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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 16, Issue 6 - Nov 2009
Volume 16, Issue 5 - Sep 2009
Volume 16, Issue 4 - Jul 2009
Volume 16, Issue 3 - May 2009
Volume 16, Issue 2 - Mar 2009
Volume 16, Issue 1 - Jan 2009
Selecting the target year
Release of Microdata and Statistical Disclosure Control Techniques
Kim, Kyu-Seong ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 1~11
DOI : 10.5351/CKSS.2009.16.1.001
When micro data are released to users, record by record data are disclosed and the disclosure risk of respondent's information is inevitable. Statistical disclosure control techniques are statistical tools to reduce the risk of disclosure as well as to increase data utility in case of data release. In this paper, we reviewed the concept of disclosure and disclosure risk as well as statistical disclosure control techniques and then investigated selection strategies of a statistical disclosure control technique related with data utility. The risk-utility frontier map method was illustrated as an example. Finally, we listed some check points at each step when microdata are released.
Performance Evaluation of the ACD Models for Analysing the Transaction Data of the KOSPI Stocks
Kim, Sahm ; Jung, Da-Woon ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 21~29
DOI : 10.5351/CKSS.2009.16.1.021
Engle and Russell (1998) proposed the ACD(Autoregressive Conditional Duration) model to explain the relationship between the prices and the duration times of the stocks. In this paper, we first introduce the various types of the ACD models such as the linear ACD, log ACD and Box-Cox ACD models and we evaluate the performance of the models for analysing the transaction data of the stocks in Korea.
Bayes Inference for the Spatial Time Series Model
Lee, Sung-Duck ; Kim, In-Kyu ; Kim, Duk-Ki ; Chung, Ae-Ran ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 31~40
DOI : 10.5351/CKSS.2009.16.1.031
Spatial time series data can be viewed either as a set of time series collected simultaneously at a number of spatial locations. In this paper, We estimate the parameters of spatial time autoregressive moving average (SIARMA) process by method of Gibbs sampling. Finally, We apply this method to a set of U.S. Mumps data over a 12 states region.
Seasonal Adjustment on Chain-Linking
Jeon, Gyeong-Bae ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 41~50
DOI : 10.5351/CKSS.2009.16.1.041
Chain-linking is a method for aggregating volume measures which would improve the quality of estimates of economic growth over the present fixed base in Korea. There is a risk that choice of chain-linking techniques such annual overlap, one-quarter overlap or over-the-year overlap may create an artificial seasonality to the volume series. The empirical results on Korean GDP suggest that the use of the annual overlap is recommended. And conducting seasonal adjustment after chain-linking to produce the chain-linked seasonally adjusted GDP is more appropriated in Korea.
Precision and Safety Comparison for SM, CRM and ATD in Phase I Clinical Trials
Kim, Dong-Uk ; Kil, Sun-Kyoung ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 51~65
DOI : 10.5351/CKSS.2009.16.1.051
The purpose of a phase I clinical trial is to determine the maximum tolerated dose(MTD) of a new drug. This paper investigates the performance of standard method, continual reassessment method and accelerated titration designs in phase I clinical trials. Especially we study the precision and safety at the MTD of these methods. We utilize hyperbolic tangent function and power function to define dose-toxicity model. For each method, expected toxicity rate at MTD is computed and compared with target toxicity probability. We also suggest some modifications of these methods and show some improvements in performance.
Important SNPs Identification from the Economic Traits for the High Quality Korean Cattle
Lee, Jea-Young ; Kim, Dong-Chul ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 67~74
DOI : 10.5351/CKSS.2009.16.1.067
In order to make the high quality Korean cattle, it has been identified the gene markers which influence to various economic traits. To identify statistically significances among SNP markers, Lee et. al. (2008b) identified SNP(19_1)
SNP(28_2) marker was an important marker in LMA(longissimus muscle dorsi area). In addition, CWT(carcass cold weight) and ADG(average daily gain) are applied for expanded multifactor dimensionality reduction (expanded MDR) method from the comprehensive economic traits. The results showed that SNP(19_1)
SNP(28_2) interaction marker was good and a very meaningful for economic traits.
Parallelism Test of Slope in Simple Linear Regression Models
Park, Hyun-Wook ; Kim, Dong-Jae ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 75~83
DOI : 10.5351/CKSS.2009.16.1.075
Parallelism tests are proposed for slope in the simple linear regression models. In this paper, we suggest the parametric test using HSD testing method (Tukey,1953) and distribution-free test using Kruskal-wallis (1952) for more than three slopes. Monte Carlo simulation study is adapted to compare the power of the proposed methods with Wilks' Lambda multivariate procedure.
Decrement Models with an Application to Variable Annuities under Fractional Age Distributions
Lee, Hang-Suck ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 85~102
DOI : 10.5351/CKSS.2009.16.1.085
This paper derives conversion formulas from yearly-based absolute rates of decrements to monthly-based rates of decrement due to cause J under fractional age distributions. Next, it suggests conversion formulas from monthly-based absolute rates of decrements to monthly-based rates of decrement due to cause j under fractional age distributions. In addition, it applies the conversion formulas including a dynamic lapse rate model to variable annuities. Some numerical examples are discussed.
Forming Weighting Adjustment Cells for Unit-Nonresponse in Sample Surveys
Kim, Young-Won ; Nam, Si-Ju ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 103~113
DOI : 10.5351/CKSS.2009.16.1.103
Weighting is a common form of unit nonresponse adjustment in sample surveys where entire questionnaires are missing due to noncontact or refusal to participate. A common approach computes the response weight as the inverse of the response rate within adjustment cells based on covariate information. In this paper, we consider the efficiency and robustness of nonresponse weight adjustment bated on the response propensity and predictive mean. In the simulation study based on 2000 Fishry Census in Korea, the root mean squared errors for assessing the various ways of forming nonresponse adjustment cell s are investigated. The simulation result suggest that the most important feature of variables for inclusion in weighting adjustment is that they are predictive of survey outcomes. Though useful, prediction of the propensity to response is a secondary. Also the result suggest that adjustment cells based on joint classification by the response propensity and predictor of the outcomes is productive.
A Test of the Rank Conditions in the Simultaneous Equation Models
So, Sun-Ha ; Park, You-Sung ; Lee, Dong-Hee ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 115~125
DOI : 10.5351/CKSS.2009.16.1.115
Simultaneous equation models, which are widely used in business and economic areas, generally consist of endogenous variables determined within models and exogenous variables externally determined and in the simultaneous equations model framework there are rank and order conditions for the model identification and the existence of unique solutions. By contrast, their estimating results have less efficiencies and furthermore do not exist, since the most estimating procedures are performed under the assumptions for rank and order conditions. We propose the new statistical test for sufficiency of the rank condition under the order condition, and show the asymptotic properties for the test. The Monte Carlo simulation studies are achieved in order to evaluate its power and to suggest the baseline for satisfying the rank conditions.
Statistical Modeling for Forecasting Maximum Electricity Demand in Korea
Yoon, Sang-Hoo ; Lee, Young-Saeng ; Park, Jeong-Soo ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 127~135
DOI : 10.5351/CKSS.2009.16.1.127
It is necessary to forecast the amount of the maximum electricity demand for stabilizing the flow of electricity. The time series data was collected from the Korea Energy Research between January 2000 and December 2006. The data showed that they had a strong linear trend and seasonal change. Winters seasonal model, ARMA model were used to examine it. Root mean squared prediction error and mean absolute percentage prediction error were a criteria to select the best model. In addition, a nonstationary generalized extreme value distribution with explanatory variables was fitted to forecast the maximum electricity.
Choice of the Kernel Function in Smoothing Moment Restrictions for Dependent Processes
Lee, Jin ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 137~141
DOI : 10.5351/CKSS.2009.16.1.137
We study on selecting the kernel weighting function in smoothing moment conditions for dependent processes. For hypothesis testing in Generalized Method of Moments or Generalized Empirical Likelihood context, we find that smoothing moment conditions by Bartlett kernel delivers smallest size distortions based on empirical Edgeworth expansions of the long-run variance estimator.
Variable Selection Based on Mutual Information
Huh, Moon-Y. ; Choi, Byong-Su ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 143~155
DOI : 10.5351/CKSS.2009.16.1.143
Best subset selection procedure based on mutual information (MI) between a set of explanatory variables and a dependent class variable is suggested. Derivation of multivariate MI is based on normal mixtures. Several types of normal mixtures are proposed. Also a best subset selection algorithm is proposed. Four real data sets are employed to demonstrate the efficiency of the proposals.
The Uniform Law of Large Numbers for the Baker Transformation
Bae, Jong-Sig ; Hwang, Chang-Ha ; Shim, Joo-Yong ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 157~162
DOI : 10.5351/CKSS.2009.16.1.157
The baker transformation is an ergodic transformation defined on the half open unit square. This paper considers the limiting behavior of the partial sum process of a martingale sequence constructed from the baker transformation. We get the uniform law of large numbers for the baker transformation.
Assessing the Accuracy of Outlier Tests in Nonlinear Regression
Kahng, Myung-Wook ; Kim, Bu-Yang ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 163~168
DOI : 10.5351/CKSS.2009.16.1.163
Given the specific mean shift outlier model, the standard approaches to obtaining test statistics for outliers are discussed. Accuracy of outlier tests is investigated using subset curvatures. These subset curvatures appear to be reliable indicators of the adequacy of the linearization based test. Also, we consider obtaining graphical summaries of uncertainty in estimating parameters through confidence curves. The results are applied to the problem of assessing the accuracy of outlier tests.
Unrelated Question Model in Sensitive Multi-Character Surveys
Sidhu, Sukhjinder Singh ; Bansal, Mohan Lal ; Kim, Jong-Min ; Singh, Sarjinder ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 169~183
DOI : 10.5351/CKSS.2009.16.1.169
The simplicity and wide application of Greenberg et al. (1971) prompts to propose a set of alternative estimators of population total for multi-character surveys that elicit simultaneous information on many. sensitive study variables. The proposed estimators take into account the already known rough value of the correlation coefficient between Y(the characteristic under study) and p(the measure of size). These estimators are biased, but it is expected that the extent of bias will be smaller, since the proposed estimators are suitable for situations in between those optimum for the usual estimators and the estimators based on multi-characters for no correlation. The relative efficiency of the proposed estimators has been studied under a super population model through empirical study. It has been found through simulation study that a choice of an unrelated variable in the Greenberg et al. (1971) model could be made based on its correlation with the auxiliary variable used at estimation stage in multi-character surveys.
Visualizing Multi-Variable Prediction Functions by Segmented k-CPG's
Huh, Myung-Hoe ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 185~193
DOI : 10.5351/CKSS.2009.16.1.185
Machine learning methods such as support vector machines and random forests yield nonparametric prediction functions of the form y =
. As a sequel to the previous article (Huh and Lee, 2008) for visualizing nonparametric functions, I propose more sensible graphs for visualizing y =
herein which has two clear advantages over the previous simple graphs. New graphs will show a small number of prototype curves of
, revealing statistically plausible portion over the interval of
which changes with (
). To complement the visual display, matching importance measures for each of p predictor variables are produced. The proposed graphs and importance measures are validated in simulated settings and demonstrated for an environmental study.
Multi-Optimal Designs for Second-Order Response Surface Models
Park, You-Jin ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 195~208
DOI : 10.5351/CKSS.2009.16.1.195
A conventional single design optimality criterion has been used to select an efficient experimental design. But, since an experimental design is constructed with respect to an optimality criterion pre specified by investigators, an experimental design obtained from one optimality criterion which is superior to other designs may perform poorly when the design is evaluated by another optimality criterion. In other words, none of these is entirely satisfactory and even there is no guarantee that a design which is constructed from using a certain design optimality criterion is also optimal to the other design optimality criteria. Thus, it is necessary to develop certain special types of experimental designs that satisfy multiple design optimality criteria simultaneously because these multi-optimal designs (MODs) reflect the needs of the experimenters more adequately. In this article, we present a heuristic approach to construct second-order response surface designs which are more flexible and potentially very useful than the designs generated from a single design optimality criterion in many real experimental situations when several competing design optimality criteria are of interest. In this paper, over cuboidal design region for
variables, we construct multi-optimal designs (MODs) that might moderately satisfy two famous alphabetic design optimality criteria, G- and IV-optimality criteria using a GA which considers a certain amount of randomness. The minimum, average and maximum scaled prediction variances for the generated response surface designs are provided. Based on the average and maximum scaled prediction variances for k = 3, 4 and 5 design variables, the MODs from a genetic algorithm (GA) have better statistical property than does the theoretically optimal designs and the MODs are more flexible and useful than single-criterion optimal designs.
Quantile Regression with Non-Convex Penalty on High-Dimensions
Choi, Ho-Sik ; Kim, Yong-Dai ; Han, Sang-Tae ; Kang, Hyun-Cheol ;
Communications for Statistical Applications and Methods, volume 16, issue 1, 2009, Pages 209~215
DOI : 10.5351/CKSS.2009.16.1.209
In regression problem, the SCAD estimator proposed by Fan and Li (2001), has many desirable property such as continuity, sparsity and unbiasedness. In this paper, we extend SCAD penalized regression framework to quantile regression and hence, we propose new SCAD penalized quantile estimator on high-dimensions and also present an efficient algorithm. From the simulation and real data set, the proposed estimator performs better than quantile regression estimator with