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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
On the Study for the Simultaneous Test
Park, Hyo-Il ;
Communications for Statistical Applications and Methods, volume 20, issue 4, 2013, Pages 241~246
DOI : 10.5351/CSAM.2013.20.4.241
In this study, we propose a nonparametric simultaneous test procedure for the location translation and scale parameters. We consider the Wilcoxon rank sum test for the location translation parameter and the Mood test for the scale parameter with the quadratic and maximal types of combining functions. Then we derive the limiting null distributions of the combining functions. We illustrate our procedure with an example and compare efficiency by obtaining the empirical powers through a simulation study. Finally, we discuss some interesting features related to the nonparametric simultaneous tests.
A Class of Estimators for Population Variance in Two Occasion Rotation Patterns
Singh, G.N. ; Priyanka, Priyanka ; Prasad, Shakti ; Singh, Sarjinder ; Kim, Jong-Min ;
Communications for Statistical Applications and Methods, volume 20, issue 4, 2013, Pages 247~257
DOI : 10.5351/CSAM.2013.20.4.247
A variety of practical problems can be addressed in the framework of rotation (successive) sampling. The present work presents a sample rotation pattern where sampling units are drawn on two successive occasions. The problem of estimation of population variance on current (second) occasion in two - occasion successive (rotation) sampling has been considered. A class of estimators has been proposed for population variance that includes many estimators as a particular case. Asymptotic properties of the proposed class of estimators are discussed. The proposed class of estimators is compared with the sample variance estimator when there is no matching from the previous occasion. Optimum replacement policy is discussed. Results are supported with the empirical means of comparison.
Two-Stage Penalized Composite Quantile Regression with Grouped Variables
Bang, Sungwan ; Jhun, Myoungshic ;
Communications for Statistical Applications and Methods, volume 20, issue 4, 2013, Pages 259~270
DOI : 10.5351/CSAM.2013.20.4.259
This paper considers a penalized composite quantile regression (CQR) that performs a variable selection in the linear model with grouped variables. An adaptive sup-norm penalized CQR (ASCQR) is proposed to select variables in a grouped manner; in addition, the consistency and oracle property of the resulting estimator are also derived under some regularity conditions. To improve the efficiency of estimation and variable selection, this paper suggests the two-stage penalized CQR (TSCQR), which uses the ASCQR to select relevant groups in the first stage and the adaptive lasso penalized CQR to select important variables in the second stage. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.
Functional Data Classification of Variable Stars
Park, Minjeong ; Kim, Donghoh ; Cho, Sinsup ; Oh, Hee-Seok ;
Communications for Statistical Applications and Methods, volume 20, issue 4, 2013, Pages 271~281
DOI : 10.5351/CSAM.2013.20.4.271
This paper considers a problem of classification of variable stars based on functional data analysis. For a better understanding of galaxy structure and stellar evolution, various approaches for classification of variable stars have been studied. Several features that explain the characteristics of variable stars (such as color index, amplitude, period, and Fourier coefficients) were usually used to classify variable stars. Excluding other factors but focusing only on the curve shapes of variable stars, Deb and Singh (2009) proposed a classification procedure using multivariate principal component analysis. However, this approach is limited to accommodate some features of the light curve data that are unequally spaced in the phase domain and have some functional properties. In this paper, we propose a light curve estimation method that is suitable for functional data analysis, and provide a classification procedure for variable stars that combined the features of a light curve with existing functional data analysis methods. To evaluate its practical applicability, we apply the proposed classification procedure to the data sets of variable stars from the project STellar Astrophysics and Research on Exoplanets (STARE).
Statistical Analysis of Bivariate Recurrent Event Data with Incomplete Observation Gaps
Kim, Yang-Jin ;
Communications for Statistical Applications and Methods, volume 20, issue 4, 2013, Pages 283~290
DOI : 10.5351/CSAM.2013.20.4.283
Subjects can experience two types of recurrent events in a longitudinal study. In addition, there may exist intermittent dropouts that results in repeated observation gaps during which no recurrent events are observed. Therefore, theses periods are regarded as non-risk status. In this paper, we consider a special case where information on the observation gap is incomplete, that is, the termination time of observation gap is not available while the starting time is known. For a statistical inference, incomplete termination time is incorporated in terms of interval-censored data and estimated with two approaches. A shared frailty effect is also employed for the association between two recurrent events. An EM algorithm is applied to recover unknown termination times as well as frailty effect. We apply the suggested method to young drivers' convictions data with several suspensions.
Method-Free Permutation Predictor Hypothesis Tests in Sufficient Dimension Reduction
Lee, Kyungjin ; Oh, Suji ; Yoo, Jae Keun ;
Communications for Statistical Applications and Methods, volume 20, issue 4, 2013, Pages 291~300
DOI : 10.5351/CSAM.2013.20.4.291
In this paper, we propose method-free permutation predictor hypothesis tests in the context of sufficient dimension reduction. Different from an existing method-free bootstrap approach, predictor hypotheses are evaluated based on p-values; therefore, usual statistical practitioners should have a potential preference. Numerical studies validate the developed theories, and real data application is provided.
Predicting Gross Box Office Revenue for Domestic Films
Song, Jongwoo ; Han, Suji ;
Communications for Statistical Applications and Methods, volume 20, issue 4, 2013, Pages 301~309
DOI : 10.5351/CSAM.2013.20.4.301
This paper predicts gross box office revenue for domestic films using the Korean film data from 2008-2011. We use three regression methods, Linear Regression, Random Forest and Gradient Boosting to predict the gross box office revenue. We only consider domestic films with a revenue size of at least KRW 500 million; relevant explanatory variables are chosen by data visualization and variable selection techniques. The key idea of analyzing this data is to construct the meaningful explanatory variables from the data sources available to the public. Some variables must be categorized to conduct more effective analysis and clustering methods are applied to achieve this task. We choose the best model based on performance in the test set and important explanatory variables are discussed.
A Robust Estimation for the Composite Lognormal-Pareto Model
Pak, Ro Jin ;
Communications for Statistical Applications and Methods, volume 20, issue 4, 2013, Pages 311~319
DOI : 10.5351/CSAM.2013.20.4.311
Cooray and Ananda (2005) proposed a composite lognormal-Pareto model to analyze loss payment data in the actuarial and insurance industries. Their model is based on a lognormal density up to an unknown threshold value and a two-parameter Pareto density. In this paper, we implement the minimum density power divergence estimation for the composite lognormal-Pareto density. We compare the performances of the minimum density power divergence estimator (MDPDE) and the maximum likelihood estimator (MLE) by simulations and an example. The minimum density power divergence estimator performs reasonably well against various violations in the distribution. The minimum density power divergence estimator better fits small observations and better resists against extraordinary large observations than the maximum likelihood estimator.
Constrained Bayes and Empirical Bayes Estimator Applications in Insurance Pricing
Kim, Myung Joon ; Kim, Yeong-Hwa ;
Communications for Statistical Applications and Methods, volume 20, issue 4, 2013, Pages 321~327
DOI : 10.5351/CSAM.2013.20.4.321
Bayesian and empirical Bayesian methods have become quite popular in the theory and practice of statistics. However, the objective is to often produce an ensemble of parameter estimates as well as to produce the histogram of the estimates. For example, in insurance pricing, the accurate point estimates of risk for each group is necessary and also proper dispersion estimation should be considered. Well-known Bayes estimates (which is the posterior means under quadratic loss) are underdispersed as an estimate of the histogram of parameters. The adjustment of Bayes estimates to correct this problem is known as constrained Bayes estimators, which are matching the first two empirical moments. In this paper, we propose a way to apply the constrained Bayes estimators in insurance pricing, which is required to estimate accurately both location and dispersion. Also, the benefit of the constrained Bayes estimates will be discussed by analyzing real insurance accident data.
An Improvement of the James-Stein Estimator with Some Shrinkage Points using the Stein Variance Estimator
Lee, Ki Won ; Baek, Hoh Yoo ;
Communications for Statistical Applications and Methods, volume 20, issue 4, 2013, Pages 329~337
DOI : 10.5351/CSAM.2013.20.4.329
Consider a p-variate(
) normal distribution with mean
and covariance matrix
for any unknown scalar
. In this paper we improve the James-Stein estimator of
in cases of shrinking toward some vectors using the Stein variance estimator. It is also shown that this domination does not hold for the positive part versions of these estimators.
A Functional Central Limit Theorem for an ARMA(p, q) Process with Markov Switching
Lee, Oesook ;
Communications for Statistical Applications and Methods, volume 20, issue 4, 2013, Pages 339~345
DOI : 10.5351/CSAM.2013.20.4.339
In this paper, we give a tractable sufficient condition for functional central limit theorem to hold in Markov switching ARMA (p, q) model.