• Journal of the Korean Statistical Society
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• v.29 no.2
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• pp.155-168
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• 2000

Regarding to multiple comparison problem (MCP) of k normal population variances, we suggest a Bayesian method for calculating posterior probabilities for various hypotheses of equality among population variances. This leads to a simple method for obtaining pairwise comparisons of variances in a statistical experiment with a partition on the parameter space induced by equality and inequality relationships among the variances. The method is derived from the fact that certain features of the hierarchical nonparametric family of Dirichlet process priors, in general, make it amenable to solving the MCP and estimating the posterior probabilities by means of posterior simulation, the Gibbs sampling. Two examples are illustrated for the method. For these examples, the method is straightforward for specifying distributionally and to implement computationally, with output readily adapted for required comparison.

• Journal of the Korean Statistical Society
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• v.17 no.2
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• pp.61-80
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• 1988

For testing homogeneity of scale parameters aginst ordered alternatives, some nonparametric test statistics based on pairwise ranking method are proposed. The proposed tests are distribution-free. The asymptotic distributions of the proposed statistcs are also investigated. It is shown that the Pitman efficiencies of the proposed rank tests are the same as those of the corresponding two-sample rank tests in the scale problem. A small-sample Monte Carlo study is also performed. The results show that the proposed tests are robust and efficient.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.179-186
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• 1996

Bioequivanence models arise typically in bioassays when new preparations are compared against standard ones by means of responses on some biological organisms. Relative potency measures provide nice interpretations for such bioequivalence and their estimation constitutes the prime interest of such studies. A conditional quantile process based on the k-nearest neighbor method is proposed for this purpose. An alternative procedure based on Kolmogrov-Smirnov type estimator has also been considered along with. ARIC ultrasound data are analyzed as examples.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.21-30
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• 2001

Suppose we observe a set of data (X$_1$,Y$_1$(, …, (X$_{n}$,Y$_{n}$) and use the Nadaraya-Watson regression estimator to estimate m(x)=E(Y│X=x). in this article bandwidth selection problem for the Nadaraya-Watson regression estimator is investigated. In particular cross validation method based on average square error(ASE) is considered. Theoretical results here include a central limit theorem that quantifies convergence rates of the bandwidth selector.tor.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.63-76
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• 2001

An adaptive estimation and testing method is proposed for comparing dispersions among several ordered groups. Based upon the large sampling theory for nonparametric quartile estimators, we derive the order restricted estimators and construct a simple test statistic. This test statistic has a mixture of several chi-square distributions as its asymptotic null distribution. The proposed test is illustratively applied to survival time data for the patients with carcinoma of the oropharynx.

• Journal of the Korean Statistical Society
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• v.31 no.2
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• pp.261-276
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• 2002

Probability density or its derivatives may have a discontinuity/change point at an unknown location. We propose a method of estimating the location and the jump size of the discontinuity point based on kernel type density or density derivatives estimators with one-sided equivalent kernels. The rates of convergence of the proposed estimators are derived, and the finite-sample performances of the methods are illustrated by simulated examples.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.707-722
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• 1998

This paper evaluates a new clinical trial designs for low infection rate disease. This type of sparse disease reaction makes the traditional two sample t-test or Wilcoxon rank-sum test inefficient compared to a new test suggested. The new test, which is based solely on the larger changes, is shown to be more effective than existing method by simulation for small samples. However, this test can be shown to be connected to the locally most powerful rank test under certain practical conditions. This design is motivated in testing the treatment effects in periodontal disease research.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.89-94
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• 2001

In this study we focus on variable selection in decision tree growing structure. Some of the splitting rules and variable selection algorithms are discussed. We propose a competitive variable selection method based on Kruskal-Wallis test, which is a nonparametric version of ANOVA F-test. Through a Monte Carlo study we note that CART has serious bias in variable selection towards categorical variables having many values, and also QUEST using F-test is not so powerful to select informative variables under heavy tailed distributions.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.891-901
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• 1997

The objective of this research is to investigate the problem of goodness of fit testing based on nonparametric density estimation with a data-driven smoothing parameter. The small and large smaple properties of the proposed test statistic $Z_{mn}$ are investigated with the minimizer $\widehat{m}$ of the estimated mean integrated squared error by the Diggle and Hall (1986) method.

• Membrane and Water Treatment
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• v.10 no.5
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• pp.339-352
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• 2019

Long term water quality change was analyzed to evaluate the effect of the Total Maximum Daily Load (TMDL) policy. A trend analysis was performed for biochemical oxygen demand (BOD) and total phosphorus (TP) concentrations data monitored at the outlets of the total 41 TMDL unit watersheds of the Nakdong River in the Republic of Korea. Because water quality data do not usually follow a normal distribution, a nonparametric statistical trend analysis method was used. The monthly mean values of BOD and TP for the period between 2004 and 2015 were analyzed by the seasonal Mann-Kendall test and the locally weighted scatterplot smoother (LOWESS). The TMDL policy effect on the water quality change of each unit watershed was analyzed together with the results of the trend analysis. From the seasonal Mann-Kendall test results, it was found that for BOD, 7.8 % of the 41 points showed downward trends, 26.8 % and the rest 65.9% showed upward and no trends. For TP, 51.2% showed no trends and the rest 48.8% showed downward trends. From the LOWESS analysis results, TP began to decrease in most of the unit watersheds from mid-2010s when intensive chemical treatment processes were introduced to existing wastewater treatment plants. Overall, for BOD, relatively more points were improved in the main stream compared to the points of the tributaries although overall trends were mostly no trend or upward. For TP, about half of the points were improved and the rest showed no trends.