• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.670-679
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• 2003

This paper deals with application of wavelet transform, which is known to be good for time-frequency analysis, in order to detect the underwater transient signals embedded in ambient noise. A new detector of acoustic transient signals is presented. It combines two detection tools: wavelet analysis and higher-order statistics. Using both techniques, the detection of the transient signal is possible in low signal to noise ratio condition. The proposed algorithm uses the wavelet transform of a partition of the signal on frequency domain, and then higher-order statistics tests the Gaussian nature of the segments.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.485-493
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• 2011

The proportional likelihood ratio order is an extension of the likelihood ratio order for the non-negative absolutely continuous random variables. In addition, the Lindley distribution has been over looked as a mixture of two exponential distributions due to the popularity of the exponential distribution. In this paper, we first recalled the above concepts and then obtained various properties of the Lindley distribution due to the proportional likelihood ratio order. These results are more general than the likelihood ratio ordering aspects related to this distribution. Finally, we discussed the proportional likelihood ratio ordering in view of the weighted version of the Lindley distribution.

• Wind and Structures
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• v.34 no.6
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• pp.469-482
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• 2022

The generalized extreme value distribution (GEVD) is frequently used to fit the block maximum of environmental parameters such as the annual maximum wind speed. There are several methods for estimating the parameters of the GEV distribution, including the least-squares method (LSM). However, the application of the LSM with the expected order statistics has not been reported. This study fills this gap by proposing a fitting method based on the expected order statistics. The study also proposes a plotting position to approximate the expected order statistics; the proposed plotting position depends on the distribution shape parameter. The use of this approximation for distribution fitting is carried out. Simulation analysis results indicate that the developed fitting procedure based on the expected order statistics or its approximation for GEVD is effective for estimating the distribution parameters and quantiles. The values of the probability plotting correlation coefficient that may be used to test the distributional hypothesis are calculated and presented. The developed fitting method is applied to extreme thunderstorm and non-thunderstorm winds for several major cities in Canada. Also, the implication of using the GEVD and Gumbel distribution to model the extreme wind speed on the structural reliability is presented and elaborated.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.879-887
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• 2019

We construct a procedure to test the stochastic order of two samples of interval-valued data. We propose a test statistic that belongs to a U-statistic and derive its asymptotic distribution under the null hypothesis. We compare the performance of the newly proposed method with the existing one-sided bivariate Kolmogorov-Smirnov test using real data and simulated data.

• Journal of the Korean Mathematical Society
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• v.54 no.2
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• pp.441-460
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• 2017

In this paper, we establish some conditional versions of the first part of the Borel-Cantelli lemma. As its applications, we study strong limit results of $\mathfrak{F}$-independent random variables sequences, the convergence of sums of $\mathfrak{F}$-independent random variables and the conditional version of strong limit results of the concomitants of order statistics.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.139-155
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• 2000

An exclusive simulation study is conducted in computing means for order statistics in standard normal variate. Monte Carlo moments are used in Shapiro-Francia W' statistic computation. Finally, quantiles for Shapiro-Francia W' are generated. The study shows that in computing means for order statistics in standard normal variate, complicated distributions and intensive numerical integrations can be avoided by using Monte Carlo simulation. Lack of accuracy is minimal and computation simplicity is noteworthy.

• International Journal of Reliability and Applications
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• v.8 no.1
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• pp.41-51
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• 2007

In this paper, we use the moments of order statistics derived by Lieblein (1955) to develop the correlation goodness-of-fit test for the Rayleigh distribution. In such we simulate the percentage points of the test statistics for the one-parameter and two-parameter cases. In addition, we calculate the power of the proposed tests based on some alterative distributions. Finally, we apply the procedures developed in the paper to some real data.

• Bulletin of the Korean Mathematical Society
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• v.50 no.5
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• pp.1639-1650
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• 2013

In this paper, a class of set-valued mappings is introduced, which is called uniformly same-order. For this sort of mappings, some minimax problems, in which the minimization and the maximization of set-valued mappings are taken in the sense of vector optimization, are investigated without any hypotheses of convexity.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.637-642
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• 2013

In this paper, we consider the maximum entropy test in in nite order autoregressiv models. Its asymptotic distribution is derived under the null hypothesis. A bootstrap version of the test is discussed and its performance is evaluated through Monte Carlo simulations.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.41-44
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• 2002

Blind source separation (BSS) is a fundamental problem that is encountered in many practical applications. In most existing methods, stationary sources are considered higher-order statistics is necessary either explicitly or implicitly. But, many natural signals are nonstationary, and it is possible to perform BSS using only second-order statistics. Our method is based on only second order statistics. The algorithms are developed using the gradient descent method in orthogonality constraint and their performance is confirmed by numerical experiments.