协方差的非齐次估计的可容许性

本文研究协方差的非齐次二次估计的可容许性,在平方损失下,我们给出了一个非齐次二次估计在非齐次二次估计类中是协方差的容许估计的充要条件.     

椭球约束下误差方差非负二次估计的可容许性

本文给出了线性模型中椭球约束下，误差方差非负二次估计可容许的一个必要条件，并且，对于线性模型中设计阵X＝1nj=(1,1,…，1）′的特殊情形，本文给出了一个充要条件。     

协方差的二次型容许估计

本文研究方差的二次型估计的容许性,在平方损失下,我们给出了一个二次型估计在二次型估计类中是协方差的容许估计的充要条件。     

平衡损失下一般Gauss—Markoff模型中回归系数的非齐次线性可容许估计

提出了适用于—般Gauss-Markoff模型的平衡损失函数,并在该平衡损失下,研究了模型中回归系数的非齐次线性估计在非齐次线性估计类中的可容许性,得到了充要条件．     

矩阵损失函数下回归系数矩阵的非齐次线性估计的可容许性

在二次矩阵损失函数下研究了协方差矩阵未知的多元线性模型中回归系数矩阵的可估线性函数的矩阵非齐次线性估计的可容许性,给出了矩阵非齐次线性估计在线性估计类中可容许的一个充要条件.     

方差分量模型中回归系数的线性估计的可容许性

考虑方差分量模型$\ep Y=X\beta,\;\cov(Y)=\tsm_{i=1}^{m}\theta_iV_i$, 其中$n\times p$矩阵$X$和非负定矩阵$V_i\;(i=1,2,\cdots,m)$都是已知的, $\beta\in R^p,\;\theta_i\geq 0$或$\theta_i>0\;(i=1,2,\cdots,m)$均为参数\bd 在本文中, 我们在二次损失下, 当$\mu{(X)} \subset\mu{(V)}$时, 给出了关于可估函数$S\beta$的线性估计在线性估计类中可容许性的充要条件     

两个方差分量的不变二次估计的可容许性

本文对Ｙ￣Ｎ（Ｘβ，θ１Ｖ１＋θ２Ｖ２），Ｖ１，Ｖ２〉０给出了方差分量的线性函数的不变二次无偏估计、不变二次非负估计及不变二次估计不可容许的充要条件，并据此给出了具体判别上述估计的可容许性的方法。     

矩阵损失下带约束的回归系数非齐次线性估计的Minimax可容许性的特征

本文在矩阵损失下给出了带约束的回归系数线性估计在非齐次线性估计类中是Minimax可容许估计的充要条件。     

常用正态协方差阵估计的非容许性

在二次损失函数下,本文给出了正态方差最优同变估计的一个新的改进估计,并证明了常用正态协方差和协方差阵的估计都是非容许估计。     

许—模型下误差方差的可容许估计

本文讨论了在许－模型下非负二次估计类中误差方差的可容许估计的条件。     

Some modifications of improved estimators of a normal variance

Consider the problem of estimating a normal variance based on a random sample when the mean is unknown. Scale equivariant estimators which improve upon the best scale and translation equivariant one have been proposed by several authors for various loss functions including quadratic loss. However, at least for quadratic loss function, improvement is not much. Herein, some methods are proposed to construct improving estimators which are not scale equivariant and are expected to be considerably better when the true variance value is close to the specified one. The idea behind the methods is to modify improving equivariant shrinkage estimators, so that the resulting ones shrink little when the usual estimate is less than the specified value and shrink much more otherwise. Sufficient conditions are given for the estimators to dominate the best scale and translation equivariant rule under the quadratic loss and the entropy loss. Further, some results of a Monte Carlo experiment are reported which show the significant improvements by the proposed estimators.     

Asymptotic variance estimation in multivariate distributions

A version of an asymptotic estimation problem of the unknown variance in a multivariate location-scale parameter family is studied under a general loss function. The asymptotic inadmissibility of the traditional estimator is established. In a particular case we derive an admissible improvement on this estimator.     

Linear combinations of overlapping variance estimators for simulation

We estimate the variance parameter of a stationary simulation-generated process using a linear combination of overlapping standardized time series (STS) area variance estimators based on different batch sizes. We establish the linear-combination estimator's asymptotic distribution, presenting analytical and simulation-based results exemplifying its potential for improvements in accuracy and computational efficiency.     

QUADRATIC ESTIMATORS OF QUADRATIC FUNCTIONS WITH PARAMETERS IN NORMAL LINEAR MODELS

ＱＵＡＤＲＡＴＩＣＥＳＴＩＭＡＴＯＲＳＯＦＱＵＡＤＲＡＴＩＣＦＵＮＣＴＩＯＮＳＷＩＴＨＰＡＲＡＭＥＴＥＲＳＩＮＮＯＲＭＡＬＬＩＮＥＡＲＭＯＤＥＬＳ￥ＷＵＱＩＧＵＡＮＧ（吴启光）（ＩｎｓｔｉｔｕｔｅｏｆＳｙｓｔｅｍｅＳｃｉｅｎｃｅ,ｔｈｅＣｈｉｎｅｓｅ...     

General admissibility for linear estimators in a general multivariate linear model under balanced loss function

A generalization of Zellner’s balanced loss function is proposed. General admissibility in a general multivariate linear model is investigated under the generalized balanced loss function. And the sufficient and necessary conditions for linear estimators to be generally admissible in classes of homogeneous and nonhomogeneous linear estimators are given, respectively.     

Folded overlapping variance estimators for simulation

We propose and analyze a new class of estimators for the variance parameter of a steady-state simulation output process. The new estimators are computed by averaging individual estimators from “folded” standardized time series based on overlapping batches composed of consecutive observations. The folding transformation on each batch can be applied more than once to produce an entire set of estimators. We establish the limiting distributions of the proposed estimators as the sample size tends to infinity while the ratio of the sample size to the batch size remains constant. We give analytical and Monte Carlo results showing that, compared to their counterparts computed from nonoverlapping batches, the new estimators have roughly the same bias but smaller variance. In addition, these estimators can be computed with order-of-sample-size work.     

Minimaxity and admissibility of the product limit estimator

In this article we examine the minimaxity and admissibility of the product limit (PL) estimator under the loss function% MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqaqpepeea0xe9qqVa0l% b9peea0lb9sq-JfrVkFHe9peea0dXdarVe0Fb9pgea0xa9pue9Fve9% Ffc8meGabaqaciGacaGaaeqabaWaaeaaeaaakeaacaWGmbGaaiikai% aadAeacaGGSaGabmOrayaajaGaaiykaiabg2da9maapeaabaGaaiik% aiaadAeacaGGOaGaamiDaiaacMcaaSqabeqaniabgUIiYdGccqGHsi% slceWGgbGbaKaacaGGOaGaamiDaiaacMcacaGGPaWaaWbaaSqabeaa% caaIYaaaaOGaamOramaaCaaaleqabaaccmGae8xSdegaaOGaaiikai% aadshacaGGPaGaaiikaiaaigdacqGHsislcaWGgbGaaiikaiaadsha% caGGPaGaaiykamaaCaaaleqabaGaeqOSdigaaOGaamizaiaadEfaca% GGOaGaamiDaiaacMcaaaa!5992!\[L(F,\hat F) = \int {(F(t)} - \hat F(t))^2 F^\alpha (t)(1 - F(t))^\beta dW(t)\].To avoid some pathological and uninteresting cases, we restrict the parameter space to ={F: F(ymin) }, where (0, 1) and y ₁,...y,_n are the censoring times. Under this set up, we obtain several interesting results. When y ₁=···=y _n, we prove the following results: the PL estimator is admissible under the above loss function for , {–1, 0}; if n=1, ==–1, the PL estimator is minimax iff dW ({y})=0; and if n2, , {–1, 0}, the PL estimator is not minimax for certain ranges of . For the general case of a random right censorship model it is shown that the PL estimator is neither admissible nor minimax. Some additional results are also indicated.Partially supported by the Governor's Challenge Grant.Part of the work was done while the author was visiting William Paterson College.     

A class of minimax estimators of a normal quantile

The estimation problem of the quantiles of a normal distribution with both parameters unknown, is considered. We construct a class of minimax procedures each of which improves upon the traditional (best equivariant) estimator of a quantile different from the median. For this purpose a differential inequality and a family of its solutions is found.