• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.539-556
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• 2019

This paper derive a method to solve change point regression problems via a process for obtaining consequential results using properties of a difference-based intercept estimator first introduced by Park and Kim (Communications in Statistics - Theory Methods, 2019) for outlier detection in multiple linear regression models. We describe the statistical properties of the difference-based regression model in a piecewise simple linear regression model and then propose an efficient algorithm for change point detection. We illustrate the merits of our proposed method in the light of comparison with several existing methods under simulation studies and real data analysis. This methodology is quite valuable, "no matter what regression lines" and "no matter what the number of change points".

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.891-908
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• 2010

In Korea, the small area estimation method is currently unpopular in generating o cial statistics. Because it may be difficult to determine the reliability for small area estimation, although small area estimation ha a sufficiently good advantage to generate small area statistics for Korea. This paper inspects the method of making small area unemployment through the small area estimation method. To estimate small area unemployment we used an EBLUP-type estimator based on a logistic linear mixed model. To evaluate the EBLUP-type estimator we accomplished the real data analysis and simulation experiment from the population and housing census data. In addition, small area estimates are compared to large sample survey estimates. We found the provided method in this paper is highly recommendable to generate small area unemployment as the official statistics.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.871-881
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• 2011

In this paper we propose a logistic regression method to estimate the survival function and the median survival time in interval-censored data. The proposed method is motivated by the data augmentation technique with no sacrifice in augmenting data. In addition, we develop a cross validation criterion to determine the size of data augmentation. We compare the proposed estimator with other existing methods such as the parametric method, the single point imputation method, and the nonparametric maximum likelihood estimator through extensive numerical studies to show that the proposed estimator performs better than others in the sense of the mean squared error. An illustrative example based on a real data set is given.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.127-134
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• 1997

A method is proposed for the choice of the number of principal components in principal component regression based on the predicted error sum of squares. To do this, we approximately evaluate that statistic using a linear approximation based on the perturbation expansion. In this paper, we apply the proposed method to various data sets and discuss some properties in choosing the number of principal components in principal component regression.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.519-531
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• 2017

We consider goodness-of-fit test statistics for Weibull distributions when data are randomly censored and the parameters are unknown. Koziol and Green (Biometrika, 63, 465-474, 1976) proposed the $Cram\acute{e}r$-von Mises statistic's randomly censored version for a simple hypothesis based on the Kaplan-Meier product limit of the distribution function. We apply their idea to the other statistics based on the empirical distribution function such as the Kolmogorov-Smirnov and Liao and Shimokawa (Journal of Statistical Computation and Simulation, 64, 23-48, 1999) statistics. The latter is a hybrid of the Kolmogorov-Smirnov, $Cram\acute{e}r$-von Mises, and Anderson-Darling statistics. These statistics as well as the Koziol-Green statistic are considered as test statistics for randomly censored Weibull distributions with estimated parameters. The null distributions depend on the estimation method since the test statistics are not distribution free when the parameters are estimated. Maximum likelihood estimation and the graphical plotting method with the least squares are considered for parameter estimation. A simulation study enables the Liao-Shimokawa statistic to show a relatively high power in many alternatives; however, the null distribution heavily depends on the parameter estimation. Meanwhile, the Koziol-Green statistic provides moderate power and the null distribution does not significantly change upon the parameter estimation.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1163-1170
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• 2015

Various computational methods for obtaining volatilities for financial time series are reviewed and compared with each other. We reviewed model based GARCH approach as well as the data based method which can essentially be regarded as a smoothing technique applied to the squared data. The method for high frequency data is focused to obtain the realized volatility. A hybrid method is suggested by combining the model based GARCH and the historical volatility which is a data based method. Korea stock prices are analysed to illustrate various computational methods for volatilities.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.759-765
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• 2010

In this paper, two new inverse regression methods are introduced. One is a distance based method, and the other is a likelihood based method. While a model is fitted by minimizing the sum of squared prediction errors of y's and x's in the classical and inverse methods, respectively. In the new distance based method, we simultaneously minimize the sum of both squared prediction errors. In the likelihood based method, we propose an inverse regression with Arnold-Beaver Skew Normal(ABSN) error distribution. Using the cross validation method with an asymmetric real data set, two new and two existing methods are studied based on the relative prediction bias(RBP) criteria.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.859-871
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• 2003

In this paper we introduce some adaptive M-estimators using selector statistics to estimate the slope of regression model under the symmetric and continuous underlying error distributions. This selector statistics is based on the residuals after the preliminary fit L$_1$ (least absolute estimator) and the idea of Hogg(1983) and Hogg et. al. (1988) who used averages of some order statistics to discriminate underlying symmetric distributions in the location model. If we use L$_1$ as a preliminary fit to get residuals, we find the asymptotic distribution of sample quantiles of residual are slightly different from that of sample quantiles in the location model. If we use the functions of sample quantiles of residuals as selector statistics, we find the suitable quantile points of residual based on maximizing the asymptotic distance index to discriminate distributions under consideration. In Monte Carlo study, this adaptive M-estimation method using selector statistics works pretty good in wide range of underlying error distributions.

• Journal of Korean Society for Quality Management
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• v.42 no.2
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• pp.209-219
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• 2014

Purpose: The purpose of this study is to develop the statistical test for process capability index $C_p$ based on mixture normal process. Methods: This study uses Bootstrap method to calculate the approximate P-value for various simulation conditions under mixture normal process. Results: This study indicates that our proposed method is effective way to test for process capability index $C_p$ based on mixture normal process. Conclusion: This study finds out that statistical test for process capability index $C_p$ based on mixture normal process is useful for real application.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1143-1152
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• 2009

A canonical correlation analysis(CCA)-based method is proposed for prediction of future climate change which combines information from ensembles of atmosphere-ocean general circulation models(AOGCMs) and observed climate values. This paper focuses on predictions of future climate on a regional scale which are of potential economic values. The proposed method is obtained by coupling the classical CCA with empirical orthogonal functions(EOF) for dimension reduction. Furthermore, we generate a distribution of climate responses, so that extreme events as well as a general feature such as long tails and unimodality can be revealed through the distribution. Results from real data examples demonstrate the promising empirical properties of the proposed approaches.