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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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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
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Fast Simulation of Overflow Probabilities in Multi-Class Queues with Class-Transition
Song, Mi-Jung ; Bae, Kyung-Soon ; Lee, Ji-Yeon ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 217~228
DOI : 10.5351/CKSS.2009.16.2.217
In this paper, we consider a multi-class queueing system in which different classes of customers have different arrival rates, service rates and class-transition probabilities. We use the fast simulation method to estimate the overflow probability and the expected number of customers of each class at the first time the total number of customers hits a high level. We also discuss the overflow probabilities and the expected number of customers at different loads, respectively.
Maximum Trimmed Likelihood Estimator for Categorical Data Analysis
Choi, Hyun-Jip ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 229~238
DOI : 10.5351/CKSS.2009.16.2.229
We propose a simple algorithm for obtaining MTL(maximum trimmed likelihood) estimates. The algorithm finds the subset to use to obtain the global maximum in the series of eliminating process which depends on the likelihood of cells in a contingency table. To evaluate the performance of the algorithm for MTL estimators, we conducted simulation studies. The results showed that the algorithm is very competitive in terms of computational burdens required to get the same or the similar results in comparison with the complete enumeration.
Criterion of Test Statistics for Validation in Credit Rating Model
Park, Yong-Seok ; Hong, Chong-Sun ; Lim, Han-Seung ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 239~347
DOI : 10.5351/CKSS.2009.16.2.239
This paper presents Kolmogorov-Smirnov, mean difference, AUROC and AR, four well known statistics that have been widely used for evaluating the discriminatory power of credit rating models. Criteria for these statistics are determined by the value of mean difference under the assumption of normality and equal standard deviation. Alternative criteria are proposed through the simulations according to various sample sizes, type II error rates, and the ratio of bads, also we suggest the meaning of statistic on the basis of discriminatory power. Finally we make a comparative study of the currently used guidelines and simulated results.
Pricing an Outside Barrier Equity-Indexed Annuity with Flexible Monitoring Period
Shin, Seung-Hee ; Lee, Hang-Suck ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 249~264
DOI : 10.5351/CKSS.2009.16.2.249
Equity-indexed annuities(EIAs) provide their customers with the greater of either the return linked to the underlying index or the minimum guaranteed return. Insurance companies have developed EIAs to attract customers reluctant to buy traditional fixed annuities because of low returns and also reluctant to buy mutual funds for fear of the high volatility in the stock market. This paper proposes a new type of EIA embedded with an outside barrier option with flexible monitoring period in order to increase its participation rate. It also derives an explicit pricing formula for this proposed product, and discusses numerical examples to show relationships among participation rate, barrier level, index volatility and correlation.
Generalized Maximum Entropy Estimator for the Linear Regression Model with a Spatial Autoregressive Disturbance
Cheon, Soo-Young ; Lim, Seong-Seop ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 265~275
DOI : 10.5351/CKSS.2009.16.2.265
This paper considers a linear regression model with a spatial autoregressive disturbance with ill-posed data and proposes the generalized maximum entropy(GME) estimator of regression coefficients. The performance of this estimator is investigated via Monte Carlo experiments. The results show that the GME estimator provides efficient and robust estimate for the unknown parameter.
Registration and Visualization of Medical Image Using Conditional Entropy and 3D Volume Rendering
Kim, Sun-Worl ; Cho, Wan-Hyun ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 277~286
DOI : 10.5351/CKSS.2009.16.2.277
Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. In this paper, we introduce a robust brain registration technique for correcting the difference between two temporal images by the different coordinate systems in MR and CT image obtained from the same patient. Two images are registered where this measure is minimized using a modified conditional entropy(MCE: Modified Conditional Entropy) computed from the joint histograms for the intensities of two given images, we conduct the rendering for visualization of 3D volume image.
Prediction of Conditional Variance under GARCH Model Based on Bootstrap Methods
Kim, Hee-Young ; Park, Man-Sik ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 287~297
DOI : 10.5351/CKSS.2009.16.2.287
In terms of generalized autoregressive conditional heteroscedastic(GARCH) model, estimation of prediction interval based on likelihood is quite sensitive to distribution of error. Moveover, it is not an easy job to construct prediction interval for conditional variance. Recent studies show that the bootstrap method can be one of the alternatives for solving the problems. In this paper, we introduced the bootstrap approach proposed by Pascual et al. (2006). We employed it to Korean stock price data set.
Exact Constrained Optimal Design
Kim, Young-Il ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 299~308
DOI : 10.5351/CKSS.2009.16.2.299
It is very rare to conduct an experimental design with a single objective in mind. since we have uncertainties in model and its assumptions. Basically we have three approaches in literature to handle this problem, the mini-max, compound, constrained experimental design. Since Cook and Wong (1994) announced the equivalence between the compound and the constrained design, many constrained experimental design approaches have adopted the approximate design algorithm of compound experimental design. In this paper we attempt to modify the row-exchange algorithm under exact experimental design setting, not approximate experimental design one. This attempt will provide more realistic design setting for the field experiment. In this process we proposed another criterion on how to set the constrained experimental design. A graph to show the general issue of infeasibility, which occurs quite often in constrained experimental design, is suggested.
Multiple Deletions in Logistic Regression Models
Jung, Kang-Mo ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 309~315
DOI : 10.5351/CKSS.2009.16.2.309
We extended the results of Roy and Guria (2008) to multiple deletions in logistic regression models. Since single deletions may not exactly detect outliers or influential observations due to swamping effects and masking effects, it needs multiple deletions. We developed conditional deletion diagnostics which are designed to overcome problems of masking effects. We derived the closed forms for several statistics in logistic regression models. They give useful diagnostics on the statistics.
Imputation Using Factor Score Regression
Lee, Sang-Eun ; Hwang, Hee-Jin ; Shin, Key-Il ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 317~323
DOI : 10.5351/CKSS.2009.16.2.317
Recently not even government polices but small town decisions are based on the survey data/information, so the most of government agencies/organizations demand various sample surveys in each fields for more detail information. However in conducting the sample survey, nonresponse problem rises very often and it becomes a major issue on judging the accuracy of survey. For that matters, one solution ran be using the administration data. However unfortunately most of administration data are restricted to the common users. The other solution can be the imputation. Therefore several method, of imputation are studied in various fields. In this study, in stead of the simple regression imputation method which is commonly used, factor score regression method is applied specially to the incomplete data which have the unit and item misting values in survey data. Here for simulation study, Consumer Expenditure Surveys in Korea are used.
A Test Procedure for Right Censored Data under the Additive Model
Park, Hyo-Il ; Hong, Seung-Man ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 325~334
DOI : 10.5351/CKSS.2009.16.2.325
In this research, we propose a nonparametric test procedure for the right censored and grouped data under the additive hazards model. For deriving the test statistics, we use the likelihood principle. Then we illustrate proposed test with an example and compare the performance with other procedure by obtaining empirical powers. Finally we discuss some interesting features concerning the proposed test.
Fuzzy Semiparametric Support Vector Regression for Seasonal Time Series Analysis
Shim, Joo-Yong ; Hwang, Chang-Ha ; Hong, Dug-Hun ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 335~348
DOI : 10.5351/CKSS.2009.16.2.335
Fuzzy regression is used as a complement or an alternative to represent the relation between variables among the forecasting models especially when the data is insufficient to evaluate the relation. Such phenomenon often occurs in seasonal time series data which require large amount of data to describe the underlying pattern. Semiparametric model is useful tool in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. In this paper we propose fuzzy semiparametric support vector regression so that it can provide good performance on forecasting of the seasonal time series by incorporating into fuzzy support vector regression the basis functions which indicate the seasonal variation of time series. In order to indicate the performance of this method, we present two examples of predicting the seasonal time series. Experimental results show that the proposed method is very attractive for the seasonal time series in fuzzy environments.
Goodness-of-fit Test for the Weibull Distribution Based on Multiply Type-II Censored Samples
Kang, Suk-Bok ; Han, Jun-Tae ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 349~361
DOI : 10.5351/CKSS.2009.16.2.349
In this paper, we derive the approximate maximum likelihood estimators of the shape parameter and the scale parameter in a Weibull distribution under multiply Type-II censoring by the approximate maximum likelihood estimation method. We develop three modified empirical distribution function type tests for the Weibull distribution based on multiply Type-II censored samples. We also propose modified normalized sample Lorenz curve plot and new test statistic.
Choosing between the Exact and the Approximate Confidence Intervals: For the Difference of Two Independent Binomial Proportions
Lee, Seung-Chun ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 363~372
DOI : 10.5351/CKSS.2009.16.2.363
The difference of two independent binomial proportions is frequently of interest in biomedical research. The interval estimation may be an important tool for the inferential problem. Many confidence intervals have been proposed. They can be classified into the class of exact confidence intervals or the class of approximate confidence intervals. Ore may prefer exact confidence interval s in that they guarantee the minimum coverage probability greater than the nominal confidence level. However, someone, for example Agresti and Coull (1998) claims that "approximation is better than exact." It seems that when sample size is large, the approximate interval is more preferable to the exact interval. However, the choice is not clear when sample, size is small. In this note, an exact confidence and an approximate confidence interval, which were recommended by Santner et al. (2007) and Lee (2006b), respectively, are compared in terms of the coverage probability and the expected length.
Statistical Bias and Inflated Variance in the Genehunter Nonparametric Linkage Test Statistic
Song, Hae-Hiang ; Choi, Eun-Kyeong ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 373~381
DOI : 10.5351/CKSS.2009.16.2.373
Evidence of linkage is expressed as a decreasing trend of the squared trait difference of two siblings with increasing identical by descent scores. In contrast to successes in the application of a parametric approach of Haseman-Elston regression, notably low powers are demonstrated in the nonparametric linkage analysis methods for complex traits and diseases with sib-pairs data. We report that the Genehunter nonparametric linkage statistic is biased and furthermore the variance formula that they used is an inflated one, and this is one reason for a low performance. Thus, we propose bias-corrected nonparametric linkage statistics. Simulation studies comparing our proposed nonparametric test statistics versus the existing test statistics suggest that the bias-corrected new nonparametric test statistics are more powerful and attains efficiencies close to that of Haseman-Elston regression.
Estimating Variance Function with Kernel Machine
Kim, Jong-Tae ; Hwang, Chang-Ha ; Park, Hye-Jung ; Shim, Joo-Yong ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 383~388
DOI : 10.5351/CKSS.2009.16.2.383
In this paper we propose a variance function estimation method based on kernel trick for replicated data or data consisted of sample variances. Newton-Raphson method is used to obtain associated parameter vector. Furthermore, the generalized approximate cross validation function is introduced to select the hyper-parameters which affect the performance of the proposed variance function estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.
Use of Beta-Polynomial Approximations for Variance Homogeneity Test and a Mixture of Beta Variates
Ha, Hyung-Tae ; Kim, Chung-Ah ;
Communications for Statistical Applications and Methods, volume 16, issue 2, 2009, Pages 389~396
DOI : 10.5351/CKSS.2009.16.2.389
Approximations for the null distribution of a test statistic arising in multivariate analysis to test homogeneity of variances and a mixture of two beta distributions by making use of a product of beta baseline density function and a polynomial adjustment, so called beta-polynomial density approximant, are discussed. Explicit representations of density and distribution approximants of interest in each case can easily be obtained. Beta-polynomial density approximants produce good approximation over the entire range of the test statistic and also accommodate even the bimodal distribution using an artificial example of a mixture of two beta distributions.