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1.
Summary For estimating the mean of ap-variate normal distribution under a quadratic loss, a class of estimators, known as Stein's estimators, is known to dominate
the maximum likelihood estimator (MLE) forp≧3. But, whereas the risk of the MLE has the same value, equal to a constant, for each component, the maximum component risk
of Stein's estimator is large for large values ofp. Certain modification of Stein's rule has been proposed in the literature for reducing the maximum component risk. In this
paper, a new rule is given for reducing the maximum component risk. The new rule yields larger reduction in the maximum component
risk, compared to its competitor. 相似文献
2.
Yuzo Maruyama William E. Strawderman 《Annals of the Institute of Statistical Mathematics》2005,57(1):157-165
This paper develops necessary conditions for an estimator to dominate the James-Stein estimator and hence the James-Stein
positive-part estimator. The ultimate goal is to find classes of such dominating estimators which are admissible. While there
are a number of results giving classes of estimators dominating the James-Stein estimator, the only admissible estimator known
to dominate the James-Stein estimator is the generalized Bayes estimator relative to the fundamental harmonic function in
three and higher dimension. The prior was suggested by Stein and the domination result is due to Kubokawa. Shao and Strawderman
gave a class of estimators dominating the James-Stein positive-part estimator but were unable to demonstrate admissiblity
of any in their class. Maruyama, following a suggestion of Stein, has studied generalized Bayes estimators which are members
of a point mass at zero and a prior similar to the harmonic prior. He finds a subclass which is minimax and admissible but
is unable to show that any in his class with positive point mass at zero dominate the James-Stein estimator. The results in
this paper show that a subclass of Maruyama's procedures including the class that Stein conjectured might contain members
dominating the James-Stein estimator cannot dominate the James-Stein estimator. We also show that under reasonable conditions,
the “constant” in shrinkage factor must approachp-2 for domination to hold. 相似文献
3.
Yutaka Kano 《Annals of the Institute of Statistical Mathematics》1986,38(1):57-68
Summary This paper is concerned with the consistency of estimators in a single common factor analysis model when the dimension of
the observed vector is not fixed. In the model several conditions on the sample sizen and the dimensionp are established for the least squares estimator (L.S.E.) to be consistent. Under some assumptions,p/n→0 is a necessary and sufficient condition that the L.S.E. converges in probability to the true value. A sufficient condition
for almost sure convergence is also given. 相似文献
4.
or the variance parameter of the normal distribution with a normal-inverse-gamma prior, we analytically calculate the Bayes posterior estimator with respect to a conjugate normal-inverse-gamma prior distribution under Stein's loss function. This estimator minimizes the Posterior Expected Stein's Loss (PESL). We also analytically calculate the Bayes posterior estimator and the PESL under the squared error loss function. The numerical simulations exemplify our theoretical studies that the PESLs do not depend on the sample, and that the Bayes posterior estimator and the PESL under the squared error loss function are unanimously larger than those under Stein's loss function. Finally, we calculate the Bayes posterior estimators and the PESLs of the monthly simple returns of the SSE Composite Index. 相似文献
5.
Toyoaki Akai 《Annals of the Institute of Statistical Mathematics》1989,41(3):485-502
Simultaneous estimation of normal means is considered for observations which are classified into several groups. In a one-way classification case, it is shown that an adaptive shrinkage estimator dominates a Stein-type estimator which shrinks observations towards individual class averages as Stein's (1966,Festschrift for J. Neyman, (ed. F. N. David), 351–366, Wiley, New York) does, and is minimax even if class sizes are small. Simulation results under quadratic loss show that it is slightly better than Stein's (1966) if between variances are larger than within ones. Further this estimator is shown to improve on Stein's (1966) with respect to the Bayes risk. Our estimator is derived by assuming the means to have a one-way classification structure, consisting of three random terms of grand mean, class mean and residual. This technique can be applied to the case where observations are classified into a two-stage hierarchy. 相似文献
6.
June Luo 《Statistics & probability letters》2012,82(1):58-62
The linear model with a growing number of predictors arises in many contemporary scientific endeavor. In this article, we consider the commonly used ridge estimator in linear models. We propose analyzing the ridge estimator for a finite sample size n and a growing dimension p. The existence and asymptotic normality of the ridge estimator are established under some regularity conditions when p→∞. It also occurs that a strictly linear model is inadequate when some of the relations are believed to be of certain linear form while others are not easily parameterized, and thus a semiparametric partial linear model is considered. For these semiparametric partial linear models with p>n, we develop a procedure to estimate the linear coefficients as if the nonparametric part is not present. The asymptotic efficiency of the proposed estimator for the linear component is studied for p→∞. It is shown that the proposed estimator of the linear component asymptotically performs very well. 相似文献
7.
The Šilov boundary of a Siegel domain of type II is equivalent to a 2-step nilpotent Lie group. In this paper, we mainly study
the Lp-boundness of the Hilbert integrals on Siegel domains of type II by using F. Ricci and E.M. Stein's result about singular
integrals on Lie groups[1]. This is the generalization of part of the work done by P.H.Phong and E.M.Stein in [2] 相似文献
8.
In this paper, we continue the investigation of an estimator proposed in [Yu. Davydov, V. Paulauskas, and A. Račkauskas, More
on p-stable convex sets in Banach spaces, J. Theor. Probab., 13:39–64, 2000] and [V. Paulauskas, A new estimator for tail index, Acta Appl. Math., 79:55–67, 2003] and considered in [V. Paulauskas and M. Vaičiulis, Once more on comparison of tail index estimators, preprint, 2010]. We propose a class of modifications of the so-called DPR estimator and demonstrate that these modifications can have better
asymptotic properties than the original DPR estimator. 相似文献
9.
In this article, we consider the problem of estimating a p-variate (p ≥ 3) normal mean vector in a decision-theoretic setup. Using a simple property of the noncentral chi-square distribution, we have produced a sequence of smooth estimators dominating the James-Stein estimator and each improved estimator is better than the previous one. It is also shown by using a technique of [5]. J. Multivariate Anal.36 121–126) that our smooth estimators can be dominated by non-smooth estimators. 相似文献
10.
Mark Ainsworth 《Numerical Methods for Partial Differential Equations》1994,10(5):609-623
The error estimator proposed by Bank and Weiser is analyzed in the case of degree p finite element approximations on quadrilateral meshes. It is shown that the scheme is asymptotically exact in the energy norm for regular solutions provided that the degree of approximation is of odd order and the elements are rectangles. Perhaps surprisingly, the hypotheses are necessary, as counterexamples show. © 1994 John Wiley & Sons, Inc. 相似文献
11.
Yoshihiko Maesono Spiridon Penev 《Annals of the Institute of Statistical Mathematics》2011,63(3):617-644
Using the kernel estimator of the pth quantile of a distribution brings about an improvement in comparison to the sample quantile estimator. The size and order
of this improvement is revealed when studying the Edgeworth expansion of the kernel estimator. Using one more term beyond
the normal approximation significantly improves the accuracy for small to moderate samples. The investigation is non- standard
since the influence function of the resulting L-statistic explicitly depends on the sample size. We obtain the expansion, justify its validity and demonstrate the numerical
gains in using it. 相似文献
12.
A good robust functional should, if possible, be efficient at the model, smooth, and have a high breakdown point. M-estimators can be made efficient and Fréchet differentiable by choosing appropriate ψ-functions but they have a breakdown point of at most 1/(p + 1) in p dimensions. On the other hand, the local smoothness of known high breakdown functionals has not been investigated. It is known that Rousseeuw's minimum volume ellipsoid estimator is not differentiable and that S-estimators based on smooth functions force a trade-off between efficiency and breakdown point. However, by using a two-step M-estimator based on the minimum volume ellipsoid we show that it is possible to obtain a highly efficient, Fréchet differentiable estimator whilst still retaining the breakdown point. This result is extended to smooth S-estimators. 相似文献
13.
Hsiuying Wang 《Journal of multivariate analysis》2004,90(2):1361-281
In the hypothesis testing problem, a most common used evidence against the null hypothesis is the p-value. Although there have been many Bayesian criticisms leveled at p-value, Hwang et al. (Ann. Statist. 20 (1992), 490) show the adequacy of using p-value as evidence against the null hypothesis by considering testing as an estimation problem. However, when the parameter space is not the natural space, Woodroofe and Wang (Ann. Statist. 28 (2000) 1561) show that the usual p-value derived by the N–P test is not appropriate to be the evidence against the null hypothesis for the Poisson distribution from an estimation point of view and provide a modified p-value. Although this modified p-value is admissible, it is not the admissible estimator which can dominate the usual p-value. In this paper, we concentrate on the simple hypothesis versus simple alternative hypothesis testing problem. Admissible estimators which dominate the usual p-value are provided. 相似文献
14.
In this paper, we propose an exponential ratio type estimator of the finite population mean when auxiliary information is qualitative in nature. Under simple random sampling without replacement scheme, the expressions for the bias and the mean square error of the proposed estimator have been obtained, up to first order of approximation. To show that our proposed estimator is more efficient as compared to the existing estimators, we have made a comparative study with respect to their mean square errors. Theoretically and numerically, we have found that our proposed estimator is always more efficient as compared to its competitor estimators including all the estimators of Abd-Elfattah et al. [1] [A.M. Abd-Elfattah, E.A. El-Sherpieny, S.M. Mohamed, and O.F. Abdou. Improvement in estimating the population mean in simple random sampling using information on auxiliary attribute. Applied Mathematics and Computation, 215 (2010), 4198-4202]. 相似文献
15.
Summary The problem is to estimate the mean of ap-dimensional normal distribution in the situation where there is vague information that the mean vector might be equal to
zero vector. Minimax property of the preliminary test estimator obtained by the use of AIC (Akaike's Information Criterion)
procedure is discussed under a loss function which is based on Kullback-Leibler information measure and evaluates both an
error of model selection and that of estimation. Whenp is even, the minimaxity is shown to hold for small values ofp but not for large values. 相似文献
16.
The best-r-point-average (BRPA) estimator of the maximizer of a regression function, proposed in Changchien (in: M.T. Chao, P.E. Cheng (Eds.), Proceedings of the 1990 Taipei Symposium in Statistics, June 28–30, 1990, pp. 63–78) has certain merits over the estimators derived through the estimation of the regression function. Some of the properties of the BRPA estimator have been studied in Chen et al. (J. Multivariate Anal. 57 (1996) 191) and Bai and Huang (Sankhya: Indian J. Statist. Ser. A. 61 (Pt. 2) (1999) 208–217). In this article, we further study the properties of the BRPA estimator and give its convergence rate under some quite general conditions. Simulation results are presented for the illustration of the convergence rate. Some comparisons with existing estimators such as the Müller estimator are provided. 相似文献
17.
Ian S. Abramson 《Journal of multivariate analysis》1982,12(4):562-567
Consider estimating a smooth p-variate density f at 0 using the classical kernel estimator fn(0) = n−1 Σibn−pw(bn−1Xi) based on a sample {Xi} from f. Under familiar conditions, assigning bn = bn−1/(4 + p) gives the best MSE decay rate O(n−4/(4 + p), but the optimal b, b* say, depends on f through its second derivatives, raising a feasibility objection to its use. By prescribing a pilot estimate of b* based on the same sample, Woodroofe has shown that there need be asymptotically no loss as against knowing the constant exactly, but his proposal is critically dependent on achieving a certain consistency rate for b*. Admitting a minor change in the risk function, we show by a tightness argument applied to the error process that any consistent estimator of b* may be used to achieve the same performance. 相似文献
18.
For the problem of estimating under squared error loss the location parameter of a p-variate spherically symmetric distribution where the location parameter lies in a ball of radius m, a general sufficient condition for an estimator to dominate the maximum likelihood estimator is obtained. Dominance results are then made explicit for the case of a multivariate student distribution with d degrees of freedom and, in particular, we show that the Bayes estimator with respect to a uniform prior on the boundary of the parameter space dominates the maximum likelihood estimator whenever and d?p. The sufficient condition matches the one obtained by Marchand and Perron (Ann. Statist. 29 (2001) 1078) in the normal case with identity covariance matrix. Furthermore, we derive an explicit class of estimators which, for , dominate the maximum likelihood estimator simultaneously for the normal distribution with identity covariance matrix and for all multivariate student distributions with d degrees of freedom, d?p. Finally, we obtain estimators which dominate the maximum likelihood estimator simultaneously for all distributions in the subclass of scale mixtures of normals for which the scaling random variable is bounded below by some positive constant with probability one. 相似文献
19.
A residual based a posteriori estimator for the reaction-diffusion problem is introduced. We show that the estimator gives both an upper and a lower bound to error. Numerical results are presented. To cite this article: M. Juntunen, R. Stenberg, C. R. Acad. Sci. Paris, Ser. I 347 (2009). 相似文献
20.
Summary In the problem of estimating the covariance matrix of a multivariate normal population, James and Stein (Proc. Fourth Berkeley Symp. Math. Statist. Prob.,1, 361–380, Univ. of California Press) obtained a minimax estimator under a scale invariant loss. In this paper we propose
an orthogonally invariant trimmed estimator by solving certain differential inequality involving the eigenvalues of the sample
covariance matrix. The estimator obtained, truncates the extreme eigenvalues first and then shrinks the larger and expands
the smaller sample eigenvalues. Adaptive version of the trimmed estimator is also discussed. Finally some numerical studies
are performed using Monte Carlo simulation method and it is observed that the trimmed estimate shows a substantial improvement
over the minimax estimator.
The second author's research was supported by NSF Grant Number MCS 82-12968. 相似文献