• Title/Summary/Keyword: Gini mean

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A Control Chart of the Deviation Based on the Gini′s Mean Difference (지니(Gini)의 평균차이를 이용한 산포관리도)

  • 남호수;강중철
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.24 no.67
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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.

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An $\overline{X}$-Control Chart Based on the Gini′s Mean Difference (지니(Gini)의 평균차이에 기초한 $\overline{X}$-관리도)

  • 남호수;강중철
    • Journal of Korean Society for Quality Management
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    • v.29 no.3
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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.

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On the Estimation of the Process Deviation Based on the Gini's Mean Difference (지니(Gini)의 평균차이를 이용한 공정산포 추정)

  • 남호수;이병근;정현석
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.23 no.58
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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.

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Estimation of the Gini Index Based on the Properties of Circle (원의 성질을 이용한 GINI INDEX의 추정)

  • 강석복;조영석
    • The Korean Journal of Applied Statistics
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    • v.16 no.2
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    • pp.283-291
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    • 2003
  • The Gini index is one of the most commonly used measures of inequality of income distributions. In this paper, the Lorenz curve is estimated by arcs of two optimal circles, and a new simple method to estimate the Gini index is proposed using the law of cosines. We compare the proposed estimator with the estimator proposed by Ogwang and Rao(1996) in terms of the mean squared error(MSE) though Monte Carlo simulation in a Pareto distribution.

Estimations of Lorenz Curve and Gini Index in a Pareto Distribution

  • Woo, Jung Soo;Yoon, Gi Ern
    • Communications for Statistical Applications and Methods
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    • v.8 no.1
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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).

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DISTRIBUTIONS AND MOMENTS FOR ESTIMATORS OF GINI INDEX IN AN EXPONENTIAL DISTRIBUTION

  • Kang, Suk-Bok;Cho, Young-Suk
    • Journal of applied mathematics & informatics
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    • v.5 no.1
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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.

A Study on the Income Inequality among the Fishing Communities in Korea (어촌계의 소득 격차와 변화에 관한 연구)

  • Ock, Young-Soo
    • The Journal of Fisheries Business Administration
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    • v.39 no.3
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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.

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THE LOG-CONVEXITY OF ANOTHER CLASS OF ONE-PARAMETER MEANS AND ITS APPLICATIONS

  • Yang, Zhen-Hang
    • Bulletin of the Korean Mathematical Society
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    • v.49 no.1
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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.

SCHUR CONVEXITY OF L-CONJUGATE MEANS AND ITS APPLICATIONS

  • Chun-Ru Fu;Huan-Nan Shi;Dong-Sheng Wang
    • Journal of the Korean Mathematical Society
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    • v.60 no.3
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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.

Prediction of Academic Performance of College Students with Bipolar Disorder using different Deep learning and Machine learning algorithms

  • Peerbasha, S.;Surputheen, M. Mohamed
    • International Journal of Computer Science & Network Security
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    • v.21 no.7
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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.