• 산업경영시스템학회지
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• 제24권67호
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• pp.11-18
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• 2001

The efficiency and robustness of the scale estimator based on the Gini's mean difference are well known in Nam et al.(2000). In this paper we propose use of robust control limits based on the Gini's mean difference for the control of the process deviation. To compare the performances of the proposed control chart with the existing R-chart or S-chart, some Monte Carlo simulations are performed. The simulation results show that the use of the Gini's mean difference in construction of the control limits has good performance.

• 품질경영학회지
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• 제29권3호
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• pp.79-85
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• 2001

Estimation of the process deviation is an important problem in statistical process control, especially in the control chart, process capability analysis or measurement system analysis. In this paper we suggest the use of the Gini's mean difference for the estimation of the process deviation when we design the control limits in construction of the control charts. The efficiency of the Gini's mean difference was well explained in Nam, Lee and Jung(2000). In this paper we propose an $\overline{X}$ control chart which use the control limits based on the Gini's mean difference. In various classes of distributions, the proposed control chart shows food performance.

• 산업경영시스템학회지
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• 제23권58호
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• pp.113-118
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• 2000

Estimation of the process deviation is an important problem in statistical process control, especially in the control chart, process capability analysis or measurement system analysis. In this paper we suggest the use of the Gini's mean difference for the estimation of the c, the measure of the process deviation through a lots of simulations in various types of distributions. The Gini's mean difference uses the differences of all possible pairs of data. This point will improve the efficiency of estimation. In various classes of distributions, the Gini's mean difference shows good performance, in sense of bias of estimates or mean squared errors.

• 응용통계연구
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• 제16권2호
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• pp.283-291
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• 2003

소득분배의 가장 대표적인 불평등척도는 Gini index이며, 이것은 통계학자인 Gini가 제안한 지표로서 소득분배 에 관한 분석에서 가장 널리 이용되고 있다. 본 논문에서는 두 원의 호에 의해 Lorenz 곡선을 추정하고 코사인법칙을 이용하여 Gini index를 추정하기 위한 새로운 간편한 방법을 제시하여, 소득분포를 따르는 파레토분포에서모의실험을 통해 Ogwang and Rao (1996)의 추정방법과 평균제곱오차 면에서 비교 분석한다.

• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.249-256
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• 2001

We shall derive the MLE and UMVUE of Lorenz Curve and Gini Index in a Pareto distribution with the pdf(1.1) and their variances. And compare mean square errors(MSE) of the MLE and UMVUE of the Lorenz Curve and Gini Index in a Pareto distribution with pdf(1.1).

• Journal of applied mathematics & informatics
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• 제5권1호
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• pp.213-222
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• 1998

In this paper we propose several estimators of Gini index of the two-parameter exponential distribution and obtain dis-tributions and moments of the proposed estimators. The proposed estimators are shown to cosistency and will be compares in terms of the proposed estimators. The proposed estimators are shown to cosistency and will be compared in terms of the mean squared error (MSE) through Monte Carlo method.

• 수산경영론집
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• 제39권3호
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• pp.25-47
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• 2008

The Fishing Communities (so-called Uchongae) in Korea was legally established in 1962. It has been gradually expanded by quantity, and we have total 1,969 communities in 2006. The major establishment purpose of Uchongae was put 2 functions. The first function is to make up the double industry structure in coastal region, and second function is to make economical condition for Uchongae. Nevertheless the Fishing Communities System in Korea was not successfully developed after first beginning. The Income gap have become heavily between fishing area and non - fishing area, including agricultural area. The income gap has been due to rapid industrialization and urbanization in Korea. And the income gap even have become heavily among Uchongaes. In this paper, It have been researched the degree of Income inequality among Uchongaes in Korea during 1986-2006. The income inequality degree was analyzed by Gini coefficient and Mean Log Deviation (MLD) using Lorenz Curve. According to analysis result, the Gini coefficient of Uchongaes in Korea has been about 2-times high from 0.0847 to 0.1770 during 20 years. And the MLD has been 5.4 times from 0.0125 to 0.0679 during same periods. This means to more wide the general Income Inequality among the Uchongaes in Korea. Especially, It means to more wide the gap of high ranking Uchongaes and low ranking Uchongaes that MLD index multiplier has been more high.

• 대한수학회보
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• 제49권1호
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• pp.33-47
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• 2012

In this paper, the log-convexity of another class one-parameter mean is investigated. As applications, some new upper and lower bounds of logarithmic mean, new estimations for identric mean and new inequalities for power-exponential mean and exponential-geometric mean are first given.

• 대한수학회지
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• 제60권3호
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• pp.503-520
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• 2023

In this paper, using the theory of majorization, we discuss the Schur m power convexity for L-conjugate means of n variables and the Schur convexity for weighted L-conjugate means of n variables. As applications, we get several inequalities of general mean satisfying Schur convexity, and a few comparative inequalities about n variables Gini mean are established.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.350-358
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• 2021

In modern years, the performance of the students is analysed with lot of difficulties, which is a very important problem in all the academic institutions. The main idea of this paper is to analyze and evaluate the academic performance of the college students with bipolar disorder by applying data mining classification algorithms using Jupiter Notebook, python tool. This tool has been generally used as a decision-making tool in terms of academic performance of the students. The various classifiers could be logistic regression, random forest classifier gini, random forest classifier entropy, decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree Classifier, GaussianNB, BernoulliNB are used. The results of such classification model deals with 13 measures like Accuracy, Precision, Recall, F1 Measure, Sensitivity, Specificity, R Squared, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, TPR, TNR, FPR and FNR. Therefore, conclusion could be reached that the Decision Tree Classifier is better than that of different algorithms.