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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
Issues Related to the Use of Time Series in Model Building and Analysis: Review Article
Wei, William W.S. ;
Communications for Statistical Applications and Methods, volume 22, issue 3, 2015, Pages 209~222
DOI : 10.5351/CSAM.2015.22.3.209
Time series are used in many studies for model building and analysis. We must be very careful to understand the kind of time series data used in the analysis. In this review article, we will begin with some issues related to the use of aggregate and systematic sampling time series. Since several time series are often used in a study of the relationship of variables, we will also consider vector time series modeling and analysis. Although the basic procedures of model building between univariate time series and vector time series are the same, there are some important phenomena which are unique to vector time series. Therefore, we will also discuss some issues related to vector time models. Understanding these issues is important when we use time series data in modeling and analysis, regardless of whether it is a univariate or multivariate time series.
VUS and HUM Represented with Mann-Whitney Statistic
Hong, Chong Sun ; Cho, Min Ho ;
Communications for Statistical Applications and Methods, volume 22, issue 3, 2015, Pages 223~232
DOI : 10.5351/CSAM.2015.22.3.223
The area under the ROC curve (AUC), the volume under the ROC surface (VUS) and the hypervolume under the ROC manifold (HUM) are defined and interpreted with probability that measures the discriminant power of classification models. AUC, VUS and HUM are expressed with the summation and integration notations for discrete and continuous random variables, respectively. AUC for discrete two random samples is represented as the nonparametric Mann-Whitney statistic. In this work, we define conditional Mann-Whitney statistics to compare more than two discrete random samples as well as propose that VUS and HUM are represented as functions of the conditional Mann-Whitney statistics. Three and four discrete random samples with some tie values are generated. Values of VUS and HUM are obtained using the proposed statistic. The values of VUS and HUM are identical with those obtained by definition; therefore, both VUS and HUM could be represented with conditional Mann-Whitney statistics proposed in this paper.
A Comparison of Size and Power of Tests of Hypotheses on Parameters Based on Two Generalized Lindley Distributions
Okwuokenye, Macaulay ; Peace, Karl E. ;
Communications for Statistical Applications and Methods, volume 22, issue 3, 2015, Pages 233~239
DOI : 10.5351/CSAM.2015.22.3.233
This study compares two generalized Lindley distributions and assesses consistency between theoretical and analytical results. Data (complete and censored) assumed to follow the Lindley distribution are generated and analyzed using two generalized Lindley distributions, and maximum likelihood estimates of parameters from the generalized distributions are obtained. Size and power of tests of hypotheses on the parameters are assessed drawing on asymptotic properties of the maximum likelihood estimates. Results suggest that whereas size of some of the tests of hypotheses based on the considered generalized distributions are essentially
-level, some are possibly not; power of tests of hypotheses on the Lindley distribution parameter from the two distributions differs.
Bayesian Typhoon Track Prediction Using Wind Vector Data
Han, Minkyu ; Lee, Jaeyong ;
Communications for Statistical Applications and Methods, volume 22, issue 3, 2015, Pages 241~253
DOI : 10.5351/CSAM.2015.22.3.241
In this paper we predict the track of typhoons using a Bayesian principal component regression model based on wind field data. Data is obtained at each time point and we applied the Bayesian principal component regression model to conduct the track prediction based on the time point. Based on regression model, we applied to variable selection prior and two kinds of prior distribution; normal and Laplace distribution. We show prediction results based on Bayesian Model Averaging (BMA) estimator and Median Probability Model (MPM) estimator. We analysis 8 typhoons in 2006 using data obtained from previous 6 years (2000-2005). We compare our prediction results with a moving-nest typhoon model (MTM) proposed by the Korea Meteorological Administration. We posit that is possible to predict the track of a typhoon accurately using only a statistical model and without a dynamical model.
Generalized Ratio-Cum-Product Type Estimator of Finite Population Mean in Double Sampling for Stratification
Tailor, Rajesh ; Lone, Hilal A. ; Pandey, Rajiv ;
Communications for Statistical Applications and Methods, volume 22, issue 3, 2015, Pages 255~264
DOI : 10.5351/CSAM.2015.22.3.255
This paper addressed the problem of estimation of finite population mean in double sampling for stratification. This paper proposed a generalized ratio-cum-product type estimator of population mean. The bias and mean square error of the proposed estimator has been obtained upto the first degree of approximation. A particular member of the proposed generalized estimator was identified and studied from a comparison point of view. It is observed that the identified particular estimator is more efficient than usual unbiased estimator and Ige and Tripathi (1987) estimators. An empirical study was conducted in support of the theoretical findings.
Exploratory Methods for Joint Distribution Valued Data and Their Application
Igarashi, Kazuto ; Minami, Hiroyuki ; Mizuta, Masahiro ;
Communications for Statistical Applications and Methods, volume 22, issue 3, 2015, Pages 265~276
DOI : 10.5351/CSAM.2015.22.3.265
In this paper, we propose hierarchical cluster analysis and multidimensional scaling for joint distribution valued data. Information technology is increasing the necessity of statistical methods for large and complex data. Symbolic Data Analysis (SDA) is an attractive framework for the data. In SDA, target objects are typically represented by aggregated data. Most methods on SDA deal with objects represented as intervals and histograms. However, those methods cannot consider information among variables including correlation. In addition, objects represented as a joint distribution can contain information among variables. Therefore, we focus on methods for joint distribution valued data. We expanded the two well-known exploratory methods using the dissimilarities adopted Hall Type relative projection index among joint distribution valued data. We show a simulation study and an actual example of proposed methods.
On Numerical Computation of Pickands Constants
Choi, Hyemi ;
Communications for Statistical Applications and Methods, volume 22, issue 3, 2015, Pages 277~283
DOI : 10.5351/CSAM.2015.22.3.277
appears in the classical result about tail probabilities of the extremes of Gaussian processes and there exist several different representations of Pickands constant. However, the exact value of
is unknown except for two special Gaussian processes. Significant effort has been made to find numerical approximations of
. In this paper, we attempt to compute numerically
based on its representation derived by
(1999) and Albin and Choi (2010). Our estimates are compared with the often quoted conjecture
for 0 <
2. This conjecture does not seem compatible with our simulation result for 1 <
< 2, which is also recently observed by Dieker and Yakir (2014) who devised a reliable algorithm to estimate these constants along with a detailed error analysis.
A Note on Bootstrapping in Sufficient Dimension Reduction
Yoo, Jae Keun ; Jeong, Sun ;
Communications for Statistical Applications and Methods, volume 22, issue 3, 2015, Pages 285~294
DOI : 10.5351/CSAM.2015.22.3.285
A permutation test is the popular and attractive alternative to derive asymptotic distributions of dimension test statistics in sufficient dimension reduction methodologies; however, recent studies show that a bootstrapping technique also can be used. We consider two types of bootstrapping dimension determination, which are partial and whole bootstrapping procedures. Numerical studies compare the permutation test and the two bootstrapping procedures; subsequently, real data application is presented. Considering two additional bootstrapping procedures to the existing permutation test, one has more supporting evidence for the dimension estimation of the central subspace that allow it to be determined more convincingly.
Estimation of Hurst Parameter in Longitudinal Data with Long Memory
Kim, Yoon Tae ; Park, Hyun Suk ;
Communications for Statistical Applications and Methods, volume 22, issue 3, 2015, Pages 295~304
DOI : 10.5351/CSAM.2015.22.3.295
This paper considers the problem of estimation of the Hurst parameter H
(1/2, 1) from longitudinal data with the error term of a fractional Brownian motion with Hurst parameter H that gives the amount of the long memory of its increment. We provide a new estimator of Hurst parameter H using a two scale sampling method based on
-Sahalia and Jacod (2009). Asymptotic behaviors (consistent and central limit theorem) of the proposed estimator will be investigated. For the proof of a central limit theorem, we use recent results on necessary and sufficient conditions for multi-dimensional vectors of multiple stochastic integrals to converges in distribution to multivariate normal distribution studied by Nourdin et al. (2010), Nualart and Ortiz-Latorre (2008), and Peccati and Tudor (2005).
Generalized Partially Double-Index Model: Bootstrapping and Distinguishing Values
Yoo, Jae Keun ;
Communications for Statistical Applications and Methods, volume 22, issue 3, 2015, Pages 305~312
DOI : 10.5351/CSAM.2015.22.3.305
We extend a generalized partially linear single-index model and newly define a generalized partially double-index model (GPDIM). The philosophy of sufficient dimension reduction is adopted in GPDIM to estimate unknown coefficient vectors in the model. Subsequently, various combinations of popular sufficient dimension reduction methods are constructed with the best combination among many candidates determined through a bootstrapping procedure that measures distances between subspaces. Distinguishing values are newly defined to match the estimates to the corresponding population coefficient vectors. One of the strengths of the proposed model is that it can investigate the appropriateness of GPDIM over a single-index model. Various numerical studies confirm the proposed approach, and real data application are presented for illustration purposes.