• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.285-292
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• 1999

In this paper we propose the several estimators of the Lorenz curve in the Pareto distribution and obtain the bias and the mean squared error for each estimator. We compare the proposed estimators with the uniformly minimum variance unbiased estimator (UMVUE) and the maximum likelihood estimator (MLE) in terms of the mean squared error (MSE) through Monte Carlo methods and discuss the results.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.159-174
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• 1996

Facing decision-making tasks, managers frequently make judgments, However, since managers are human beings, the fficiency of their judgments is limited. Two major sources of inefficiency in their judgments have been recognized : one is systematic deviations from normatively preferred decisions, so called bias or incorrect intuition, and the other is inconsistency in their judgments, i. e. erratic decision making variance. Rather than bias, variance is really expensive or damaging. Thus, if the inconsistency inmanagers judgments is removed, performance could be by far improved by virtue of the reduced random variance. One of the approaches to improve managerial judgment is to simply bring managers together by effectively moderating the random variance due to inconsistency. Focusing on combining judgments, this paper addresses many relevant issues such as why combining and how to combine judgments, and suggests methods and models to effectively aggregate subjective judgments, We conduct an experiment to validata the effectiveness of combining jugements over individual judgments. Various combining schemes are also evaluated in terms of their prective accuracy. Among them, mean bias based wighting scheme turns out the best. However, when available information is not enough to estimate the expertise of judges, simple and robust equal weighting might be more efficient and productive. This urges an imperative future research on the issue of how many and which ones to combine from a large set of experts.

• Communications for Statistical Applications and Methods
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• v.21 no.3
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• pp.201-212
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• 2014

We provide a simple basic method to find bounds for higher order moments of unimodal distributions in terms of lower order moments when the random variable takes value in a given finite real interval. The bounds for moments in terms of the geometric mean of the distribution are also derived. Both continuous and discrete cases are considered. The bounds for the ratio and difference of moments are obtained. The special cases provide refinements of several well-known inequalities, such as Kantorovich inequality and Krasnosel'skii and Krein inequality.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.982-983
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• 2013

This paper introduce a dynamic polling method for XG-PON systems and describes the performance evaluation results. We show the mean delay and delay variance of the introduced method and the existing method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.8
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• pp.689-701
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• 2011

The relations between ambient conditions and ice accretion shapes are quantitatively analyzed by employing self-organization maps and analysis of variance. Liquid water contents(LWC), mean volumetric droplet diameter(MVD), ambient temperature and free-stream velocity are chosen as ambient conditions which change ice accretion shapes. The parameters of ice accretion shape are selected as maximum thickness, icing limits, ice heading, and ice accretion area. Qualitative analysis was conducted by employing self-organization maps which show the qualitative relations between ice shapes and ambient conditions. The quantitative results of analysis of variance yield intensity of ambient conditions to the parameters of ice accretion shapes.

• Journal of Korean Society for Quality Management
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• v.29 no.1
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• pp.1-10
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• 2001

We consider the problem of detecting special variations in multivariate $T^2$-control chart when two or more multivariate outliers are present. Since a multivariate outlier may reflect slippage in mean, variance, or correlation, it can distort the sample mean vector and sample covariance matrix. Damaged sample mean vector and sample covariance matrix have difficulty in examining special variations clearly, An alternative to detection outliers or special variations is to use robust estimators of mean vector and covariance matrix that are less sensitive to extreme observations than are the standard estimators $\bar{x}$ and $\textbf{S}$. We applied popular minimum volume ellipsoid(MVE) and minimum covariance determinant(MCD) method to estimate mean vector and covariance matrix and compared its results with standard $T^2$-control chart using simulated multivariate data with outliers. We found that the modified $T^2$-control chart based on the above robust methods were more effective in detecting special variations clearly than the standard $T^2$-control chart.

• The Journal of the Acoustical Society of Korea
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• v.33 no.1
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• pp.68-74
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• 2014

In a previous work, we proposed a novel approach of histogram equalization using a supplement set which is composed of centroids of Fuzzy C-Means of the background utterances. The performance of the proposed method is affected by the size of the supplement set, but it is difficult to find the best size at the point of recognition. In this paper, we propose a histogram equalization using a supplement set for majority voting based speaker identification. The proposed method identifies test utterances using a majority voting on the histogram equalization methods with various sizes of supplement sets. The proposed method is compared with the conventional feature normalization methods such as CMN(Cepstral Mean Normalization), MVN(Mean and Variance Normalization), and HEQ(Histogram Equalization) and the histogram equalization method using a supplement set.

• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.205-216
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• 1996

Consider the problem of estimating a multivariate normal mean with an unknown covarience matrix under a weighted sum of squared error losses. We first provide hierarchical Bayes estimators which shrink the usual (maximum liklihood, uniformly minimum variance unbiased) estimator towards a regression surface and then prove the admissibility of these estimators using Blyth's (1951) method.

• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.119-131
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• 1995

Based on Monte-Carlo simulation results we propose a robust analysis of variance procedure by utilizing trimmed mean and Winsorized variance. We deal with mainly the one-way classification case. We evaluate the empirical distribution of a pseudo-F statistic based on symmetrically Winsorized sum of squares when the population is normally distributed.

• Journal of Integrative Natural Science
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• v.6 no.4
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• pp.251-255
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• 2013

For the mean vector of a p-variate normal distribution ($p{\geq}4$), the optimal estimation within the class of modified James-Stein type decision rules under the quadratic loss is given when the underlying distribution is that of a variance mixture of normals and when the norm ${\parallel}{\theta}-\bar{\theta}1{\parallel}$ it known.