• Title/Summary/Keyword: New Normality

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Goodness-of-Fit Test for the Normality based on the Generalized Lorenz Curve

  • Cho, Youngseuk;Lee, Kyeongjun
    • Communications for Statistical Applications and Methods
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    • 제21권4호
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    • pp.309-316
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    • 2014
  • Testing normality is very important because the most common assumption is normality in statistical analysis. We propose a new plot and test statistic to goodness-of-fit test for normality based on the generalized Lorenz curve. We compare the new plot with the Q-Q plot. We also compare the new test statistic with the Kolmogorov-Smirnov (KS), Cramer-von Mises (CVM), Anderson-Darling (AD), Shapiro-Francia (SF), and Shapiro-Wilks (W) test statistic in terms of the power of the test through by Monte Carlo method. As a result, new plot is clearly classified normality and non-normality than Q-Q plot; in addition, the new test statistic is more powerful than the other test statistics for asymmetrical distribution. We check the proposed test statistic and plot using Hodgkin's disease data.

NORMALIZED SAMPLE LORENZ CURVE를 이용한 검정력이 높은 정규성 검정 (More Powerful Test for Normality Based on the Normalized Sample Lorenz Curve)

  • 강석복;조영석
    • 응용통계연구
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    • 제15권2호
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    • pp.415-421
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    • 2002
  • 통계적분석에서 가장 대표적인 가정이 정규성 가정이므로 데이터의 정규성 검정은 매우 중요하다. 이 논문에서는 정규성 검정을 위해 경제학에서 소득분배의 불균형에 관한 척도로 널리 이용되는 Lorenz curve를 변형한 새로운 플롯과 검정통계량을 제시한다. 그리고 제한한 검정을 W검정 (Shapiro and Wilk (1965)), Lorenz curve를 이용한 TL검정(Kang and Cho (1999))과 몬테칼로 방법을 이용하여 검정력을 비교한다. 제안된 검정이 특별한 대립분포의 경우를 제외하고는 대부분 검정력이 높았다.

Improving Efficiency of the Moment Estimator of the Extreme Value Index

  • Yun, Seokhoon
    • Journal of the Korean Statistical Society
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    • 제30권3호
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    • pp.419-433
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    • 2001
  • In this paper we introduce a method of improving efficiency of the moment estimator of Dekkers, Einmahl and de Haan(1989) for the extreme value index $\beta$. a new estimator of $\beta$ is proposed by adding the third moment ot the original moment estimator which is composed of the first two moments of the log-transformed sample data. We establish asymptotic normality of the new estimator and examine and adaptive procedure for the new estimator. The resulting adaptive estimator proves to be asymptotically better than the moment estimator particularly for $\beta$<0.

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Logistic Model for Normality by Neural Networks

  • Lee, Jea-Young;Rhee, Seong-Won
    • Journal of the Korean Data and Information Science Society
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    • 제14권1호
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    • pp.119-129
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    • 2003
  • We propose a new logistic regression model of normality curves for normal(diseased) and abnormal(nondiseased) classifications by neural networks in data mining. The fitted logistic regression lines are estimated, interpreted and plotted by the neural network technique. A few goodness-of-fit test statistics for normality are discussed and the performances by the fitted logistic regression lines are conducted.

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PERIODOGRAM ANALYSIS WITH MISSING OBSERVATIONS

  • Ghazal M.A.;Elhassanein A.
    • Journal of applied mathematics & informatics
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    • 제22권1_2호
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    • pp.209-222
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    • 2006
  • Estimation of the spectral measure, covariance and spectral density functions of a strictly stationary r-vector valued time series is considered, under the assumption that some of the observations are missed. The modified periodograms are calculated using data window. The asymptotic normality is studied.

ON THE LIMITING DISTRIBUTION FOR ESTIMATE OF PROCESS CAPABILITY INDEX

  • Park, Hyo-Il;Cho, Joong-Jae
    • Journal of the Korean Statistical Society
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    • 제36권4호
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    • pp.471-477
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    • 2007
  • In this paper, we provide a new proof to correct the asymptotic normality for the estimate $\hat{C}_{pmk}\;of\;C_{pmk}$, which is one of the well-known definitions of the process capability index. Also we comment briefly on the correction of the limiting distribution for $\hat{C}_{pmk}$ and on the use of re-sampling methods for the inference of $C_{pmk}$. Finally we discuss the concept of asymptotic unbiasedness.

An Improved Quantize-Quantize Plot for Normality Test

  • Lee, Jea-Young;Rhee, Seong-Won
    • Communications for Statistical Applications and Methods
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    • 제5권1호
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    • pp.67-75
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    • 1998
  • A new graphical method, named transformed quantize-quantile (TQQ), of a quantize-quantile (Q-Q) Plot is developed for the detection of deviations from the normal distribution. It will be shown that TQQ is helpful for detecting patterns of how points depart from normality. TQQ characteristics of the various kinds of representations are illustrated by a generated sample from a composite of a normal distribution and a clinical example for TQQ is constructed and explained.

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부문항이 분할된 고사에서 우량한 신뢰도 계수추경과 그 평가치 분포의 정규화 (On Estimating Good Reliability Coefficient when the Test is Split into Several Formats of Subtests and Standardizing the Raw Score, whose Distribution is Departed from Normality.)

  • 홍석강
    • 한국수학교육학회지시리즈A:수학교육
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    • 제41권1호
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    • pp.109-126
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    • 2002
  • In this thesis. we estimated the good reliability coefficient ${\beta}$$\sub$k/ that is unbiased, consistent and more efficient than Cronbach's ${\alpha}$$\sub$k/ in splitting of a test into several formats of subtests and several properties of ${\beta}$$\sub$k/ are also represented. The tables of coefficients of skewness and kurtosis are represented to test the significance of departures from normality. We got the cumulative normal plots of z'from the distribution which is departed from normality using the Bock's approximation procedure and we finally enumerated the transformed standardized scores z'and a new raw score X' which enable us to proceed further evaluation procedures depending on our assessment policy.

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데이터 증가를 통한 선형 모델의 일반화 성능 개량 (중심극한정리를 기반으로) (Improvement of generalization of linear model through data augmentation based on Central Limit Theorem)

  • 황두환
    • 지능정보연구
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    • 제28권2호
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    • pp.19-31
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    • 2022
  • 기계학습 모델 구축 간 트레이닝 데이터를 활용하며, 훈련 간 사용되지 않은 테스트 데이터를 활용하여 모델의 정확도와 일반화 성능을 판단한다. 일반화 성능이 낮은 모델의 경우 새롭게 받아들이게 되는 데이터에 대한 예측 정확도가 현저히 감소하게 되며 이러한 현상을 두고 모델이 과적합 되었다고 한다. 본 연구는 중심극한정리를 기반으로 데이터를 생성 및 기존의 훈련용 데이터와 결합하여 새로운 훈련용 데이터를 구성하고 데이터의 정규성을 증가시킴과 동시에 이를 활용하여 모델의 일반화 성능을 증가시키는 방법에 대한 것이다. 이를 위해 중심극한정리의 성질을 활용해 데이터의 각 특성별로 표본평균 및 표준편차를 활용하여 데이터를 생성하였고, 새로운 훈련용 데이터의 정규성 증가 정도를 파악하기 위하여 Kolmogorov-Smirnov 정규성 검정을 진행한 결과, 새로운 훈련용 데이터가 기존의 데이터에 비해 정규성이 증가하였음을 확인할 수 있었다. 일반화 성능은 훈련용 데이터와 테스트용 데이터에 대한 예측 정확도의 차이를 통해 측정하였다. 새롭게 생성된 데이터를 K-Nearest Neighbors(KNN), Logistic Regression, Linear Discriminant Analysis(LDA)에 적용하여 훈련시키고 일반화 성능 증가정도를 파악한 결과, 비모수(non-parametric) 기법인 KNN과 모델 구성 간 정규성을 가정으로 갖는 LDA의 경우에 대하여 일반화 성능이 향상되었음을 확인할 수 있었다.

균일한 압축장에 대한 새로운 휨 형태의 파괴 매캐니즘 (New Flexural Failure Mechanisms for Uniform Compression Stress Fields)

  • 홍성걸
    • 한국콘크리트학회:학술대회논문집
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    • 한국콘크리트학회 1997년도 가을 학술발표회 논문집
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    • pp.546-551
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    • 1997
  • New typology of failure mechanisms for uniform compression fields are presented based on the classical theory of plasticity, in particular th normality rule, and the limit theorem. The concrete is assumed as a rigid-perfectly plastic material obeying the modified Coulomb failure criteria with zero tension cut-off. The failure mechanisms are capable of explaining flexural types of crushing failure in uniaxial uniform compression stress fields which are called struts in truss models. The failure mechanisms consist of sliding failure along straight failure lines or hyperbolic failure curves and rigid body rotation. The failure mechanisms involving straight failure lines are explained by constant strain expansion in the first principal direction and rigid body rotation motion. The failure mechanisms presented are applied to the explanation of bond failure of bar combined with concrete crushing failure and flexural crushing failure of concrete.

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