• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.9
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• pp.852-860
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• 2014

This paper proposes an efficient algorithm based on Kolmogorov-Smirnov test to determine the presence of impulsive noise in the given environment. Kolmogorov-Smirnov and Chi-Square tests are known in the literature to serve as a goodness-of-fit test especially for a testing for normality of the distribution. But these algorithms are difficult to implement in practice due to high complexity. The proposed algorithm gives a significant reduction of the computational complexity while decreasing the error probability of hypothesis test, which is shown in the simulation results. Also, it is worth noting that the proposed algorithm is not dependent on the noise environment.

• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.123-131
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• 1996

The problerm of testing for a constant mean is considered. A Kolmogorov-Smirnov type test using the sample Fourier coefficients is suggested and its asymptotic distribution is derived. A simulation study shows that the proposed test is more powerful than the cusum type test when there is more than one change-point or there is a cyclic change.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.129-137
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• 2002

Two-sample problem is frequently discussed problem in statistics. In this paper we consider the hypothese methods for the general two-sample problem and suggest the bootstrap methods. And we show that the modified Kolmogorov-Smirnov test is more efficient than the Kolmogorov-Smirnov test.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.515-523
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• 2000

In this paper we detect edges using stutistical methods of the change-point problem. For this, we perform the hypothesis testing for differences in gray levels to see whether any $n\timesn$ subimage contains edge segments. The proposed method based on the twosample Kolmogorov-Smirnov test is introduced and the likelihood ratio test and the \VolfeSchechtman test for change-point problem arc also applied for edge detection. \Ve perform the experimental study to assess the performance of these methods in both noisy and uncontaminated sample noises.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1065-1075
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• 2008

For the model validation of credit rating models, Kolmogorov-Smirnov(K-S) statistic has been widely used as a testing method of discriminatory power from the probabilities of default for default and non-default. For the credit rating works, K-S statistics are to test two identical distribution functions which are partitioned from a distribution. In this paper under the assumption that the distribution is known, modified K-S statistic which is formulated by using known distributions is proposed and compared K-S statistic.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.311-319
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• 2012

The simplest and the most important distribution in survival analysis is exponential distribution. Koziol and Green (1976) derived Cram$\acute{e}$r-von Mises statistic's randomly censored version based on the Kaplan-Meier product limit estimate of the distribution function; however, it could not be practical for a real data set since the statistic is for testing a simple goodness of fit hypothesis. We generalized it to the composite hypothesis for exponentiality with an unknown scale parameter. We also considered the classical Kolmogorov-Smirnov statistic and generalized it by the exact same way. The two statistics are compared through a simulation study. As a result, we can see that the generalized Koziol-Green statistic has better power in most of the alternative distributions considered.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.469-478
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• 1999

We propose a Kolmogorov-Smirnov-type test for independence of paired failure times in the presence of independent censoring times. This independent censoring mechanism is often assumed in case-control studies. To do this end, we first introduce a process defined as the difference between the bivariate survival function estimator proposed by Wang and Wells (1997) and the product of the product-limit estimators (Kaplan and Meier (1958)) for the marginal survival functions. Then, we derive its asymptotic properties under the null hypothesis of independence. Finally, we assess the performance of the proposed test by simulations, and illustrate the proposed methodology with a dataset for remission times of 21 pairs of leukemia patients taken from Oakes(1982).

• Journal of Korean Society for Quality Management
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• v.14 no.1
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• pp.39-46
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• 1986

This paper considers two sided tests for exponential null distribution against NBUE or NWUE alternative in life testing. The main results concern the strong consistency of two proposed statistics, one being similar to Kolmogorov - Smirnov statistic, the other similar to Cramer-Von Mises statistic. Also obtained are the asymtotic null distribution and the exact Bahadur slope of the statistic similar to Kolmogorov-Smirnov.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.537-550
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• 2006

In this paper, we develope three modified empirical distribution function type tests, the modified Cramer-von Mises test, the modified Anderson-Darling test, and the modified Kolmogorov-Smirnov test for the two-parameter exponential distribution with unknown parameters based on multiply Type-II censored samples. For each test, Monte Carlo techniques are used to generate the critical values. The powers of these tests are also investigated under several alternative distributions.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.519-531
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• 2017

We consider goodness-of-fit test statistics for Weibull distributions when data are randomly censored and the parameters are unknown. Koziol and Green (Biometrika, 63, 465-474, 1976) proposed the $Cram\acute{e}r$-von Mises statistic's randomly censored version for a simple hypothesis based on the Kaplan-Meier product limit of the distribution function. We apply their idea to the other statistics based on the empirical distribution function such as the Kolmogorov-Smirnov and Liao and Shimokawa (Journal of Statistical Computation and Simulation, 64, 23-48, 1999) statistics. The latter is a hybrid of the Kolmogorov-Smirnov, $Cram\acute{e}r$-von Mises, and Anderson-Darling statistics. These statistics as well as the Koziol-Green statistic are considered as test statistics for randomly censored Weibull distributions with estimated parameters. The null distributions depend on the estimation method since the test statistics are not distribution free when the parameters are estimated. Maximum likelihood estimation and the graphical plotting method with the least squares are considered for parameter estimation. A simulation study enables the Liao-Shimokawa statistic to show a relatively high power in many alternatives; however, the null distribution heavily depends on the parameter estimation. Meanwhile, the Koziol-Green statistic provides moderate power and the null distribution does not significantly change upon the parameter estimation.