• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.213-228
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• 2008

Using a linear loss function, this paper considers the one-sided testing problem for the exponential distribution via the empirical Bayes(EB) approach. Based on right censored data, we propose an EB test for the exponential parameter and obtain its convergence rate and asymptotic optimality, firstly, under the condition that the censoring distribution is known and secondly, that it is unknown.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.291-297
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• 2001

For a stochastic regression model in which the errors are assumed to form a stationary linear process, we show that the difference between the empirical distribution functions of the errors and the estimates of those errors converges uniformly in probability to zero at the rate of $o_{p}$ ( $n^{-}$$\frac{1}{2}$) as the sample size n increases.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1
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• pp.69-77
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• 2003

Measurements of backscattered intensity were made over a shallow water using 300 ㎑and 1200 ㎑ bottom mounted ADCP (Acoustic Doppler Current Profiler) to determine the temporal variability of vertical distribution of high-frequency volume scattering strength (Sv). The variability of Sv in relatively deep water column(85 m and 113 m was due to the daily vertical migration, probably of larger zooplankton. However it was not found with 1200㎑ data at shallow water column. From the empirical orthogonal function (EOF) analysis using 1200㎑ data, the vertical distribution of the first mode eigenvectors of Sv is characterized by the presence of the maximum values near the bottom of the water.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1497-1501
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• 2013

Cumulative residual Kullback-Leibler (CRKL) information is well defined on the empirical distribution function (EDF) and allows us to construct a EDF-based goodness of t test statistic. However, we need to consider a scaled CRKL because CRKL is not scale invariant. In this paper, we consider several criterions for estimating the scale parameter in the scale CRKL and compare the performances of the estimated CRKL in terms of both power and unbiasedness.

• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.113-132
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• 1998

In this paper, we consider a minimum distance (M.D.) estimation based on kernels for U-statistics. We use Cramer-von Mises type distance function which measures the discrepancy between U-empirical distribution function(d.f.) and modeled d.f. of kernel. In the distance function, we allow various integrating measures, which can be finite, $\sigma$-finite or discrete. Then we derive the asymptotic normality and study the qualitative robustness of M. D. estimates.

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.245-255
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• 2011

There are two nonparametric methods that use empirical distribution functions and probability density estimators for the test of the distribution change of data. In this paper we investigate the two methods precisely and summarize the results of previous research. We assume several probability models to make a simulation study of the change point analysis and to examine the finite sample behavior of the two methods. Empirical powers are compared to verify which is better for each model.

• The Pure and Applied Mathematics
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• v.1 no.1
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• pp.19-24
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• 1994

Let P be a probability measure on the real line with Lebesque-density f. The usual estimator of the distribution function (≡df) of P for the sample $\chi$$_1$,…, $\chi$$\_$n/ is the empirical df: F$\_$n/(t)=(equation omitted). But this estimator does not take into account the smoothness of F, that is, the existence of a density f. Therefore, one should expect that an estimator which is better adapted to this situation beats the empirical df with respect to a reasonable measure of performance.(omitted)

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.527-540
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• 2020

While analyzing data, researching outliers, which are out of the main tendency, is as important as researching data that follow the general tendency. In this study we discuss the influence function for outlier discrimination. We derive sample influence functions of sample mean, sample variance, and sample standard deviation, which were not directly derived in previous research. The results enable us to mathematically examine the relationship between the empirical influence function and sample influence function. We can also consider a method to approximate the sample influence function by the empirical influence function. Also, the validity of the relationship between the approximated sample influence function and the empirical influence function is also verified by the simulation of random sampled data in normal distribution. As the result of a simulation, both the relationship between the two influence functions, sample and empirical, and the method of approximating the sample influence function through the emperical influence function were verified. This research has significance in proposing a method that reduces errors in the approximation of the empirical influence function and in proposing an effective and practical method that proceeds from previous research that approximates the sample influence function directly through empirical influence function by constant revision.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1278-1280
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• 2003

Striping is a main factor for imaging spectroradiometer data, which is obtained by multi-sensor scanning on spacecraft. The reason causing stripes and the development of striping removal methods are simply described in this paper, particularly, the principle of Matching Empirical Distribution Functions is introduced in detail. By using this method, some experiments are done to destripe imaging spectrometer data of SZ-3. The result shows that the method of Matching Empirical Distribution Functions is available for destirping Imaging spectroradiometer data of SZ-3, and the quality of image is improved obviously. This will help to process the future similar instruments data.

• Journal of Korean Society for Quality Management
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• v.31 no.1
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• pp.109-122
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• 2003

This paper presents a robust process capability index(PCI) based on the expected loss derived from the empirical distribution function(EDF). We propose the EDF expected loss in order to develop a PCI that does not depends on the underlying process distribution. The EDF expected loss depends only on the sample data, so the PCI based on it is robust and it does nor require complex calculations. The inverted normal loss function(INLF) is employed in order to overcome the drawback of the quadratic loss which may Increase unboundedly outside the specification limits. A comprehensive simulation study was performed under various process distributions, in order to compare the accuracy and the precision of the proposed PCI with those of the PCI based on the expected loss derived from the normal distribution. The proposed PCI turned out to be more accurate than the normal PCI in most cases, especially when the process distribution has high kurtosis or skewness. It is expected that the proposed PCI can be utilized In real processes where the true distribution family may not be known.