• 제목/요약/키워드: multivariate normal

검색결과 354건 처리시간 0.021초

MULTIVARIATE JOINT NORMAL LIKELIHOOD DISTANCE

  • Kim, Myung-Geun
    • Journal of applied mathematics & informatics
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    • 제27권5_6호
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    • pp.1429-1433
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    • 2009
  • The likelihood distance for the joint distribution of two multivariate normal distributions with common covariance matrix is explicitly derived. It is useful for identifying outliers which do not follow the joint multivariate normal distribution with common covariance matrix. The likelihood distance derived here is a good ground for the use of a generalized Wilks statistic in influence analysis of two multivariate normal data.

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On the Robustness of Chi-square Test Procedure for a Compounded Multivariate Normal Mean

  • Kim, Hea-Jung
    • Communications for Statistical Applications and Methods
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    • 제2권2호
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    • pp.330-335
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    • 1995
  • The rebustness of one sample Chi-square test for multivariate normal mean vector is investigated when the multivariate normal population is mixed with another multivariate normal population with differing in the mean vector. Explicit expressions for the level of significance and power of the test are derived. Some numerical results indicate that the Chi-square test procedure is quite robust against slight mixtures of multivariate normal populations differing in location parameters.

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역정규 손실함수를 이용한 다변량 공정능력지수 (Multivariate Process Capability Index Using Inverted Normal Loss Function)

  • 문혜진;정영배
    • 산업경영시스템학회지
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    • 제41권2호
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    • pp.174-183
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    • 2018
  • In the industrial fields, the process capability index has been using to evaluate the variation of quality in the process. The traditional process capability indices such as $C_p$, $C_{pk}$, $C_{pm}$ and $C^+_{pm}$ have been applied in the industrial fields. These traditional process capability indices are mainly applied in the univariate analysis. However, the main streams in the recent industry are the multivariate manufacturing process and the multiple quality characteristics are corrected each other. Therefore, the multivariate statistical method should be used in the process capability analysis. The multivariate process indices need to be enhanced with more useful information and extensive application in the recent industrial fields. Hence, the purpose of the study is to develop a more effective multivariate process index ($MC_{pI}$) using the multivariate inverted normal loss function. The multivariate inverted normal loss function has the flexibility for the any type of the symmetrical and asymmetrical loss functions as well as the economic information. Especially, the proposed modeling method for the multivariate inverted normal loss function (MINLF) and the expected loss from MINLF in this paper can be applied to the any type of the symmetrical and asymmetrical loss functions. And this modeling method can be easily expanded from a bivariate case to a multivariate case.

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.

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.

A BAYESIAN METHOD FOR FINDING MINIMUM GENERALIZED VARIANCE AMONG K MULTIVARIATE NORMAL POPULATIONS

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • 제32권4호
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    • pp.411-423
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    • 2003
  • In this paper we develop a method for calculating a probability that a particular generalized variance is the smallest of all the K multivariate normal generalized variances. The method gives a way of comparing K multivariate populations in terms of their dispersion or spread, because the generalized variance is a scalar measure of the overall multivariate scatter. Fully parametric frequentist approach for the probability is intractable and thus a Bayesian method is pursued using a variant of weighted Monte Carlo (WMC) sampling based approach. Necessary theory involved in the method and computation is provided.

Multivariate measures of skewness for the scale mixtures of skew-normal distributions

  • Kim, Hyoung-Moon;Zhao, Jun
    • Communications for Statistical Applications and Methods
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    • 제25권2호
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    • pp.109-130
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    • 2018
  • Several measures of multivariate skewness for scale mixtures of skew-normal distributions are derived. As a special case, those of multivariate skew-t distribution are considered in detail. Furthermore, the similarities, differences, and behavior of these measures are explored for cases of some specific members of the multivariate skew-normal and skew-t distributions using a simulation study. Since some measures are vectors, it is better to take all measures in the same scale when comparing them. In order to attain such a set of comparable indices, the sample version is considered for each of the skewness measures that are taken as test statistics for the hypothesis of t distribution against skew-t distribution. An application is reported for the data set consisting of 71 total glycerol and magnesium contents in Grignolino wine.

Monte Carlo Estimation of Multivariate Normal Probabilities

  • Oh, Man-Suk;Kim, Seung-Whan
    • Journal of the Korean Statistical Society
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    • 제28권4호
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    • pp.443-455
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    • 1999
  • A simulation-based approach to estimating the probability of an arbitrary region under a multivariate normal distribution is developed. In specific, the probability is expressed as the ratio of the unrestricted and the restricted multivariate normal density functions, where the restriction is given by the region whose probability is of interest. The density function of the restricted distribution is then estimated by using a sample generated from the Gibbs sampling algorithm.

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A Goodness-of-Fit Test for Multivariate Normal Distribution Using Modified Squared Distance

  • Yim, Mi-Hong;Park, Hyun-Jung;Kim, Joo-Han
    • Communications for Statistical Applications and Methods
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    • 제19권4호
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    • pp.607-617
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    • 2012
  • The goodness-of-fit test for multivariate normal distribution is important because most multivariate statistical methods are based on the assumption of multivariate normality. We propose goodness-of-fit test statistics for multivariate normality based on the modified squared distance. The empirical percentage points of the null distribution of the proposed statistics are presented via numerical simulations. We compare performance of several test statistics through a Monte Carlo simulation.

EM 알고리즘에 의한 다변량 치우친 정규분포 혼합모형의 근사적 적합 (An approximate fitting for mixture of multivariate skew normal distribution via EM algorithm)

  • 김승구
    • 응용통계연구
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    • 제29권3호
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    • pp.513-523
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    • 2016
  • 다중 치우침 모수벡터를 가진 다변량 치우친 정규분포 (MSNMix)를 EM 알고리즘으로 적합하려면 E-step에서 다변량 절단 정규분포의 적률과 확률을 계산해야 하는데 이것은 매우 큰 계산 시간을 요구한다. 그래서 비대칭 자료를 적합하는데 흔히 단순 치우침 모수를 가진 모형을 적용한다. 이 모형은 단변량 처리방식으로 적합하는 것이 가능하기 때문에 처리속도가 매우 빠르다. 그러나 단순 치우침 모수를 적용하는 것은 응용에서 비현실적인 경우가 많다. 본 논문에서는 다중 치우침 모수를 가지는 MSNMix의 근사적 추정법을 제안하는데, 이 방법은 단변량 처리방식이 적용되므로 향상된 처리속도를 보장한다. 그리고 제안된 방법의 실효성을 보이기 위해 몇 가지 실험 결과를 제공한다.