• Journal of the Korean Statistical Society
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• v.14 no.1
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• pp.39-47
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• 1985

In this paper, we propose some nonparametric subset selection procedures for scale parameters based on rank-likes. The proposed procedures are compared to the Gupta-Sobel's parametric prcedure through a small-sample Monte Carlo study. The results show that the nonparametric procedures are quite robust for heavy-tailed distributions, but they have somewhat low efficiencies.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.95-113
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• 2006

The data resulting from a deterministic dynamic system may often appear to be random. However, it is important to distinguish a deterministic and a random processes for statistical analysis. In this paper, we propose a nonparametric test procedure to distinguish a noisy chaos from i.i.d. random process. The proposed procedure can be easily implemented by computer. We notice that the test is very effective to identify a low dimensional chaos process in some cases.

• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.401-408
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• 1993

This paper suggests a nonparametric test for the parallelism of several regression lines against ordered alternatives. The test statistic is an extension of the Potthoff statistic. The asymptotic variance of the proposed statistic is estimated by Bootstrap method. The proposed test are compared with the Adichie's parametric and nonparametric tests.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.597-607
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• 2004

The primary interest of drug development studies is identifying the lowest dose level producing a desirable effect over that of the zero-dose control, which is referred as the minimum effective dose (MED). In this paper, we suggest a nonparametric procedure for identifying the MED with binary or ordered categorical response data. Proposed test and Williams' test are compared by Monte Carlo simulation study and discussed.

• Journal of the military operations research society of Korea
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• v.27 no.2
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• pp.124-133
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• 2001

The purpose of this study is investigate the differences among parametric and nonparametric test statistics for the tree alternative hypothesis in the randomized block design. As the results, it was found that there was no large differences among parametric and nonparametric test statistics in power when the block sizes were larger, and Hollander's statistic had better power than other nonparametric test statistics. It is recommended that Hollander's test statistic is more useful method when we have no information about the distribution of population.

• ETRI Journal
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• v.43 no.1
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• pp.74-81
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• 2021

The mixture model is a very powerful and flexible tool in clustering analysis. Based on the Dirichlet process and parsimonious Gaussian distribution, we propose a new nonparametric mixture framework for solving challenging clustering problems. Meanwhile, the inference of the model depends on the efficient online variational Bayesian approach, which enhances the information exchange between the whole and the part to a certain extent and applies to scalable datasets. The experiments on the scene database indicate that the novel clustering framework, when combined with a convolutional neural network for feature extraction, has meaningful advantages over other models.

• Journal of the Korean Mathematical Society
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• v.58 no.2
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• pp.327-349
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• 2021

In this paper, we main study the strong law of large numbers and complete convergence for weighted sums of asymptotically almost negatively associated (AANA, in short) random variables, by using the Marcinkiewicz-Zygmund type moment inequality and Roenthal type moment inequality for AANA random variables. As an application, the complete consistency for the weighted linear estimator of nonparametric regression models based on AANA errors is obtained. Finally, some numerical simulations are carried out to verify the validity of our theoretical result.

• The Korean Journal of Applied Statistics
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• v.1 no.2
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• pp.66-81
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• 1987

A new nonparametric sequential testing procedure is proposed in the paper. Sequential observations are divided into equally sized groups and a nonparametric statistic, which is appropriate for testing the given hypotheses, is obtained from each group. Then Wald's sequential test is applied for the case where the log probability ratio statistic is replaced by the nonparametric statistic. The properties of such test are evaluated approximately by the Wiener process.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.77-84
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• 2012

Mack and Skillings (1980) proposed a typical nonparametric method in a randomized block design with replications. However, this method may lose information because of the use of average observations instead of individual observations. In this paper, we proposed a nonparametric method that employed an aligned method suggested by Hodges and Lehmann (1962) under a randomized block design with replications. In addition, the comparative results of a Monte Carlo power study are presented.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.193-211
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• 2012

This paper deals with robust nonparametric regression analysis when the regressors are functional random fields. More precisely, we consider $Z_i=(X_i,Y_i)$, $i{\in}\mathbb{N}^N$ be a $\mathcal{F}{\times}\mathbb{R}$-valued measurable strictly stationary spatial process, where $\mathcal{F}$ is a semi-metric space and we study the spatial interaction of $X_i$ and $Y_i$ via the robust estimation for the regression function. We propose a family of robust nonparametric estimators for regression function based on the kernel method. The main result of this work is the establishment of the asymptotic normality of these estimators, under some general mixing and small ball probability conditions.