• Title, Summary, Keyword: minimum variance unbiased estimator

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ONNEGATIVE MINIMUM BIASED ESTIMATION IN VARIANCE COMPONENT MODELS

  • Lee, Jong-Hoo
    • East Asian mathematical journal
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    • v.5 no.1
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    • pp.95-110
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    • 1989
  • In a general variance component model, nonnegative quadratic estimators of the components of variance are considered which are invariant with respect to mean value translaion and have minimum bias (analogously to estimation theory of mean value parameters). Here the minimum is taken over an appropriate cone of positive semidefinite matrices, after having made a reduction by invariance. Among these estimators, which always exist the one of minimum norm is characterized. This characterization is achieved by systems of necessary and sufficient condition, and by a cone restricted pseudoinverse. In models where the decomposing covariance matrices span a commutative quadratic subspace, a representation of the considered estimator is derived that requires merely to solve an ordinary convex quadratic optimization problem. As an example, we present the two way nested classification random model. An unbiased estimator is derived for the mean squared error of any unbiased or biased estimator that is expressible as a linear combination of independent sums of squares. Further, it is shown that, for the classical balanced variance component models, this estimator is the best invariant unbiased estimator, for the variance of the ANOVA estimator and for the mean squared error of the nonnegative minimum biased estimator. As an example, the balanced two way nested classification model with ramdom effects if considered.

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Shrinkage Estimator of Dispersion of an Inverse Gaussian Distribution

  • Lee, In-Suk;Park, Young-Soo
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.3
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    • pp.805-809
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    • 2006
  • In this paper a shrinkage estimator for the measure of dispersion of the inverse Gaussian distribution with known mean is proposed. Also we compare the relative bias and relative efficiency of the proposed estimator with respect to minimum variance unbiased estimator.

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Minimum Variance Unbiased Estimation for the Maximum Entropy of the Transformed Inverse Gaussian Random Variable by Y=X-1/2

  • Choi, Byung-Jin
    • Communications for Statistical Applications and Methods
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    • v.13 no.3
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    • pp.657-667
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    • 2006
  • The concept of entropy, introduced in communication theory by Shannon (1948) as a measure of uncertainty, is of prime interest in information-theoretic statistics. This paper considers the minimum variance unbiased estimation for the maximum entropy of the transformed inverse Gaussian random variable by $Y=X^{-1/2}$. The properties of the derived UMVU estimator is investigated.

Comparison of Two Parametric Estimators for the Entropy of the Lognormal Distribution (로그정규분포의 엔트로피에 대한 두 모수적 추정량의 비교)

  • Choi, Byung-Jin
    • Communications for Statistical Applications and Methods
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    • v.18 no.5
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    • pp.625-636
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    • 2011
  • This paper proposes two parametric entropy estimators, the minimum variance unbiased estimator and the maximum likelihood estimator, for the lognormal distribution for a comparison of the properties of the two estimators. The variances of both estimators are derived. The influence of the bias of the maximum likelihood estimator on estimation is analytically revealed. The distributions of the proposed estimators obtained by the delta approximation method are also presented. Performance comparisons are made with the two estimators. The following observations are made from the results. The MSE efficacy of the minimum variance unbiased estimator appears consistently high and increases rapidly as the sample size and variance, n and ${\sigma}^2$, become simultaneously small. To conclude, the minimum variance unbiased estimator outperforms the maximum likelihood estimator.

Estimating reliability in discrete distributions

  • Moon, Yeung-Gil;Lee, Chang-Soo
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.4
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    • pp.811-817
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    • 2011
  • We shall introduce a general probability mass function which includes several discrete probability mass functions. Especially, when the random variable X is Poisson, binomial, and negative binomial random variables as some special cases of the introduced distribution, the maximum likelihood estimator (MLE) and the uniformly minimum variance unbiased estimator (UMVUE) of the probability P(X ${\leq}$ t) are considered. And the efficiencies of the MLE and the UMVUE of the reliability ar compared each other.

Estimation of Pr(X>Y) in the case of Exponential X and Normal Y

  • Kim, Jae-Joo;Kim, Hwan-Joong
    • Journal of the Korean Society for Quality Management
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    • v.15 no.2
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    • pp.27-37
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    • 1987
  • In life testing problem, many authors obtained the minimum variance unbiased estimator of $P_r$[X>Y] for the exponential family generally and conceptually. In this paper, we study the maximum likelihood estimator and minimum variance unbiased estimator of $P_r$[X>Y] in exponential X and normal Y.

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On Optimal Estimates of System Reliability (시스템 신뢰성(信賴性)의 최적추정(最適推定))

  • Kim, Jae-Ju
    • Journal of the Korean Society for Quality Management
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    • v.7 no.2
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    • pp.7-10
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    • 1979
  • In this paper the Rao-Blackwell and Lehmann-$Scheff{\acute{e}}$ Theorem are used to drive the minimum variance unbiased estimators of system reliability for a number of distributions when a system consists of n Components whose random life times are assumed to be independent and identically distributed. For the case of a negative exponential life time, we obtain the maximum likelihood estimator of the system reliability and compair it with minimum variance unbiased estimator of the system reliability.

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Estimation of Pr(Y < X) in the Censored Case

  • Kim, Jae Joo;Yeum, Joon Keun
    • Journal of the Korean Society for Quality Management
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    • v.12 no.1
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    • pp.9-16
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    • 1984
  • We study some estimation of the ${\theta}=P_r$(Y${\theta}$. We consider asymptotic property of estimators and maximum likelihood estimator is compared with unique minimum veriance unbiased estimator in moderate sample size.

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Estimation of Normal Variance Considered Prior Information

  • Lee, Sang-do;Lee, Dong-choon;Park, Ki-joo
    • Journal of the Korean Society for Quality Management
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    • v.17 no.2
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    • pp.55-63
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    • 1989
  • In this paper we present the shrunken testing estimator for the variance of normal population and we find the condition that can be used in seeking the situations in which the proposed estimator is superior to the minimum variance unbiased estimator.

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Multi-Level Rotation Designs for Unbiased Generalized Composite Estimator

  • Park, You-Sung;Choi, Jai-Won;Kim, Kee-Whan
    • Proceedings of the Korean Statistical Society Conference
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    • pp.123-130
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    • 2003
  • We define a broad class of rotation designs whose monthly sample is balanced in interview time, level of recall, and rotation group, and whose rotation scheme is time-invariant. The necessary and sufficient conditions are obtained for such designs. Using these conditions, we derive a minimum variance unbiased generalized composite estimator (MVUGCE). To examine the existence of time-in-sample bias and recall bias, we also propose unbiased estimators and their variances. Numerical examples investigate the impacts of design gap, non-sampling error sources, and two types of correlations on the variance of MVUGCE.

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