• Nuclear Engineering and Technology
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• v.53 no.5
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• pp.1664-1675
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• 2021

In order to verify the statistical performance of the nonparametric tests used in the MARSSIM approach, all plausible contamination distribution types that can be encountered in a survey area should be investigated. As the first of such investigations, this study aims to perform the verification for normal distribution of the contamination in a survey area by simulating the collection of random samples from it through the Monte Carlo simulation. The results of the simulations conducted for a total of 81 simulation cases showed that Sign test and WRS test both exhibited an excellent statistical performance: 100% for the former and 98.8% for the latter. Therefore, in final status surveys of the MARSSIM approach, a high statistical performance can be expected in applying the nonparametric hypothesis tests to survey areas whose net contamination can be assumed to be normally distributed.

• Communications for Statistical Applications and Methods
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• v.4 no.2
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• pp.427-434
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• 1997

The purpose of this paper is to discuss distribution-free tests of hypothesis that the random samples are identically distributed against the epidemic alternative. But most tests that have been considered are depended only on specific null distribution. Two nonparametric tests are considered and compared with a likelihood ratio test by the empirical powers.

• 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.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.483-497
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• 2001

When one fits a parametric density function to a data set, it is usually advisable to test the goodness of the postulated model. In this paper we study the nonparametric tests for testing the null hypothesis against general alternatives, when the null hypothesis specifies the density function up to unknown parameters. We modify the test statistic which was proposed by the first author and his colleagues. Asymptotic distribution of the modified statistic is derived and its performance is compared with some other tests through simulation.

• Proceedings of the Korean Reliability Society Conference
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• 2004.07a
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• pp.167-175
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• 2004

A nonparametric procedure is proposed to test the exponentiality against the hypothesis that one life distribution has a greater residual life times than the other life distribution. Such a hypothesis turns out to be equivalent to the one that one failure rate is greater than the other and so the proposed test works as a competitor to more IFR tests by Kochar (1979, 1981) and Cheng (1985). Our test statistic utilizes the U-statistics theory and a large sample nonpara metric test is established. The power of the proposed test is discussed by calculating the Pitman asymptotic relative efficiencies against several alter native hypotheses. A numerical example is presented to exemplify the proposed test.

• Journal of the Korean Statistical Society
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• v.19 no.2
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• pp.128-138
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• 1990

In this paper we study the properties of nonparametric tests for testing the null hypothesis of no changes against one sided and two sideds alternatives in scale parameter at unknown point. We first propose two types of nonparametric tests based on linear rank statistics and rank-like statistics, respectively. For these statistics, we drive the asymptotic distributions under the null and contiguous alternatives. The main theoreticla tools used for derivation are the stochastic process representation of the test staistic and the Brownian bridge approximation. We evaluate the Pitman efficiencies of the test for the contiguous alternatives, and also compute empirical power by Monte Carlo simulation.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.247-255
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• 2003

Hypothesis testing for the equality of two distributions were considered. Nonparametric kernel density estimates were used for testing equality of distributions. Cross-validatory choice of bandwidth was used in the kernel density estimation. Sampling distribution of considered test statistic were developed by resampling method, called the bootstrap. Small sample Monte Carlo simulation were conducted. Empirical power of considered tests were compared for variety distributions.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.291-296
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• 2010

In this paper, we consider a versatile test based on percentile tests. The versatile test may be useful when the underlying distributions are unknown or quite different types. We consider two kinds of combining functions for the percentile statistics, the quadratic and summing forms and obtain the limiting distributions under the null hypothesis. Then we illustrate our procedure with an example. Finally we discuss some interesting features of the test as concluding remarks.

• International Journal of Reliability and Applications
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• v.10 no.2
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• pp.81-88
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• 2009

A non-parametric procedure is presented for testing exponentially against used better than aged in convex ordering class (UBAC) of life distributions based on u-test. Convergence of the proposed statistic to the normal distribution is proved. Selected critical values are tabulated for sample sizes 5(5)40. The Pitman asymptotic relative efficiency of my proposed test to tests of other classes is studied. An example of 40 patients suffering from blood cancer disease demonstrates practical application of the proposed test.

• International Journal of Reliability and Applications
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• v.6 no.1
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• pp.53-64
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• 2005

In this paper, we propose a nonparametric test procedure for the multivariate, grouped and right censored data for two sample problem. For the construction of the test statistic, we use the linear rank statistics for each component and apply the permutation principle for obtaining the null distribution. For the large sample case, the asymptotic distribution is derived under the null hypothesis with the additional assumption that two censoring distributions are also equal. Finally, we illustrate our procedure with an example and discuss some concluding remarks. In appendices, we derive the expression of the covariance matrix and prove the asymptotic distribution.