• Communications for Statistical Applications and Methods
• /
• v.15 no.2
• /
• pp.229-234
• /
• 2008

A Cook-type distance is considered for investigating the influence of observations in constrained regression models. Its exact sampling distribution is derived, which is used for judging whether each observation is influential or not. A numerical example is provided for illustration.

• Journal of the Korean Statistical Society
• /
• v.12 no.1
• /
• pp.18-23
• /
• 1983

Dwivedi, Srivastava and Hall(1980) derived the first and second moments of generalized ridge estimators. In this paper we consider the $m^{th}$ moment of a generalized ridge estimator and tabulate tis skewness measure.

• Communications for Statistical Applications and Methods
• /
• v.4 no.3
• /
• pp.847-858
• /
• 1997

In this note we develop a partial ordering among positive lower orthant dependent distributions with fixed marginals. This permits us to measure the degree of positive lower orthant dependence. Some basic properties and preservation results are derived.

• Journal of the Korean Statistical Society
• /
• v.5 no.1
• /
• pp.19-28
• /
• 1976

In empirical study for fitting a multiple linear regression model for successive cross-sections data observed on the same set of independent variables over several time periods, one often faces the problem of poor $R^2$, the multiple coefficient of determination, which provides a standard measure of how good a specified regression line fits the sample data.

• Journal of the Korean Geographical Society
• /
• v.43 no.1
• /
• pp.134-153
• /
• 2008

The main purpose of the research is to illustrate the value of the spatial statistical approach to residential differentiation by providing a framework for exploratory spatial data analysis (ESDA) using a local spatial separation measure. ESDA aims, by utilizing a variety of statistical and cartographic visualization techniques, at seeking to detect patterns, to formulate hypotheses, and to assess statistical models for spatial data. The research is driven by a realization that ESDA based on local statistics has a great potential for substantive research. The main results are as follows. First, a local spatial separation measure is correspondingly derived from its global counterpart. Second, a set of significance testing methods based on both total and conditional randomization assumptions is provided for the local measure. Third, two mapping techniques, a 'spatial separation scatterplot map' and a 'spatial separation anomaly map', are devised for ESDA utilizing the local measure and the related significance tests. Fourth, a case study of residential differentiation between the highly educated and the least educated in major Korean metropolitan cities shows that the proposed ESDA techniques are beneficial in identifying bivariate spatial clusters and spatial outliers.

• The Korean Journal of Applied Statistics
• /
• v.23 no.2
• /
• pp.317-323
• /
• 2010

Measures are very useful tools for comparing the shape variability in statistical shape analysis. For examples, the Procrustes statistic(PS) is isolated measure, and the mean Procrustes statistic(MPS) and the root mean square measure(RMS) are overall measures. But these measures are very subjective, complicated and moreover these measures are not statistical for comparing the shape variability. Therefore we need to study some tests. It is well known that the Hotelling's $T^2$ test is used for testing shape variability of two independent samples. And for testing shape variabilities of several independent samples, instead of the Hotelling's $T^2$ test, one way analysis of variance(ANOVA) can be applied. In fact, this one way ANOVA is based on the balanced samples of equal size which is called as BANOVA. However, If we have unbalanced samples with unequal size, we can not use BANOVA. Therefore we propose the unbalanced analysis of variance(UNBANOVA) for testing shape variabilities of several independent samples of unequal size.

• International Journal of Quality Innovation
• /
• v.10 no.2
• /
• pp.97-108
• /
• 2009

This paper is to develop a quality measure to evaluate the quality level of child care service in the regional level. By utilizing the biannual intensive child care statistical reports, ten variables are integrated and summarized as a quality measure for child care service in regional level by employing Principal Component Analysis (PCA). Conclusively, it is possible to get a comprehensive measure and the measure obtained from data between 2003 and 2008 illustrates the difference in child care service quality among regions over years. With the measure developed by this research, each region can also get very good insight into what kinds of factors of child care service should be paid more attention to in order to improve the quality of its child care service. Moreover, the measure obtained in this paper is proven reliable and robust in that it reflects the quality of child care service in each region and gives us statistically uniform quality scores with a different data set.

• Proceedings of the Korean Statistical Society Conference
• /
• 2002.05a
• /
• pp.79-83
• /
• 2002

Since its introduction in 1995 by Schena et al. cDNA microarrays have been established as a potential tool for high-throughput analysis which allows the global monitoring of expression levels for thousands of genes simultaneously. One of the characteristics of the cDNA microarray data is that there is inherent noise even after the removal of systematic effects in the experiment. Therefore, replication is crucial to the microarray experiment. The assessment of reproducibility among replicates, however, has drawn little attention. Reproducibility may be assessed with several different endpoints along the process of data reduction of the microarray data. We define the reproducibility to be the degree with which replicate arrays duplicate each other. The aim of this note is to develop a novel measure of reproducibility among replicates in the cDNA microarray experiment based on the unprocessed data. Suppose we have p genes and n replicates in a microarray experiment. We first develop a measure of reproducibility between two replicates and generalize this concept for a measure of reproducibility of one replicate against the remaining n-1 replicates. We used the rank of the outcome variable and employed the concept of a measure of tracking in the blood pressure literature. We applied the reproducibility measure to two sets of microarray experiments in which one experiment was performed in a more homogeneous environment, resulting in validation of this novel method. The operational interpretation of this measure is clearer than Pearson's correlation coefficient which might be used as a crude measure of reproducibility of two replicates.

• Journal of KIISE:Information Networking
• /
• v.30 no.3
• /
• pp.397-406
• /
• 2003

As the information technology grows interests in the intrusion detection system (IDS), which detects unauthorized usage, misuse by a local user and modification of important data, has been raised. In the field of anomaly-based IDS several artificial intelligence techniques such as hidden Markov model (HMM), artificial neural network, statistical techniques and expert systems are used to model network rackets, system call audit data, etc. However, there are undetectable intrusion types for each measure and modeling method because each intrusion type makes anomalies at individual measure. To overcome this drawback of single-measure anomaly detector, this paper proposes a multiple-measure intrusion detection method. We measure normal behavior by systems calls, resource usage and file access events and build up profiles for normal behavior with hidden Markov model, statistical method and rule-base method, which are integrated with a rule-based approach. Experimental results with real data clearly demonstrate the effectiveness of the proposed method that has significantly low false-positive error rate against various types of intrusion.

• Journal of the Korean Statistical Society
• /
• v.35 no.2
• /
• pp.167-177
• /
• 2006

An important role of statistical analysis in science is interpreting observed data as evidence, that is 'what do the data say?'. Although standard statistical methods (hypothesis testing, estimation, confidence intervals) are routinely used for this purpose, the theory behind those methods contains no defined concept of evidence and no answer to the basic question 'when is it correct to say that a given body of data represent evidence supporting one statistical hypothesis against another?' (Royall, 1997). In this article, we use likelihood ratios to measure evidence provided by record values in favor of a hypothesis and against an alternative. This hypothesis is concerned on mean of an exponential model and prediction of future record values.