation between stock and bond markets shows dramatic increase, and system-wide correlation wanders around zero, which possibly indicates market tranquility from a systemic perspective.
Further Results on Characteristic Functions Without Contour Integration
Song, Dae-Kun ; Kang, Seul-Ki ; Kim, Hyoung-Moon ;
Communications for Statistical Applications and Methods, volume 21, issue 5, 2014, Pages 461~469
DOI : 10.5351/CSAM.2014.21.5.461
Characteristic functions play an important role in probability and statistics; however, a rigorous derivation of these functions requires contour integration, which is unfamiliar to most statistics students. Without resorting to contour integration, Datta and Ghosh (2007) derived the characteristic functions of normal, Cauchy, and double exponential distributions. Here, we derive the characteristic functions of t, truncated normal, skew-normal, and skew-t distributions. The characteristic functions of normal, cauchy distributions are obtained as a byproduct. The derivations are straightforward and can be presented in statistics masters theory classes.