The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Nobuo Shinozaki - One of the best experts on this subject based on the ideXlab platform.
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optimal binning strategies under Squared Error Loss in selective assembly with a tolerance constraint
Communications in Statistics-theory and Methods, 2010Co-Authors: Shun Matsuura, Nobuo ShinozakiAbstract:Selective assembly is an effective approach for improving the quality of a product assembled from two types of components when the quality characteristic is the clearance between the mating components. In this article, optimal binning strategies under Squared Error Loss in selective assembly when the clearance is constrained by a tolerance parameter are discussed. Conditions for a set of constrained optimal partition limits are given, and uniqueness of this set is shown for the case when the dimensional distributions of the two components are identical and strongly unimodal. Some numerical results are reported that compare constrained optimal partitioning, unconstrained optimal partitioning, and equal width partitioning.
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optimal binning strategies under Squared Error Loss in selective assembly with measurement Error
Communications in Statistics-theory and Methods, 2007Co-Authors: Shun Matsuura, Nobuo ShinozakiAbstract:Selective assembly is an effective approach for improving a quality of a product assembled from two types of components, when the quality characteristic is the clearance between the mating components. Mease et al. (2004) have extensively studied optimal binning strategies under Squared Error Loss in selective assembly, especially for the case when two types of component dimensions are identically distributed. However, the presence of measurement Error in component dimensions has not been addressed. Here we study optimal binning strategies under Squared Error Loss when measurement Error is present. We give the equations for the optimal partition limits minimizing expected Squared Error Loss, and show that the solution to them is unique when the component dimensions and the measurement Errors are normally distributed. We then compare the expected Losses of the optimal binning strategies with and without measurement Error for normal distribution, and also evaluate the influence of the measurement Error.
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Estimation of Ordered Means of Two Poisson Distributions
Communications in Statistics-theory and Methods, 2006Co-Authors: Yuan-tsung Chang, Nobuo ShinozakiAbstract:In this article, we discuss the estimation of linear functions of two Poisson means, on which an order restriction is given. We give a necessary and sufficient condition on the coefficients of the linear function for the maximum likelihood estimator (MLE) which satisfies the order restriction to dominate the unbiased estimator under Squared Error Loss. Furthermore, simultaneous estimation of two ordered Poisson means is considered and we suggest the Clevenson–Zidek type modification of MLE which dominates the MLE under normalized Squared Error Loss. We also improve the estimator proposed by Clevenson and Zidek (1975) which ignores the order restriction.
Constance Van Eeden - One of the best experts on this subject based on the ideXlab platform.
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comparisons of the performances of estimators of a bounded normal mean under Squared Error Loss
2009Co-Authors: Constance Van EedenAbstract:This paper is concerned with the estimation under Squared-Error Loss of a normal meanbased on X ∼ N(�,1) when |�| ≤ m for a known m > 0. Nine estimators are compared, namely the maximum likelihood estimator (mle), three dominators of the mle obtained from Moors, from Charras and from Charras and van Eeden, two minimax estimators from Casella and Strawderman, a Bayes estimator of Marchand and Perron, the Pitman estimator and Bickel's asymptotically-minimax estimator. The comparisons are based on analytical as well as on graphical results concerning their risk functions. In particular, we comment on their gain in accuracy from using the restriction, as well as on their robustness with respect to misspecification of m.
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minimax estimation of a lower bounded scale parameter of a gamma distribution for scale invariant Squared Error Loss
Canadian Journal of Statistics-revue Canadienne De Statistique, 1995Co-Authors: Constance Van EedenAbstract:Let X have a gamma distribution with known shape parameter θr;aL and unknown scale parameter θ. Suppose it is known that θ ≥ a for some known a > 0. An admissible minimax estimator for scale-invariant Squared-Error Loss is presented. This estimator is the pointwise limit of a sequence of Bayes estimators. Further, the class of truncated linear estimators C = {θρ|θρ(x) = max(a, ρ), ρ > 0} is studied. It is shown that each θρ is inadmissible and that exactly one of them is minimax. Finally, it is shown that Katz's [Ann. Math. Statist., 32, 136–142 (1961)] estimator of θ is not minimax for our Loss function. Some further properties of and comparisons among these estimators are also presented. Soit X, ayant une distribution gamma avec un parametre de forme connu αaL et un parametre d'echelle inconnu θ. Supposons qu'il est accepte que θ ≥ a pour un αaL connu positif. Cet article presente un estimateur minimax admissible par rapport a la fonction de perte a l'erreur quadratique invariante en changement d'echelle. Cet estimateur est la limite ponctuelle d'une suite d'estimateurs de Bayes. De plus, la classe des estimateurs lineaires tronques C = {θρ|θρ(x) = max(a, ρx), ρ > 0} est etudiee. Il est demontre que chaque θρ est inadmissible et qu'exactement un d'entre eux est minimax. Enfin, il est demontre que l'estimateur de Katz [Ann. Math. Statist., 32, 136–142 (1961)] de θ n'est pas minimax pour notre fonction de perte. Quelques autres proprietes ainsi que des comparaisons entre ces estimateurs sont presentees.
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Minimax estimation of a lower‐bounded scale parameter of a gamma distribution for scale‐invariant Squared‐Error Loss
Canadian Journal of Statistics-revue Canadienne De Statistique, 1995Co-Authors: Constance Van EedenAbstract:Let X have a gamma distribution with known shape parameter θr;aL and unknown scale parameter θ. Suppose it is known that θ ≥ a for some known a > 0. An admissible minimax estimator for scale-invariant Squared-Error Loss is presented. This estimator is the pointwise limit of a sequence of Bayes estimators. Further, the class of truncated linear estimators C = {θρ|θρ(x) = max(a, ρ), ρ > 0} is studied. It is shown that each θρ is inadmissible and that exactly one of them is minimax. Finally, it is shown that Katz's [Ann. Math. Statist., 32, 136–142 (1961)] estimator of θ is not minimax for our Loss function. Some further properties of and comparisons among these estimators are also presented. Soit X, ayant une distribution gamma avec un parametre de forme connu αaL et un parametre d'echelle inconnu θ. Supposons qu'il est accepte que θ ≥ a pour un αaL connu positif. Cet article presente un estimateur minimax admissible par rapport a la fonction de perte a l'erreur quadratique invariante en changement d'echelle. Cet estimateur est la limite ponctuelle d'une suite d'estimateurs de Bayes. De plus, la classe des estimateurs lineaires tronques C = {θρ|θρ(x) = max(a, ρx), ρ > 0} est etudiee. Il est demontre que chaque θρ est inadmissible et qu'exactement un d'entre eux est minimax. Enfin, il est demontre que l'estimateur de Katz [Ann. Math. Statist., 32, 136–142 (1961)] de θ n'est pas minimax pour notre fonction de perte. Quelques autres proprietes ainsi que des comparaisons entre ces estimateurs sont presentees.
X U Junmei - One of the best experts on this subject based on the ideXlab platform.
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bayes estimation of poisson distribution parameter under scale Squared Error Loss function
Journal of Lanzhou University of Technology, 2008Co-Authors: X U JunmeiAbstract:The Bayes estimation of the Poisson distribution parameter under scale Squared Error Loss function and its admissibility were investigated for a Poisson distribution X1,X2,…,Xn with given capacity as n.The expression of Bayes estimation for multiplayer parameters was given,also.
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bayes estimation of pareto distribution paremeter under the scaled Squared Error Loss function
Journal of Jishou University, 2008Co-Authors: X U JunmeiAbstract:The Bayesian estimation of Pareto Distribution parameter under the scaled Squared Error Loss function is studied,and the Pareto random sample X1,X2,…,Xn is given.It is proved that the Bayesian estimation is admissible.
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bayes estimation of gamma distribution paremeter under the scaled Squared Error Loss function
Journal of Tonghua Teachers College, 2007Co-Authors: X U JunmeiAbstract:In this article,the Bayes estimation of gamma distribution parameter under the scaled Squared Error Loss function is studied, the sample of gamma distribution x1,x2,…xn is given,it is proved that the Bayes estimation is admissible,and the Bayes interval estimation of unknown parameter is given.
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bayesian estimation of pascal distribution parameter under the scaled Squared Error Loss function
Journal of Bohai University, 2007Co-Authors: X U JunmeiAbstract:This paper covers the Bayesian estimation of pascal distribution parameter and its admissibili- ty.It is also proposed that the bayes lowers confidence limit and the exact form of multilayer Bayesian estima- tion.
Akimichi Takemura - One of the best experts on this subject based on the ideXlab platform.
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Admissible estimator of the eigenvalues of the variance-covariance matrix for multivariate normal distributions
Journal of Multivariate Analysis, 2011Co-Authors: Yo Sheena, Akimichi TakemuraAbstract:An admissible estimator of the eigenvalues of the variance-covariance matrix is given for multivariate normal distributions with respect to the scale-invariant Squared Error Loss.
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Admissible Estimator of the Eigenvalues of Variance-Covariance Matrix for Multivariate Normal Distributions--Detailed Proof--
arXiv: Statistics Theory, 2009Co-Authors: Yo Sheena, Akimichi TakemuraAbstract:An admissible estimator of the eigenvalues of the variance-covariance matrix is given for multivariate normal distributions with respect to the scale-invariant Squared Error Loss.
Ashok Shanubhogue - One of the best experts on this subject based on the ideXlab platform.
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Estimating After Selection in the Discrete Exponential Family under k−Normalized Squared Error Loss Function
2020Co-Authors: Riyadh R. Al-mosawi, Ashok ShanubhogueAbstract:Let p(p ≥ 2) be independent discrete exponential family populations with unknown parameter θi,1 ≤ i ≤ p. Suppose from each population we have an observation Xi. The population corresponding to the largest θi, is the best population. To select a population out of these populations, we consider the natural selection rule which selects the population having the largest Xi and randomly selects in case of ties. Our aim in the paper is to estimate the parameter of the selected population under k−normalized Squared Error Loss function for the important special cases k =0 ,1, and 2. The natural estimator is shown to be risk-biased using Lehmann’s (1951) risk−unbiased concept. For the average worth of the parameters of tied populations with the largest Xi, the UMV U E and UMRUE are also derived. Some applications are presented for the selected Poisson, and negative binomial populations. Mathematics Subject Classification: Primary 62F10; secondary 62F07.
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estimating after selection in the discrete exponential family under k normalized Squared Error Loss function
2010Co-Authors: Riyadh R Almosawi, Ashok ShanubhogueAbstract:Let p(p ≥ 2) be independent discrete exponential family populations with unknown parameter θi,1 ≤ i ≤ p. Suppose from each population we have an observation Xi. The population corresponding to the largest θi, is the best population. To select a population out of these populations, we consider the natural selection rule which selects the population having the largest Xi and randomly selects in case of ties. Our aim in the paper is to estimate the parameter of the selected population under k−normalized Squared Error Loss function for the important special cases k =0 ,1, and 2. The natural estimator is shown to be risk-biased using Lehmann’s (1951) risk−unbiased concept. For the average worth of the parameters of tied populations with the largest Xi, the UMV U E and UMRUE are also derived. Some applications are presented for the selected Poisson, and negative binomial populations. Mathematics Subject Classification: Primary 62F10; secondary 62F07.
