• Journal of the Korean Statistical Society
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• v.26 no.2
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• pp.223-229
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• 1997

It is demonstrated that many standard nonparametric test such as the Mann-Whitney-Wilcoxon test, the Fisher-Yates test, the Savage test and the median test are admissible for a two-sample nonparametric testing problem. The admissibility of the Kruskal-Wallis test is demonstrated for a nonparametric one-way layout testing problem.

• Proceedings of the Safety Management and Science Conference
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• 2008.04a
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• pp.451-460
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• 2008

This paper reviews nonparametric statistics by Neyman-Pearson test and Fisher test. Nonparametric statistics deal with the small sample with distribution-free assumption in multi-product and small-volume production. Two tests for various nonparametric statistic methods such as sign test, Wilcoxon test, Mann-Whitney test, Kruskal-Wallis test, Mood test, Friedman test and run test are also presented with the steps for testing hypotheses and test of significance.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.485-493
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• 2004

This paper considers the test of money and income causality. Jeong (1991, 2003) developed a nonparametric causality test based on the kernel estimation method. We apply the nonparametric test to USA data of money and income. We also compare the test results with ones of the conventional parametric test.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.373-381
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• 2009

Evidence of linkage is expressed as a decreasing trend of the squared trait difference of two siblings with increasing identical by descent scores. In contrast to successes in the application of a parametric approach of Haseman-Elston regression, notably low powers are demonstrated in the nonparametric linkage analysis methods for complex traits and diseases with sib-pairs data. We report that the Genehunter nonparametric linkage statistic is biased and furthermore the variance formula that they used is an inflated one, and this is one reason for a low performance. Thus, we propose bias-corrected nonparametric linkage statistics. Simulation studies comparing our proposed nonparametric test statistics versus the existing test statistics suggest that the bias-corrected new nonparametric test statistics are more powerful and attains efficiencies close to that of Haseman-Elston regression.

• Communications for Statistical Applications and Methods
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• v.14 no.2
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• pp.443-457
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• 2007

This paper deals with the problem of testing the existence of change in mean and estimating the change-point using nonparametric bootstrap technique. A test statistic using Gombay and Horvath (1990)'s functional form is applied to derive a test statistic and nonparametric change-point estimator with bootstrapping idea. Achieved significance level of the test is calculated for the proposed test to show the evidence against the null hypothesis. MSE and percentiles of the bootstrap change-point estimators are given to show the distribution of the proposed estimator in simulation.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.811-816
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• 2000

The purpose of this paper is to study of data-driven smoothing goodness of it test, when the hypothesis is complete. The smoothing goodness of fit test statistic by nonparametric function estimation techniques is proposed in this paper. The results of simulation studies for he powers of show that the proposed test statistic compared well to other.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.409-418
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• 2006

We propose a nonparametric test procedure for the K-sample problem with grouped data. We construct the test statistics using the scores derived for the linear model based on likelihood ratio principle and obtain asymptotic distribution. Also we illustrate our procedure with an example. Finally we discuss some concluding remarks.

• Journal of applied mathematics & informatics
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• v.8 no.2
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• pp.669-682
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• 2001

In this paper a nonparametric test for the parallelism of k regression lines against ordered alternatives, when the independent variables are positive and all regression lines have a common intercept is proposed. The proposed test is based on a Jonckheere-type statistic applied to residuals. Under some conditions the proposed test statistic is asymptotically distribution-free. The small-sample powers of our test are compared with other tests by a Monte Carlo study. The simulation results show that the proposed test has significantly higher empirical powers than the other tests considered in this paper.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.227-244
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• 2023

In this paper, a depth-based nonparametric test for a multivariate two-sample scale problem is proposed. The proposed test statistic is based on the depth-induced ranks and is thus distribution-free. In this article, the depth values of data points of one sample are calculated with respect to the other sample or distribution and vice versa. A comprehensive simulation study is used to examine the performance of the proposed test for symmetric as well as skewed distributions. Comparison of the proposed test with the existing depth-based nonparametric tests is accomplished through empirical powers over different depth functions. The simulation study admits that the proposed test outperforms existing nonparametric depth-based tests for symmetric and skewed distributions. Finally, an actual life data set is used to demonstrate the applicability of the proposed test.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.411-416
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• 1998

A simple nonparametric test of complete or total independence is suggested for continuous multivariate distributions. This procedure first discretizes the original variables based on their order statistics, and then tests the hypothesis of complete independence for the resulting contingency table. Under the hypothesis of independence, the chi-squared test statistic has an asymptotic chi-squared distribution. We present a simulation study to illustrate the accuracy in finite samples of the limiting distribution of the test statistic. We compare our method to another nonparametric test of complete independence via a simulation study. Finally, we apply our method to the residuals from a real data set.