• 제목/요약/키워드: normal approximation

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Polynomially Adjusted Normal Approximation to the Null Distribution of Ansari-Bradley Statistic

  • Ha, Hyung-Tae;Yang, Wan-Youn
    • 응용통계연구
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    • 제24권6호
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    • pp.1161-1168
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    • 2011
  • The approximation for the distribution functions of nonparametric test statistics is a significant step in statistical inference. A rank sum test for dispersions proposed by Ansari and Bradley (1960), which is widely used to distinguish the variation between two populations, has been considered as one of the most popular nonparametric statistics. In this paper, the statistical tables for the distribution of the nonparametric Ansari-Bradley statistic is produced by use of polynomially adjusted normal approximation as a semi parametric density approximation technique. Polynomial adjustment can significantly improve approximation precision from normal approximation. The normal-polynomial density approximation for Ansari-Bradley statistic under finite sample sizes is utilized to provide the statistical table for various combination of its sample sizes. In order to find the optimal degree of polynomial adjustment of the proposed technique, the sum of squared probability mass function(PMF) difference between the exact distribution and its approximant is measured. It was observed that the approximation utilizing only two more moments of Ansari-Bradley statistic (in addition to the first two moments for normal approximation provide) more accurate approximations for various combinations of parameters. For instance, four degree polynomially adjusted normal approximant is about 117 times more accurate than normal approximation with respect to the sum of the squared PMF difference.

다변량 왜정규분포 기반 선형결합통계량에 대한 안장점근사 (Saddlepoint Approximation to the Linear Combination Based on Multivariate Skew-normal Distribution)

  • 나종화
    • 응용통계연구
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    • 제27권5호
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    • pp.809-818
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    • 2014
  • 다변량 왜정규분포는 다변량 정규분포를 포함하는 분포로 최근 많은 응용분야에서 활용되고 있다. 본 논문에서는 다변량 왜정규분포를 기반으로 하는 선형결합통계량의 분포함수에 대한 안장점근사를 다루었다. 이는 단변량 왜정규분포 기반 표본평균에 대한 Na와 Yu (2013)의 결과를 선형결합 및 다변량의 경우로 확장한 것이다. 모의실험과 실제자료분석을 통해 제안된 근사법의 유용성과 정확도를 확인하였다.

왜정규 표본평균의 분포함수에 대한 안장점근사 (Saddlepoint approximation for distribution function of sample mean of skew-normal distribution)

  • 나종화;유혜경
    • Journal of the Korean Data and Information Science Society
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    • 제24권6호
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    • pp.1211-1219
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    • 2013
  • 최근 많은 통계 이론과 응용 문제에 정규분포의 대안으로 왜정규분포에 대한 활용이 높아지고 있다. 본 논문에서는 왜정규분포에 기반한 표본평균의 분포함수에 대한 안장점근사를 다루었다. 안장점근사는 기존의 정규근사에 비해 매우 뛰어난 정확성을 보일 뿐 아니라, 소표본에서도 정확한 근사결과를 제공한다. 본 논문에서 제시한 왜정규분포에 관련된 안장점근사는 복잡한 계산이 요구되는 기존의 Gupta와 Chen (2001)과 Chen 등 (2004)에 대한 근사적 방법으로 사용될 수 있다. 모의실험을 통해 표본평균의 분포함수에 대한 제안된 안장점근사의 정확도를 확인하고, 실제 자료에 대한 응용으로 Roberts (1966)의 쌍둥이 자료의 분석에 적용하였다.

KOLMOGOROV DISTANCE FOR MULTIVARIATE NORMAL APPROXIMATION

  • Kim, Yoon Tae;Park, Hyun Suk
    • Korean Journal of Mathematics
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    • 제23권1호
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    • pp.1-10
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    • 2015
  • This paper concerns the rate of convergence in the multidimensional normal approximation of functional of Gaussian fields. The aim of the present work is to derive explicit upper bounds of the Kolmogorov distance for the rate of convergence instead of Wasserstein distance studied by Nourdin et al. [Ann. Inst. H. Poincar$\acute{e}$(B) Probab.Statist. 46(1) (2010) 45-98].

다변량 왜정규분포 기반 이차형식의 분포함수에 대한 안장점근사 (Saddlepoint approximation to the distribution function of quadratic forms based on multivariate skew-normal distribution)

  • 나종화
    • 응용통계연구
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    • 제29권4호
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    • pp.571-579
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    • 2016
  • 이차형식 통계량의 분포함수에 대한 연구는 주로 다변량 정규분포의 가정하에서 진행되어 왔다. 최근 다변량 정규분포를 포함하는 다변량 왜정규분포에 대한 연구가 활발하다. 본 논문에서는 다변량 왜정규분포의 가정하에서 이차형식 통계량의 분포함수에 대한 근사를 다루었다. 근사의 방법으로는 소표본에서도 정확도가 뛰어난 근사법으로 알려진 안장점근사를 사용하였으며, 모의실험을 통해 그 정도를 확인하였다.

Approximation of binomial Distribution via Dynamic Graphics

  • Lee, Kee-Won
    • Communications for Statistical Applications and Methods
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    • 제6권3호
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    • pp.821-830
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    • 1999
  • In This paper we calculate the probabilities of binomial and Poisson distributions when n or${\mu}$ is large. Based on this calculation we consider the normal approximation to the binomial and binomial approximation to Poisson. We implement this approximation via CGI and dynamic graphs. These implementation are made available through the internet.

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APPROXIMATION TO THE CUMULATIVE NORMAL DISTRIBUTION USING HYPERBOLIC TANGENT BASED FUNCTIONS

  • Yun, Beong-In
    • 대한수학회지
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    • 제46권6호
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    • pp.1267-1276
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    • 2009
  • This paper presents a method for approximation of the standard normal distribution by using hyperbolic tangent based functions. The presented approximate formula for the cumulative distribution depends on one numerical coefficient only, and its accuracy is admissible. Furthermore, in some particular cases, closed forms of inverse formulas are derived. Numerical results of the present method are compared with those of an existing method.

한켈특이치와 특이벡터를 이용한 복수 입력 시간지연 시스템의 유리근사화 (Rational Approximation of Multiple Input Delay Systems Using the Hankel Singular Values Vectors)

  • 황이철
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 1996년도 춘계학술대회 논문집
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    • pp.299-304
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    • 1996
  • This paper studies the rational approximation of multiple input delay systems using the Hankel singular values and vectors, which are the soultion of a transcendental equation. Rational approximatants are obtained from output normal realizations which are constructed by the Hankel singular values and vectors. Consequently, it is shown that rational approximants by output normal realization preserve intrinsic properties of time delay systems than Pade approximants.

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STANCU TYPE GENERALIZATION OF MODIFIED GAMMA OPERATORS BASED ON q-INTEGERS

  • Chen, Shu-Ni;Cheng, Wen-Tao;Zeng, Xiao-Ming
    • 대한수학회보
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    • 제54권2호
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    • pp.359-373
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    • 2017
  • In this paper, we propose the Stancu type generalization of a kind of modified q-Gamma operators. We estimate the moments of these operators and give the basic convergence theorem. We also obtain the Voronovskaja type theorem. Furthermore, we obtain the local approximation, rate of convergence and weighted approximation for these operators.

OPTIMAL APPROXIMATION BY ONE GAUSSIAN FUNCTION TO PROBABILITY DENSITY FUNCTIONS

  • Gwang Il Kim;Seung Yeon Cho;Doobae Jun
    • East Asian mathematical journal
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    • 제39권5호
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    • pp.537-547
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    • 2023
  • In this paper, we introduce the optimal approximation by a Gaussian function for a probability density function. We show that the approximation can be obtained by solving a non-linear system of parameters of Gaussian function. Then, to understand the non-normality of the empirical distributions observed in financial markets, we consider the nearly Gaussian function that consists of an optimally approximated Gaussian function and a small periodically oscillating density function. We show that, depending on the parameters of the oscillation, the nearly Gaussian functions can have fairly thick heavy tails.