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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 18, Issue 6 - Nov 2011
Volume 18, Issue 5 - Sep 2011
Volume 18, Issue 4 - Jul 2011
Volume 18, Issue 3 - May 2011
Volume 18, Issue 2 - Mar 2011
Volume 18, Issue 1 - Jan 2011
Selecting the target year
A Short Note on Empirical Penalty Term Study of BIC in K-means Clustering Inverse Regression
Ahn, Ji-Hyun ; Yoo, Jae-Keun ;
Communications for Statistical Applications and Methods, volume 18, issue 3, 2011, Pages 267~275
DOI : 10.5351/CKSS.2011.18.3.267
According to recent studies, Bayesian information criteria(BIC) is proposed to determine the structural dimension of the central subspace through sliced inverse regression(SIR) with high-dimensional predictors. The BIC may be useful in K-means clustering inverse regression(KIR) with high-dimensional predictors. However, the direct application of the BIC to KIR may be problematic, because the slicing scheme in SIR is not the same as that of KIR. In this paper, we present empirical penalty term studies of BIC in KIR to identify the most appropriate one. Numerical studies and real data analysis are presented.
Warranty Analysis Based on Different Lengths of Warranty Periods
Park, Min-Jae ;
Communications for Statistical Applications and Methods, volume 18, issue 3, 2011, Pages 277~286
DOI : 10.5351/CKSS.2011.18.3.277
Global companies can sell their products with dierent warranty periods based on location and times. Customers can select the length of warranty on their own if they pay an additional fee. In this paper, we consider the warranty period and the repair time limit as random variables. A two-dimensional warranty policy is considered with repair times and failure times. The repair times are considered within the repair time limit and the failure times are considered within the warranty period. Under the non-renewable warranty policy, we obtain the expected number of warranty services and their variances in the censored area by warranty period and repair time limit to conduct a warranty cost analysis. Numerical examples are discussed to demonstrate the applicability of the methodologies and results using field data based on the proposed approach in the paper.
The Method of Improvement in Fairness on Peer Assessment - Based on Convenience Analysis
Choi, Kyoung-Ho ;
Communications for Statistical Applications and Methods, volume 18, issue 3, 2011, Pages 287~294
DOI : 10.5351/CKSS.2011.18.3.287
Peer assessment is an educational valuation system that involves studying with a colleague and granting value to the progress made by the colleague. Although this method has many merits, there is also a drawback pertaining to calculating the mean of the scores that were granted to the levels of contribution. However, this has been improved upon by a diversified study. However, the concept of the chi-square test and p-value used in the preceding study is not easy individuals engaged in the industrial engineering field or education when using peer assessment. This study uses simple statistics like standard deviation, in addition to, investigating the availability of a suggested method as well as examples of utility and application. This study can contribute to increase the convenience of users through the use of convenience analysis and with this method.
Bayesian Estimation of Uniformly Stochastically Ordered Distributions with Square Loss
Oh, Myong-Sik ;
Communications for Statistical Applications and Methods, volume 18, issue 3, 2011, Pages 295~300
DOI : 10.5351/CKSS.2011.18.3.295
The Bayesian nonparametric estimation of two uniformly stochastically ordered distributions is studied. We propose a restricted Dirichlet Process. Among many types of restriction we consider only uniformly stochastic ordering in this paper since the computation of integrals is relatively easy. An explicit expression of the posterior distribution is given. When square loss function is used the posterior distribution can be obtained by easy integration using some computer program such as Mathematica.
Probabilistic Modeling of Fiber Length Segments within a Bounded Area of Two-Dimensional Fiber Webs
Chun, Heui-Ju ;
Communications for Statistical Applications and Methods, volume 18, issue 3, 2011, Pages 301~317
DOI : 10.5351/CKSS.2011.18.3.301
Statistical and probabilistic behaviors of fibers forming fiber webs of all kinds are of great significance in the determination of the uniformity and physical properties of the webs commonly found in many industrial products such as filters, membranes and non-woven fabrics. However, in studying the spatial geometry of the webs the observations must be theoretically as well as experimentally confined within a specified unit area. This paper provides a general theory and framework for computer simulation for quantifying the fiber segments bounded by the unit area in consideration of the "edge effects" resulting from the truncated length segments within the boundary. The probability density function and the first and second moments of the length segments found within the counting region were derived by properly defining the seeding region and counting region.
Extending the Scope of Automatic Time Series Model Selection: The Package autots for R
Jang, Dong-Ik ; Oh, Hee-Seok ; Kim, Dong-Hoh ;
Communications for Statistical Applications and Methods, volume 18, issue 3, 2011, Pages 319~331
DOI : 10.5351/CKSS.2011.18.3.319
In this paper, we propose automatic procedures for the model selection of various univariate time series data. Automatic model selection is important, especially in data mining with large number of time series, for example, the number (in thousands) of signals accessing a web server during a specific time period. Several methods have been proposed for automatic model selection of time series. However, most existing methods focus on linear time series models such as exponential smoothing and autoregressive integrated moving average(ARIMA) models. The key feature that distinguishes the proposed procedures from previous approaches is that the former can be used for both linear time series models and nonlinear time series models such as threshold autoregressive(TAR) models and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA-GARCH) models. The proposed methods select a model from among the various models in the prediction error sense. We also provide an R package autots that implements the proposed automatic model selection procedures. In this paper, we illustrate these algorithms with the artificial and real data, and describe the implementation of the autots package for R.
A Digital Nervous System for Elementary Statistics Education in the Mobile Age: SmartNote
Han, Kyung-Soo ;
Communications for Statistical Applications and Methods, volume 18, issue 3, 2011, Pages 333~342
DOI : 10.5351/CKSS.2011.18.3.333
Many students in introductory statistics courses do not engage in learning under traditional classroom settings. A statistics instructor is often irritated by student behaviors such as sleeping, talking out of place, and acting bored or apathetic during lectures. The lecture and exercises in the computer laboratory should constantly compete with materials via the Internet to draw the attention of the student. To address problems in statistics education, we propose a digital nervous system in which a teacher and students can communicate with each other.
Optimal Criterion of Classification Accuracy Measures for Normal Mixture
Yoo, Hyun-Sang ; Hong, Chong-Sun ;
Communications for Statistical Applications and Methods, volume 18, issue 3, 2011, Pages 343~355
DOI : 10.5351/CKSS.2011.18.3.343
For a data with the assumption of the mixture distribution, it is important to find an appropriate threshold and evaluate its performance. The relationship is found of well-known nine classification accuracy measures such as MVD, Youden's index, the closest-to-(0, 1) criterion, the amended closest-to-(0, 1) criterion, SSS, symmetry point, accuracy area, TA, TR. Then some conditions of these measures are categorized into seven groups. Under the normal mixture assumption, we calculate thresholds based on these measures and obtain the corresponding type I and II errors. We could explore that which classification measure has minimum type I and II errors for estimated mixture distribution to understand the strength and weakness of these classification measures.
Estimating Parameters in Muitivariate Normal Mixtures
Ahn, Sung-Mahn ; Baik, Sung-Wook ;
Communications for Statistical Applications and Methods, volume 18, issue 3, 2011, Pages 357~365
DOI : 10.5351/CKSS.2011.18.3.357
This paper investigates a penalized likelihood method for estimating the parameter of normal mixtures in multivariate settings with full covariance matrices. The proposed model estimates the number of components through the addition of a penalty term to the usual likelihood function and the construction of a penalized likelihood function. We prove the consistency of the estimator and present the simulation results on the multi-dimensional nor-mal mixtures up to the 8-dimension.
Modified BLS Weight Adjustment
Park, Jung-Joon ; Cho, Ki-Jong ; Lee, Sang-Eun ; Shin, Key-Il ;
Communications for Statistical Applications and Methods, volume 18, issue 3, 2011, Pages 367~376
DOI : 10.5351/CKSS.2011.18.3.367
BLS weight adjustment is a widely used method for business surveys with non-responses and outliers. Recent surveys show that the non-response weight adjustment of the BLS method is the same as the ratio imputation method. In this paper, we suggested a modified BLS weight adjustment method by imputing missing values instead of using weight adjustment for non-response. Monthly labor survey data is used for a small Monte-Carlo simulation and we conclude that the suggested method is superior to the original BLS weight adjustment method.
On the Plug-in Estimator and its Asymptotic Distribution Results for Vector-Valued Process Capability Index C
Cho, Joong-Jae ; Park, Byoung-Sun ;
Communications for Statistical Applications and Methods, volume 18, issue 3, 2011, Pages 377~389
DOI : 10.5351/CKSS.2011.18.3.377
A higher quality level is generally perceived by customers as improved performance by assigning a correspondingly higher satisfaction score. The third generation index
is more powerful than two useful indices
that have been widely used in six sigma industries to assess process performance. In actual manufacturing industries, process capability analysis often entails characterizing or assessing processes or products based on more than one engineering specification or quality characteristic. Since these characteristics are related, it is a risky undertaking to represent the variation of even a univariate characteristic by a single index. Therefore, the desirability of using vector-valued process capability index(PCI) arises quite naturally. In this paper, we consider more powerful vector-valued process capability index
that consider the univariate process capability index
. First, we examine the process capability index
and plug-in estimator
. In addition, we derive its asymptotic distribution and variance-covariance matrix
for the vector valued process capability index
. Under the assumption of bivariate normal distribution, we study asymptotic confidence regions of our vector-valued process capability index
Identification of Cluster with Composite Mean and Variance
Kim, Seung-Gu ;
Communications for Statistical Applications and Methods, volume 18, issue 3, 2011, Pages 391~401
DOI : 10.5351/CKSS.2011.18.3.391
Consider a cluster, so called a 'son cluster', whose mean and variance is composed of the means and variances of both clusters called as a 'father cluster' and a 'mother cluster'. In this paper, a method for identifying each of three clusters is provided by modeling the relationship with father and mother clusters. Under the normal mixture model, the parameters are estimated via EM algorithm. We were able to overcome the problems of estimation using ECM approximation. Numerical examples show that our method can effectively identify the three clusters, so called a 'family of clusters'.