共查询到20条相似文献,搜索用时 31 毫秒
1.
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
2.
Konno Y. 《Journal of multivariate analysis》1995,52(2)
Suppose that we have (n − a) independent observations from Np(0, Σ) and that, in addition, we have a independent observations available on the last (p − c) coordinates. Assuming that both observations are independent, we consider the problem of estimating Σ under the Stein′s loss function, and show that some estimators invariant under the permutation of the last (p − c) coordinates as well as under those of the first c coordinates are better than the minimax estimators of Eaten. The estimators considered outperform the maximum likelihood estimator (MLE) under the Stein′s loss function as well. The method involved here is computation of an unbiased estimate of the risk of an invariant estimator considered in this article. In addition we discuss its application to the problem of estimating a covariance matrix in a GMANOVA model since the estimation problem of the covariance matrix with extra data can be regarded as its canonical form. 相似文献
3.
Many applied problems require a covariance matrix estimator that is not only invertible, but also well-conditioned (that is, inverting it does not amplify estimation error). For large-dimensional covariance matrices, the usual estimator—the sample covariance matrix—is typically not well-conditioned and may not even be invertible. This paper introduces an estimator that is both well-conditioned and more accurate than the sample covariance matrix asymptotically. This estimator is distribution-free and has a simple explicit formula that is easy to compute and interpret. It is the asymptotically optimal convex linear combination of the sample covariance matrix with the identity matrix. Optimality is meant with respect to a quadratic loss function, asymptotically as the number of observations and the number of variables go to infinity together. Extensive Monte Carlo confirm that the asymptotic results tend to hold well in finite sample. 相似文献
4.
Delete-group Jackknife Estimate in
Partially Linear Regression Models with Heteroscedasticity 总被引:3,自引:0,他引:3
Abstract Consider a partially linear regression model with an unknown vector parameter β,an unknownfunction g(.),and unknown heteroscedastic error variances.Chen,You proposed a semiparametric generalizedleast squares estimator(SGLSE)for β,which takes the heteroscedasticity into account to increase efficiency.Forinference based on this SGLSE,it is necessary to construct a consistent estimator for its asymptotic covariancematrix.However,when there exists within-group correlation, the traditional delta method and the delete-1jackknife estimation fail to offer such a consistent estimator.In this paper, by deleting grouped partial residualsa delete-group jackknife method is examined.It is shown that the delete-group jackknife method indeed canprovide a consistent estimator for the asymptotic covariance matrix in the presence of within-group correlations.This result is an extension of that in[21]. 相似文献
5.
We consider the problem of estimating the confidence statement of the usual confidence set, with confidence coefficient 1?α, of the mean of a p-variate normal distribution with identity covariance matrix. For p?5, we give an explicit sufficient condition for domination over the standard estimator 1?α by an estimator correcting it, that is, by 1?α+s where s is a suitable function. That condition mainly relies on a partial differential inequality of the form kΔs+s2?0 (for a certain constant k>0). It allows us to formally establish (with no recourse to simulations) this domination result. To cite this article: D. Fourdrinier, P. Lepelletier, C. R. Acad. Sci. Paris, Ser. I 337 (2003). 相似文献
6.
This paper is a continuation of the work in [11] and [2] on the problem of estimating by a linear estimator, N unobservable input vectors, undergoing the same linear transformation, from noise-corrupted observable output vectors. Whereas
in the aforementioned papers, only the matrix representing the linear transformation was assumed uncertain, here we are concerned
with the case in which the second order statistics of the noise vectors (i.e., their covariance matrices) are also subjected to uncertainty. We seek a robust mean-squared error estimator immuned against
both sources of uncertainty. We show that the optimal robust mean-squared error estimator has a special form represented by
an elementary block circulant matrix, and moreover when the uncertainty sets are ellipsoidal-like, the problem of finding
the optimal estimator matrix can be reduced to solving an explicit semidefinite programming problem, whose size is independent
of N.
The research was partially supported by BSF grant #2002038 相似文献
7.
《Journal of computational and graphical statistics》2013,22(4):928-946
Mahalanobis-type distances in which the shape matrix is derived from a consistent, high-breakdown robust multivariate location and scale estimator have an asymptotic chi-squared distribution as is the case with those derived from the ordinary covariance matrix. For example, Rousseeuw's minimum covariance determinant (MCD) is a robust estimator with a high breakdown. However, even in quite large samples, the chi-squared approximation to the distances of the sample data from the MCD center with respect to the MCD shape is poor. We provide an improved F approximation that gives accurate outlier rejection points for various sample sizes. 相似文献
8.
Takaki Hayashi Nakahiro Yoshida 《Annals of the Institute of Statistical Mathematics》2008,60(2):367-406
We consider the problem of estimating the covariance of two diffusion-type processes when they are observed only at discrete
times in a nonsynchronous manner. In our previous work in 2003, we proposed a new estimator which is free of any ‘synchronization’
processing of the original data and showed that it is consistent for the true covariance of the processes as the observation
interval shrinks to zero; Hayashi and Yoshida (Bernoulli, 11, 359–379, 2005). This paper is its sequel. Specifically, it establishes asymptotic normality of the estimator in a general nonsynchronous sampling scheme. 相似文献
9.
Using the Markowitz mean–variance portfolio optimization theory, researchers have shown that the traditional estimated return greatly overestimates the theoretical optimal return, especially when the dimension to sample size ratio p/n is large. Bai et al. (2009) propose a bootstrap-corrected estimator to correct the overestimation, but there is no closed form for their estimator. To circumvent this limitation, this paper derives explicit formulas for the estimator of the optimal portfolio return. We also prove that our proposed closed-form return estimator is consistent when n → ∞ and p/n → y ∈ (0, 1). Our simulation results show that our proposed estimators dramatically outperform traditional estimators for both the optimal return and its corresponding allocation under different values of p/n ratios and different inter-asset correlations ρ, especially when p/n is close to 1. We also find that our proposed estimators perform better than the bootstrap-corrected estimators for both the optimal return and its corresponding allocation. Another advantage of our improved estimation of returns is that we can also obtain an explicit formula for the standard deviation of the improved return estimate and it is smaller than that of the traditional estimate, especially when p/n is large. In addition, we illustrate the applicability of our proposed estimate on the US stock market investment. 相似文献
10.
In this paper, a theorem on the moderate deviation principle for random arrays under m-dependence with unbounded m is established. This partially extends the results of Chen (Stat. Probab. Lett. 35:123–134, 1997). As an application, the moderate deviation principle for the truncation estimator of the variance in the analysis of time
series is obtained.
相似文献
11.
S. N. Elogne O. Perrin C. Thomas-Agnan 《Statistical Inference for Stochastic Processes》2008,11(2):177-205
In this paper we introduce a nonparametric approach for the estimation of the covariance function of a stationary stochastic
process X
t
indexed by The data consist of a finite number of observations of the process at irregularly spaced time points and the aim is to estimate
the covariance at any lag point without parametric assumptions and in such a way that it is a positive definite function.
After interpolating the process, we use the estimator designed by Parzen (Technometrics 3:167–190,1961) for continuous-time
data. Our estimator is shown to be consistent under smoothness assumptions on the covariance. Its performance is evaluated
by simulations. 相似文献
12.
C. S. Withers 《Annals of the Institute of Statistical Mathematics》1991,43(1):147-156
Based on a sample of size n, we investigate a class of estimators of the mean of a p-variate normal distribution with independent components having unknown covariance. This class includes the James-Stein estimator and Lindley's estimator as special cases and was proposed by Stein. The mean squares error improves on that of the sample mean for p3. Simple approximations imations for this improvement are given for large n or p. Lindley's estimator improves on that of James and Stein if either n is large, and the coefficient of variation of is less than a certain increasing function of p, or if p is large. An adaptive estimator is given which for large samples always performs at least as well as these two estimators. 相似文献
13.
This article discusses the problem of parameter estimation with nonlinear mean-reversion type stochastic differential equations (SDEs) driven by Brownian motion for population growth model. The estimator in the population model is the climate effects, population policy and environmental circumstances which affect the intrinsic rate of growth r. The consistency and asymptotic distribution of the estimator θ is studied in our general setting. In the calculation method, unlike previous study, since the nonlinear feature of the model, it is difficult to obtain an explicit formula for the estimator. To solve this, some criteria are used to derive an asymptotically consistent estimator. Furthermore Girsanov transformation is used to simplify the equations, which then gives rise to the corresponding convergence of the estimator being with respect to a family of probability measures indexed by the dispersion parameter, while in the literature the existing results have dealt with convergence with respect to a given probability measure. 相似文献
14.
Michael J. Johnson 《Constructive Approximation》2004,20(2):303-324
We show that the Lp-approximation order of surface spline interpolation
equals m+1/p for p in the range 1 \leq p \leq 2, where m is an integer
parameter which specifies the surface spline. Previously it was known that this
order was bounded below by m + &frac; and above by m+1/p. With
h denoting the fill-distance between the interpolation points and the domain
, we show specifically that the Lp()-norm of the error between f
and its surface spline interpolant is O(hm + 1/p) provided that f belongs
to an appropriate Sobolev or Besov space and that \subset
Rd is open, bounded, and has the C2m-regularity
property. We also show that the boundary effects (which cause the rate of
convergence to be significantly worse than O(h2m)) are confined to a
boundary layer whose width is no larger than a constant multiple of
h |log h|. Finally, we state numerical evidence which supports the
conjecture that the
Lp-approximation order of surface spline interpolation is m + 1/p for
2 < p \leq \infty. 相似文献
15.
Changhua Chen Richard A. Davis Peter J. Brockwell 《Journal of multivariate analysis》1996,57(2):175-190
LetX1, …, Xnbe observations from a multivariate AR(p) model with unknown orderp. A resampling procedure is proposed for estimating the orderp. The classical criteria, such as AIC and BIC, estimate the orderpas the minimizer of the function[formula]wherenis the sample size,kis the order of the fitted model, Σ2kis an estimate of the white noise covariance matrix, andCnis a sequence of specified constants (for AIC,Cn=2m2/n, for Hannan and Quinn's modification of BIC,Cn=2m2(ln ln n)/n, wheremis the dimension of the data vector). A resampling scheme is proposed to estimate an improved penalty factorCn. Conditional on the data, this procedure produces a consistent estimate ofp. Simulation results support the effectiveness of this procedure when compared with some of the traditional order selection criteria. Comments are also made on the use of Yule–Walker as opposed to conditional least squares estimations for order selection. 相似文献
16.
In this article, we study a model of a single variable sampling plan with Type I censoring. Assume that the quality of an
item in a batch is measured by a random variable which follows a Weibull distributionW (λ,m), with scale parameter λ and shape parameterm having a gamma-discrete prior distribution or σ=1/λ andm having an inverse gamma-uniform prior distribution. The decision function is based on the Kaplan-Meier estimator. Then, the
explicit expressions of the Bayes risk are derived. In addition, an algorithm is suggested so that an optimal sampling plan
can be determined approximately after a finite number of searching steps. 相似文献
17.
We obtain an explicit representation as Dunwoody manifolds of all cyclic branched coverings of torus knots of type (p,mp±1), with p > 1 and m > 0. 相似文献
18.
Muneya Matsui Akimichi Takemura 《Annals of the Institute of Statistical Mathematics》2005,57(1):183-199
We consider goodness-of-fit tests of the Cauchy distribution based on weighted integrals of the squared distance between the
empirical characteristic function of the standardized data and the characteristic function of the standard Cauchy distribution.
For standardization of data Gürtler and Henze (2000,Annals of the Institute of Statistical Mathematics,52, 267–286) used the median and the interquartile range. In this paper we use the maximum likelihood estimator (MLE) and an
equivariant integrated squared error estimator (EISE), which minimizes the weighted integral. We derive an explicit form of
the asymptotic covariance function of the characteristic function process with parameters estimated by the MLE or the EISE.
The eigenvalues of the covariance function are numerically evaluated and the asymptotic distributions of the test statistics
are obtained by the residue theorem. A simulation study shows that the proposed tests compare well to tests proposed by Gürtler
and Henze and more traditional tests based on the empirical distribution function. 相似文献
19.
James P. Cossey 《Algebras and Representation Theory》2011,14(2):391-402
The Alperin weight conjecture states that if G is a finite group and p is a prime, then the number of irreducible Brauer characters of a group G should be equal to the number of conjugacy classes of p-weights of G. This conjecture is known to be true for the symmetric group S
n
, however there is no explicit bijection given between the two sets. In this paper we develop an explicit bijection between
the p-weights of S
n
and a certain set of partitions that is known to have the same cardinality as the irreducible Brauer characters of S
n
. We also develop some properties of this bijection, especially in relation to a certain class of partitions whose corresponding
Specht modules over fields of characteristic p are known to be irreducible. 相似文献
20.
R. Khattree D. P. Schmidt I. E. Schochetman 《Journal of Optimization Theory and Applications》1999,103(2):359-384
We consider an infinite-dimensional isotonic regression problem which is an extension of the suitably revised classical isotonic regression problem. Given p-summable data, for p finite and at least one, there exists an optimal estimator to our problem. For p greater than one, this estimator is unique and is the limit in the p-norm of the sequence of unique estimators in canonical finite-dimensional truncations of our problem. However, for p equal to one, our problem, as well as the finite-dimensional truncations, admit multiple optimal estimators in general. In this case, the sequence of optimal estimator sets to the truncations converges to the optimal estimator set of the infinite problem in the sense of Kuratowski. Moreover, the selection of natural best optimal estimators to the truncations converges in the 1-norm to an optimal estimator of the infinite problem. 相似文献