• 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.

• Journal of the Korean Statistical Society
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• v.32 no.4
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• pp.373-384
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• 2003

In this paper, we formulate multivariate one-sided alternatives and propose a class of nonparametric tests for possibly right censored data. We obtain the asymptotic tail probability (or p-value) by showing that our proposed test statistics have asymptotically multivariate normal distributions. Also, we illustrate our procedure with an example and compare it with other procedures in terms of empirical powers for the bivariate case. Finally, we discuss some properties of our test.

• 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.

• 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.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.693-701
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• 2002

When the lowest dose level compared with zero-dose control has significant difference in effect, it is referred as minimum effective dose (MED). In this paper, we discuss several nonparametric methods for finding MED using updated rank at each sequential test step in small sample size and suggest new nonparametric procedures based on placement. Monte Carlo Simulation is adapted to compare power and Familywise Error Rate(FWE) of the new procedures with those of discussed nonparametric tests for finding MED.

• Journal of Environmental Impact Assessment
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• v.4 no.2
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• pp.93-103
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• 1995

This study was carried out to suggest the best statistical test to analyze the trend in monthly water quality data. Traditional parametric tests such as t-test and regression analysis are based on the assumption that the underlying population has a normal distribution and regression analysis additionally assumes that residual errors are independent. Analyzing 9-years monthly COD data collected at Paldang in Han River, the underlying population was found to be neither normal nor independent. Therefore parametric tests are invalid for trend detection. Four Kinds of nonparametric statistical tests, such as Run Test, Daniel test, Mann-Kendall test, and Time Series Residual Analysis were applied to analyze the trend in the COD data, Daniel test and Mann-Kendall test indicated upward trend in COD data. The best nonparametric test was suggested to be Daniel test, which is simple in computation and easy to understand the intuitive meaning.

• 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.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.208-219
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• 2004

This paper considers a nonparametric estimation of lifetime distribution for ramp stress tests with stress bound under intermittent inspection. The test items are inspected only at specified time points an⊂1 so the collected observations are grouped data. Under the cumulative exposure model, two nonparametric estimation methods of estimating the lifetime distribution at use condition stress are proposed for the situation which the time transformation function relating stress to lifetime is a type of the inverse power law. Each of items is initially put on test under ramp stress and then survivors are put on test under constant stress, where all failures in the Inspection interval are assumed to occur at the midi)oint or the endpoint of that interval. Two proposed estimators of quantile from grouped data consisting of the number of items failed in each inspection interval are numerically compared with the maximum likelihood estimator(MLE) based on Weibull distribution.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.39-46
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• 1996

Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. In this paper we compare power of goodness-of-fit test statistics for testing the (null)parametric model versus the (alternative) nonparametric model.

• Journal of the Korea Safety Management & Science
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• v.10 no.3
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• pp.237-243
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• 2008

The present study proposes two normality tests using nonparametric rank measures for small sample such as modified normal probability paper(NPP) tests and modified Ryan-Joiner Test. This research also reviews various normality tests such as $X^2$ test, and Kullback-Leibler test. The proposed normality tests can be efficiently applied to the sparsity tests of factor effect or contrast using saturated design in $k^n$ factorial and fractional factorial design.