• 제목/요약/키워드: Statistical tests

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난수의 임의성을 평가하기 위한 탐색적 그림도구로서의 재현그림 (Recurrence Plots as an Exploratory Graphical Tool for Evaluating Randomness)

  • 장대흥
    • 응용통계연구
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    • 제22권6호
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    • pp.1153-1165
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    • 2009
  • 의사난수발생기로부터 얻어지는 난수의 임의성을 검정하기 위한 통계방법들이 기존에 많이 제시되었다. 수열의 임의성을 평가하기 위한 탐색적 그림도구로서 우리는 재현그림을 이용할 수 있다.

ORDER RESTRICTED STATISTICAL INFERENCE ON LORENZ CURVES OF PARETO DISTRIBUTIONS

  • Oh, Myongsik
    • Journal of applied mathematics & informatics
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    • 제13권1_2호
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    • pp.457-470
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    • 2003
  • The comparison of two or more Lorenz curves of Pareto distributions of first kind under arbitrary order restriction is studied. The problem is turned out to be a statistical inference problem concerning scale parameters under order restriction. We assume that the location parameters of Palate distributions are completely unknown. In this paper the maximum likelihood estimation and likelihood ratio tests for and against order restriction are proposed.

Rank Tests for Multivariate Linear Models in the Presence of Missing Data

  • Lee, Jae-Won;David M. Reboussin
    • Journal of the Korean Statistical Society
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    • 제26권3호
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    • pp.319-332
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    • 1997
  • The application of multivariate linear rank statistics to data with item nonresponse is considered. Only a modest extension of the complete data techniques is required when the missing data may be thought of as a random sample, and an appropriate modification of the covariances is derived. A proof of the asymptotic multivariate normality is given. A review of some related results in the literature is presented and applications including longitudinal and repeated measures designs are discussed.

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Goodness-of-fit Test for the Weibull Distribution Based on Multiply Type-II Censored Samples

  • Kang, Suk-Bok;Han, Jun-Tae
    • Communications for Statistical Applications and Methods
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    • 제16권2호
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    • pp.349-361
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    • 2009
  • In this paper, we derive the approximate maximum likelihood estimators of the shape parameter and the scale parameter in a Weibull distribution under multiply Type-II censoring by the approximate maximum likelihood estimation method. We develop three modified empirical distribution function type tests for the Weibull distribution based on multiply Type-II censored samples. We also propose modified normalized sample Lorenz curve plot and new test statistic.

On the Study for the Simultaneous Test

  • Park, Hyo-Il
    • Communications for Statistical Applications and Methods
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    • 제20권4호
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    • pp.241-246
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    • 2013
  • In this study, we propose a nonparametric simultaneous test procedure for the location translation and scale parameters. We consider the Wilcoxon rank sum test for the location translation parameter and the Mood test for the scale parameter with the quadratic and maximal types of combining functions. Then we derive the limiting null distributions of the combining functions. We illustrate our procedure with an example and compare efficiency by obtaining the empirical powers through a simulation study. Finally, we discuss some interesting features related to the nonparametric simultaneous tests.

INFERENCE FOR ABSOLUTE LORENZ CURVE AND ABSOLUTE LORENZ ORDERING

  • Arora Sangeeta;Jain Kanchan;Pundir Sudesh
    • Journal of the Korean Statistical Society
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    • 제35권3호
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    • pp.305-316
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    • 2006
  • Absolute Lorenz curve plays an important role for measuring absolute income inequality. Properties of absolute Lorenz curve are listed. Asymptotically distribution free and consistent tests have been proposed for comparing two absolute Lorenz curves in the whole interval [P1, P2] where 0 < P1 < P2 < 1. Absolute Lorenz ordering has been discussed for some distributions.

A Simple Chi-squared Test of Multivariate Normality Based on the Spherical Data

  • Park, Cheolyong
    • Communications for Statistical Applications and Methods
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    • 제8권1호
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    • pp.117-126
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    • 2001
  • We provide a simple chi-squared test of multivariate normality based on rectangular cells on the spherical data. This test is simple since it is a direct extension of the univariate chi-squared test to multivariate case and the expected cell counts are easily computed. We derive the limiting distribution of the chi-squared statistic via the conditional limit theorems. We study the accuracy in finite samples of the limiting distribution and then compare the poser of our test with those of other popular tests in an application to a real data.

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Testing the Goodness of Fit of a Parametric Model via Smoothing Parameter Estimate

  • Kim, Choongrak
    • Journal of the Korean Statistical Society
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    • 제30권4호
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    • pp.645-660
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    • 2001
  • In this paper we propose a goodness-of-fit test statistic for testing the (null) parametric model versus the (alternative) nonparametric model. Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. Our test is based on the bootstrap estimator of the probability that the smoothing parameter estimator is infinite when fitting residuals to cubic smoothing spline. Power performance of this test is investigated and is compared with many other tests. Illustrative examples based on real data sets are given.

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Analysis of Linear Regression Model with Two Way Correlated Errors

  • Ssong, Seuck-Heun
    • Journal of the Korean Statistical Society
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    • 제29권2호
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    • pp.231-245
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    • 2000
  • This paper considers a linear regression model with space and time data in where the disturbances follow spatially correlated error components. We provide the best linear unbiased predictor for the one way error components. We provide the best linear unbiased predictor for the one way error component model with spatial autocorrelation. Further, we derive two diagnostic test statistics for the assessment of model specification due to spatial dependence and random effects as an application of the Lagrange Multiplier principle.

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Goodness-of-Fit Test for the Exponential Distribution Based on the Transformed Sample Lorenz curve

  • Suk-Bok;Young-Suk
    • Communications for Statistical Applications and Methods
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    • 제7권1호
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    • pp.277-284
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    • 2000
  • The transformed sample Lorenz curve provides a powerful and easily computed goodness-of-fit test for exponentiality which does not depend on the unknown scale parameter. We compare the power of the transformed sample Lorenz curve statistic with the other goodness-of-fit tests for exponentiality against various alternatives through Monte Carlo methods and discuss the results.

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