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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 13, Issue 3 - Dec 2006
Volume 13, Issue 2 - Aug 2006
Volume 13, Issue 1 - Apr 2006
Selecting the target year
Moving Estimates Test for Jumps in Time Series Models
Na, O-Kyoung ; Lee, Seon-Joo ; Lee, Sang-Yeol ; Choi, In-Bong ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 205~217
DOI : 10.5351/CKSS.2006.13.2.205
In this paper, we consider the problem of testing for a change of the parameter function
that may have a discontinuity at some unknown point
. We introduce a varying-h moving estimate to test the null hypothesis that
is continuous against the alternative that
. Simulation results are provided for illustration.
Graphical Methods for Correlation and Independence
Hong, Chong-Sun ; Yoon, Jang-Sub ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 219~231
DOI : 10.5351/CKSS.2006.13.2.219
When the correlation of two random variables is weak, the value of one variable can not be used effectively to predict the other. Even when most of the values are overlapped, it is difficult to find a linear relationship. In this paper, we propose two graphical methods of representing the measures of correlation and independence between two random variables. The first method is used to represent their degree of correlation, and the other is used to represent their independence. Both of these methods are based on the cumulative distribution functions defined in this work.
LMS and LTS-type Alternatives to Classical Principal Component Analysis
Huh, Myung-Hoe ; Lee, Yong-Goo ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 233~241
DOI : 10.5351/CKSS.2006.13.2.233
Classical principal component analysis (PCA) can be formulated as finding the linear subspace that best accommodates multidimensional data points in the sense that the sum of squared residual distances is minimized. As alternatives to such LS (least squares) fitting approach, we produce LMS (least median of squares) and LTS (least trimmed squares)-type PCA by minimizing the median of squared residual distances and the trimmed sum of squares, in a similar fashion to Rousseeuw (1984)'s alternative approaches to LS linear regression. Proposed methods adopt the data-driven optimization algorithm of Croux and Ruiz-Gazen (1996, 2005) that is conceptually simple and computationally practical. Numerical examples are given.
Relative Frequency of Order Statistics in Independent and Identically Distributed Random Vectors
Park, So-Ryoung ; Kwon, Hyoung-Moon ; Kim, Sun-Yong ; Song, Iick-Ho ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 243~254
DOI : 10.5351/CKSS.2006.13.2.243
The relative frequency of order statistics is investigated for independent and identically distributed (i.i.d.) random variables. Specifically, it is shown that the probability
is no less than the probability
at any point
denotes the r-th order statistic of an i.i.d. discrete random vector and
depends on the population probability distribution. A similar result for i.i.d. continuous random vectors is also presented.
On the Distribution and Its Properties of the Sum of a Normal and a Doubly Truncated Normal
Kim, Hea-Jung ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 255~266
DOI : 10.5351/CKSS.2006.13.2.255
This paper proposes a class of distributions which is useful in making inferences about the sum of values from a normal and a doubly truncated normal distribution. It is seen that the class is associated with the conditional distributions of truncated bivariate normal. The salient features of the class are mathematical tractability and strict inclusion of the normal and the skew-normal laws. Further it includes a shape parameter, to some extent, controls the index of skewness so that the class of distributions will prove useful in other contexts. Necessary theories involved in deriving the class of distributions are provided and some properties of the class are also studied.
A Role of Local Influence in Selecting Regressors
Kim, Myung-Geun ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 267~272
DOI : 10.5351/CKSS.2006.13.2.267
A procedure for selecting regressors in the linear regression model is suggested using local influence approach. Under an appropriate perturbation scheme, the effect of perturbation of regressors on the profile log-likelihood displacement is assessed for variable selection. A numerical example is provided for illustration.
Monotone Local Linear Quasi-Likelihood Response Curve Estimates
Park, Dong-Ryeon ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 273~283
DOI : 10.5351/CKSS.2006.13.2.273
In bioassay, the response curve is usually assumed monotone increasing, but its exact form is unknown, so it is very difficult to select the proper functional form for the parametric model. Therefore, we should probably use the nonparametric regression model rather than the parametric model unless we have at least the partial information about the true response curve. However, it is well known that the nonparametric regression estimate is not necessarily monotone. Therefore the monotonizing transformation technique is of course required. In this paper, we compare the finite sample properties of the monotone transformation methods which can be applied to the local linear quasi-likelihood response curve estimate.
A Bayesian Meta Analysis for Assessing Bioequivalence among Two Generic Copies of the Same Brand-Name Drug
Oh, Hyun-Sook ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 285~295
DOI : 10.5351/CKSS.2006.13.2.285
As more generic drugs become available, the quality, safety, and efficacy of generic drugs have become a public concern. Specifically, drug interchangeability among generic copies of the same brand-name drug is a safety concern. This research proposes a Bayesian method for assessing bioequivalence between two generic copies of the same brand-name drug from two independent
crossover design experiments. Uninformative priors are considered for general use and the posterior distribution of the difference of two generic drug effects is derived from which the highest probability density interval can be evaluated. Examples are presented for illustration.
Mutual Information and Redundancy for Categorical Data
Hong, Chong-Sun ; Kim, Beom-Jun ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 297~307
DOI : 10.5351/CKSS.2006.13.2.297
Most methods for describing the relationship among random variables require specific probability distributions and some assumptions of random variables. The mutual information based on the entropy to measure the dependency among random variables does not need any specific assumptions. And the redundancy which is a analogous version of the mutual information was also proposed. In this paper, the redundancy and mutual information are explored to multi-dimensional categorical data. It is found that the redundancy for categorical data could be expressed as the function of the generalized likelihood ratio statistic under several kinds of independent log-linear models, so that the redundancy could also be used to analyze contingency tables. Whereas the generalized likelihood ratio statistic to test the goodness-of-fit of the log-linear models is sensitive to the sample size, the redundancy for categorical data does not depend on sample size but its cell probabilities itself.
Count Five Statistics Using Trimmed Mean
Hong, Chong-Sun ; Jun, Jae-Woon ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 309~318
DOI : 10.5351/CKSS.2006.13.2.309
There are many statistical methods of testing the equality of two population variances. Among them, the well-known F test is very sensitive to the normality assumption. Several other tests that do not assume normality have been proposed, but these tests usually need tables of critical values or software for hypotheses testing. McGrath and Yeh (2005) suggested a quick and compact Count Five test requiring only the calculation of the number of extreme points. Since the Count Five test uses only extreme values, this discards some information from the samples, often resulting in a degradation in power. In this paper, an alternative Count Five test using the trimmed mean is proposed and its properties are discussed for some distributions and normal mixtures.
Filtered Randomized Response Technique
Choi, Kyoung-Ho ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 319~326
DOI : 10.5351/CKSS.2006.13.2.319
Randomized response technique is a survey technique for eliminating evasive answer bias. This technique is popular in social survey for sensitive issues. In this paper we present a simple and obvious procedure for estimating the population proportion of a sensitive group. Here, we shows the weak point in the method of Kim and Warde (2005). Also, it is found that the proposed procedure is more efficient than the ones of Warner (1965) and Kim and Warde (2005). Lastly we discuss the conditions that the suggested method will be more efficienct.
Allocation in Multi-way Stratification by Linear Programing
NamKung, Pyong ; Choi, Jae-Hyuk ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 327~341
DOI : 10.5351/CKSS.2006.13.2.327
Winkler (1990, 2001), Sitter and Skinner (1994), Wilson and Sitter (2002) present a method which applies linear programing to designing surveys with multi-way stratification, primarily in situation where the desired sample size is less than or only slightly larger than the total number of stratification cells. A comparison is made with existing methods both by illustrating the sampling schemes generated for specific examples, by evaluating sample mean, variance estimation, and mean squared errors, and by simulating sample mean for all methods. The computations required can, however, increase rapidly as the number of cells in the multi-way classification increase. In this article their approach is applied to multi-way stratification using real data.
Bootstrap Confidence Intervals for the INAR(p) Process
Kim, Hee-Young ; Park, You-Sung ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 343~358
DOI : 10.5351/CKSS.2006.13.2.343
The distributional properties of forecasts in an integer-valued time series model have not been discovered yet mainly because of the complexity arising from the binomial thinning operator. We propose two bootstrap methods to obtain nonparametric prediction intervals for an integer-valued autoregressive model : one accommodates the variation of estimating parameters and the other does not. Contrary to the results of the continuous ARMA model, we show that the latter is better than the former in forecasting the future values of the integer-valued autoregressive model.
Rank Scores for Linear Models under Asymmetric Distributions
Choi, Young-Hun ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 359~368
DOI : 10.5351/CKSS.2006.13.2.359
In this paper we derived the asymptotic relative efficiency, ARE(ms, rs), of our new score function with respect to the McKean and Sievers scores for the asymmetric error distributions which often occur in practice. We thoroughly explored the asymptotic relative efficiency, ARE(ms, rs), of our score function that provides much improvement over the McKean and Sievers scores for all values of r and s under asymmetric distributions.
Binary Forecast of Heavy Snow Using Statistical Models
Sohn, Keon-Tae ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 369~378
DOI : 10.5351/CKSS.2006.13.2.369
This Study focuses on the binary forecast of occurrence of heavy snow in Honam area based on the MOS(model output statistic) method. For our study daily amount of snow cover at 17 stations during the cold season (November to March) in 2001 to 2005 and Corresponding 45 RDAPS outputs are used. Logistic regression model and neural networks are applied to predict the probability of occurrence of Heavy snow. Based on the distribution of estimated probabilities, optimal thresholds are determined via true shill score. According to the results of comparison the logistic regression model is recommended.
On Convex Combination of Local Constant Regression
Mun, Jung-Won ; Kim, Choong-Rak ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 379~387
DOI : 10.5351/CKSS.2006.13.2.379
Local polynomial regression is widely used because of good properties such as such as the adaptation to various types of designs, the absence of boundary effects and minimax efficiency Choi and Hall (1998) proposed an estimator of regression function using a convex combination idea. They showed that a convex combination of three local linear estimators produces an estimator which has the same order of bias as a local cubic smoother. In this paper we suggest another estimator of regression function based on a convex combination of five local constant estimates. It turned out that this estimator has the same order of bias as a local cubic smoother.
Performance Analysis of VaR and ES Based on Extreme Value Theory
Yeo, Sung-Chil ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 389~407
DOI : 10.5351/CKSS.2006.13.2.389
Extreme value theory has been used widely in many areas of science and engineering to deal with the assessment of extreme events which are rare but have catastrophic consequences. The potential of extreme value theory has only been recognized recently in finance area. In this paper, we provide an overview of extreme value theory for estimating and assessing value at risk and expected shortfall which are the methods for modelling and measuring the extreme financial risks. We illustrate that the approach based on extreme value theory is very useful for estimating tail related risk measures through backtesting of an empirical data.
Nonparametric Tests for Grouped K-Sample Problem
Park, Hyo-Il ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 409~418
DOI : 10.5351/CKSS.2006.13.2.409
We propose a nonparametric test procedure for the K-sample problem with grouped data. We construct the test statistics using the scores derived for the linear model based on likelihood ratio principle and obtain asymptotic distribution. Also we illustrate our procedure with an example. Finally we discuss some concluding remarks.
Modified Sign Test Using Reverse Ranked Ordering-Set Samples
Kim, Hyun-Gee ; Kim, Dong-Hee ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 419~428
DOI : 10.5351/CKSS.2006.13.2.419
The method of Reverse Ranked Ordering-Set Sampling(RROSS) as an opposed Ranked Ordering-Set Sampling(ROSS) and Ranked-Set Sampling(RSS) is discussed. We propose the test statistic using sign test on RROSS. This method is effective when observations are expensive and measurement is perhaps destructive or invasive. This method obtains more informations than ROSS and RSS. The asymptotic relative efficiencies of RROSS with respect to ROSS and RSS are always greater than 1 for all sample sizes. We consider a simple model to describe the effect of imperfect judgment errors.
Distribution of a Sum of Weighted Noncentral Chi-Square Variables
Heo, Sun-Yeong ; Chang, Duk-Joon ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 429~440
DOI : 10.5351/CKSS.2006.13.2.429
In statistical computing, it is often for researchers to need the distribution of a weighted sum of noncentral chi-square variables. In this case, it is very limited to know its exact distribution. There are many works to contribute to this topic, e.g. Imhof (1961) and Solomon-Stephens (1977). Imhof's method gives good approximation to the true distribution, but it is not easy to apply even though we consider the development of computer technology Solomon-Stephens's three moment chi-square approximation is relatively easy and accurate to apply. However, they skipped many details, and their simulation is limited to a weighed sum of central chi-square random variables. This paper gives details on Solomon-Stephens's method. We also extend their simulation to the weighted sum of non-central chi-square distribution. We evaluated approximated powers for homogeneous test and compared them with the true powers. Solomon-Stephens's method shows very good approximation for the case.
Information Loss from Type I versus Type II Censoring
Lim, Jo-Han ; Song, Hyun-Seok ; Lee, Sung-Im ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 441~448
DOI : 10.5351/CKSS.2006.13.2.441
If the completely observed data are assumed to have full information, the censoring causes the loss of information. Previous studies have introduced the indices of information loss via measuring relative changes between the data with censoring and without censoring. In this paper, the comparisons are made for the information loss between type I and type II censoring in two sample problems.
Pruning the Boosting Ensemble of Decision Trees
Yoon, Young-Joo ; Song, Moon-Sup ;
Communications for Statistical Applications and Methods, volume 13, issue 2, 2006, Pages 449~466
DOI : 10.5351/CKSS.2006.13.2.449
We propose to use variable selection methods based on penalized regression for pruning decision tree ensembles. Pruning methods based on LASSO and SCAD are compared with the cluster pruning method. Comparative studies are performed on some artificial datasets and real datasets. According to the results of comparative studies, the proposed methods based on penalized regression reduce the size of boosting ensembles without decreasing accuracy significantly and have better performance than the cluster pruning method. In terms of classification noise, the proposed pruning methods can mitigate the weakness of AdaBoost to some degree.