• The Korean Journal of Applied Statistics
• /
• v.3 no.1
• /
• pp.121-141
• /
• 1990

Computer intensive bootstrap methods are studied as a tool of statistics. Practical calculation and theoretical justification problem of the methods in estimating the sampling distribution and construction confidence region of parameters are discussed through several examples. Statistical meaning of the methods are also considered.

• Communications for Statistical Applications and Methods
• /
• v.13 no.1
• /
• pp.101-111
• /
• 2006

One of the most widely used method of handling missingness in microarray data is the kNN(k Nearest Neighborhood) method. Recently Li and Gui (2004) suggested, so called PCR(Partial Cox Regression) method which deals with censored survival times and microarray data efficiently via kNN imputation method. In this article, we try to show that the way to treat missingness eventually affects the further statistical analysis.

• Communications for Statistical Applications and Methods
• /
• v.8 no.1
• /
• pp.185-192
• /
• 2001

Using the Lorenz curve that is proved to be a powerful tool to measure the income inequality within a population of income receivers, we propose the normalized sample Lorenz curve for the goodness-of-fit test that is very important test in statistical analysis. For two hodgkin's disease data sets, we compare the Q-Q plot and the proposed normalized sample Lorenz curve.

• Communications for Statistical Applications and Methods
• /
• v.2 no.1
• /
• pp.74-88
• /
• 1995

This article introduces a unified method of choosing the most explanatory and significant multiway partitions for classification tree design and analysis. The method is derived on the impurity reduction (IR) measure of divergence, which is proposed to extend the proportional-reduction-in-error (PRE) measure in the decision-theory context. For the method derivation, the IR measure is analyzed to characterize its statistical properties which are used to consistently handle the subjects of feature formation, feature selection, and feature deletion required in the associated classification tree construction. A numerical example is considered to illustrate the proposed approach.

• Proceedings of the Safety Management and Science Conference
• /
• 2010.04a
• /
• pp.509-517
• /
• 2010

The paper proposes the misuse types of statistical quality tools according to the kind of data and the number of population in DMAIC process of six sigma. The result presented in this paper can be extended to the QC story 15 steps of QC circle. The study also provides the improvement methods about control chart, measurement system analysis, statistical difference, and practical equivalence.

• Journal of the Korea Safety Management & Science
• /
• v.7 no.1
• /
• pp.147-157
• /
• 2005

Recently, growth of internet causes rapid changes in many areas of statistics such as statistical computation and analysis. Especially, bootstrap is the most interesting statistical methods applying computer resampling simulation. In this paper, we try to present how to use a method of bootstrap on the internet. We also develop to user a statistical system which is programed with ASP for user to handle easily in manufacturing system.

• The Korean Journal of Applied Statistics
• /
• v.3 no.2
• /
• pp.17-25
• /
• 1990

We will compare several statistical package programs on aspects of exploratory data analysis(EDA). Specifically, PC versions of Minitab, NCSS, SAS, SPSS/PC+, Statgraphics, and SYSTAT are reviewed.

• Communications for Statistical Applications and Methods
• /
• v.6 no.3
• /
• pp.901-908
• /
• 1999

Using the Transformed Lorenz curve which is introduced by Cho et al.(1999) we propose the test statistic for testing of normality that is very important test in statistical analysis and compare the proposed test statistic with the Shapiro and Wilk's W test statistic in terms of the power of test through by Monte Carlo method.

• Journal of the Korean Statistical Society
• /
• v.25 no.1
• /
• pp.67-80
• /
• 1996

Factor analysis is a multivariate technique for describing the in-terrelationship among many variables in terms of a few underlying but unobservable random variables called factors. There are various approaches for this factor analysis. In particular, principal factor analysis is one of the most popular methods. This follows the mathematical algorithm of the principal component analysis based on the singular value decomposition. But it is known that the singular value decomposition is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, using the resistant singular value decomposition of Choi and Huh (1994), we derive a resistant principal factor analysis relatively little influenced by notable observations.

• Communications for Statistical Applications and Methods
• /
• v.29 no.1
• /
• pp.103-125
• /
• 2022

In this paper, we analyze the time series data of the case and death counts of COVID-19 that broke out in China in December, 2019. The study period is during the lockdown of Wuhan. We exploit functional data analysis methods to analyze the collected time series data. The analysis is divided into three parts. First, the functional principal component analysis is conducted to investigate the modes of variation. Second, we carry out the functional canonical correlation analysis to explore the relationship between confirmed and death cases. Finally, we utilize a clustering method based on the Expectation-Maximization (EM) algorithm to run the cluster analysis on the counts of confirmed cases, where the number of clusters is determined via a cross-validation approach. Besides, we compare the clustering results with some migration data available to the public.