共查询到20条相似文献,搜索用时 15 毫秒
1.
In this paper, we propose a combined regression estimator by using a parametric estimator and a nonparametric estimator of the regression function. The asymptotic distribution of this estimator is obtained for cases where the parametric regression model is correct, incorrect, and approximately correct. These distributional results imply that the combined estimator is superior to the kernel estimator in the sense that it can never do worse than the kernel estimator in terms of convergence rate and it has the same convergence rate as the parametric estimator in the case where the parametric model is correct. Unlike the parametric estimator, the combined estimator is robust to model misspecification. In addition, we also establish the asymptotic distribution of the estimator of the weight given to the parametric estimator in constructing the combined estimator. This can be used to construct consistent tests for the parametric regression model used to form the combined estimator. 相似文献
2.
本文研究了Panel模型中回归系数常见估计的比较问题,给出了在Pitman准则,协方差阵准则和广义均方误差准则下最小二乘估计,Within估计,Between估计及两步估计之间的优良性比较结果.特别地,本文证明了在Pitman准则下最小二乘估计一致地优于Between估计. 相似文献
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4.
Receiver operating characteristic (ROC) curves are often used to study the two sample problem in medical studies. However, most data in medical studies are censored. Usually a natural estimator is based on the Kaplan-Meier estimator. In this paper we propose a smoothed estimator based on kernel techniques for the ROC curve with censored data. The large sample properties of the smoothed estimator are established. Moreover, deficiency is considered in order to compare the proposed smoothed estimator of the ROC curve with the empirical one based on Kaplan-Meier estimator. It is shown that the smoothed estimator outperforms the direct empirical estimator based on the Kaplan-Meier estimator under the criterion of deficiency. A simulation study is also conducted and a real data is analyzed. 相似文献
5.
本文提出方差分量ANOVA估计的一种改进方法, 证明了对于一般的方差分量模型, 只要方差分量的ANOVA估计存在就可以通过此方法给出其改进形式, 并且在均方误差意义下优于ANOVA估计. 特别地, 对于单向分类随机效应模型, Kelly和Mathew[1]对ANOVA估计的改进就是我们提出的改进方法的特殊形式, 这也给出了此类改进估计在均方误差意义下优于ANOVA估计的另一种合理的解释. 同时, 本文又将此思想应用到对谱分解估计的改进上. 本文应用协方差的简单性质证明了对带有一个随机效应的方差分量模型, 当随机效应的协方差阵只有一个非零特征值时, 随机效应方差分量谱分解估计在均方误差意义下总是优于ANOVA估计. 本文最后将第三节的结论推广到广义谱分解估计下, 同时给出广义谱分解估计待定系数的一个合理的取值. 相似文献
6.
Hironori Fujisawa 《Journal of multivariate analysis》2003,86(1):126-142
The conditional maximum likelihood estimator is suggested as an alternative to the maximum likelihood estimator and is favorable for an estimator of a dispersion parameter in the normal distribution, the inverse-Gaussian distribution, and so on. However, it is not clear whether the conditional maximum likelihood estimator is asymptotically efficient in general. Consider the case where it is asymptotically efficient and its asymptotic covariance depends only on an objective parameter in an exponential model. This remand implies that the exponential model possesses a certain parallel foliation. In this situation, this paper investigates asymptotic properties of the conditional maximum likelihood estimator and compares the conditional maximum likelihood estimator with the maximum likelihood estimator. We see that the bias of the former is more robust than that of the latter and that two estimators are very close, especially in the sense of bias-corrected version. The mean Pythagorean relation is also discussed. 相似文献
7.
Motivated by problems in molecular biosciences wherein the evaluation of entropy of a molecular system is important for understanding its thermodynamic properties, we consider the efficient estimation of entropy of a multivariate normal distribution having unknown mean vector and covariance matrix. Based on a random sample, we discuss the problem of estimating the entropy under the quadratic loss function. The best affine equivariant estimator is obtained and, interestingly, it also turns out to be an unbiased estimator and a generalized Bayes estimator. It is established that the best affine equivariant estimator is admissible in the class of estimators that depend on the determinant of the sample covariance matrix alone. The risk improvements of the best affine equivariant estimator over the maximum likelihood estimator (an estimator commonly used in molecular sciences) are obtained numerically and are found to be substantial in higher dimensions, which is commonly the case for atomic coordinates in macromolecules such as proteins. We further establish that even the best affine equivariant estimator is inadmissible and obtain Stein-type and Brewster–Zidek-type estimators dominating it. The Brewster–Zidek-type estimator is shown to be generalized Bayes. 相似文献
8.
人口普查质量评估中所使用的双系统估计量是否为无偏估计量是一个很值得深入讨论的问题。只有无偏,才能确保使用双系统估计量估计的目标总体实际人数及人口普查净误差平均等于它们的实际数。针对人口普查质量评估工作中所使用的双系统估计量,论证这个估计量的无偏性条件。采用从既定假设出发进行推演的路径论证。研究结果表明,双系统估计量是目标总体实际人数无偏估计量的必要但非充分的条件是,人口普查与其质量评估调查相互独立以及目标总体中的每一个人在人口普查中的登记概率相同,在质量评估调查中登记的概率也相同。 相似文献
9.
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
10.
Choongrak Kim Byeong U. Park Woochul Kim Chiyon Lim 《Annals of the Institute of Statistical Mathematics》2003,55(2):359-367
Estimation of a survival function from randomly censored data is very important in survival analysis. The Kaplan-Meier estimator
is a very popular choice, and kernel smoothing is a simple way of obtaining a smooth estimator. In this paper, we propose
a new smooth version of the Kaplan-Meier estimator using a Bezier curve. We show that the proposed estimator is strongly consistent.
Numerical results reveal the that proposed estimator outperforms the Kaplan-Meier estimator and its kernel weighted smooth
version in the sense of mean integrated square error.
This research is supported by the Korea Research Foundation (1998-015-d00047) made in the program year of 1998. 相似文献
11.
S.E. Ahmed 《随机分析与应用》2013,31(4):475-492
The simultaneous asymptotic estimation theory of quantiles is considered for an arbitrary population. The Stein–type estimator and its positive version are considered. The relative merits of the proposed estimators are compared with those of the usual estimator using asymptotic quadratic distributional risk those of the usual estimator using asymptotic quadratic distributional risk under local alternatives. It is shown that both proposed estimators are asymptotically superior to the classical estimator. Further, it is demonstrated that the Stein-type estimator is dominated by its positive part 相似文献
12.
Anton Schick Wolfgang Wefelmeyer 《Annals of the Institute of Statistical Mathematics》2002,54(2):245-260
The usual estimator for the expectation of a function under the innovation distribution of a nonlinear autoregressive model is the empirical estimator based on estimated innovations. It can be improved by exploiting that the innovation distribution has mean zero. We show that the resulting estimator is efficient if the innovations are estimated with an efficient estimator for the autoregression parameter. Efficiency of this estimator is necessary except when the expectation of the function can be estimated adaptively. Analogous results hold for heteroscedastic models. 相似文献
13.
In sampling theory, the traditional ratio estimator is the most common estimator of the population mean when the correlation between study and auxiliary variables is positively high. We introduce a new ratio-type estimator based on the order statistics of a simple random sample. We show that this new estimator is considerably more efficient than the traditional ratio estimator under non-normality, and remarkably robust to data anomalies such as presence of outliers in data sets. 相似文献
14.
We consider the problem of multivariate density estimation, using samples from the distribution of interest as well as auxiliary
samples from a related distribution. We assume that the data from the target distribution and the related distribution may
occur individually as well as in pairs. Using nonparametric maximum likelihood estimator of the joint distribution, we derive
a kernel density estimator of the marginal density. We show theoretically, in a simple special case, that the implied estimator
of the marginal density has smaller integrated mean squared error than that of a similar estimator obtained by ignoring dependence
of the paired observations. We establish consistency of the marginal density estimator under suitable conditions. We demonstrate
small sample superiority of the proposed estimator over the estimator that ignores dependence of the samples, through a simulation
study with dependent and non-normal populations. The application of the density estimator in nonparametric classification
is also discussed. It is shown that the misclassification probability of the resulting classifier is asymptotically equivalent
to that of the Bayes classifier. We also include a data analytic illustration. 相似文献
15.
We study an estimator of the survival function under the random censoring model. Bahadur-type representation of the estimator
is obtained and asymptotic expression for its mean squared errors is given, which leads to the consistency and asymptotic
normality of the estimator. A data-driven local bandwidth selection rule for the estimator is proposed. It is worth noting
that the estimator is consistent at left boundary points, which contrasts with the cases of density and hazard rate estimation.
A Monte Carlo comparison of different estimators is made and it appears that the proposed data-driven estimators have certain
advantages over the common Kaplan-Meier estmator. 相似文献
16.
In this paper, we propose a new biased estimator of the regression parameters, the generalized ridge and principal correlation estimator. We present its some properties and prove that it is superior to LSE (least squares estimator), principal correlation estimator, ridge and principal correlation estimator under MSE (mean squares error) and PMC (Pitman closeness) criterion, respectively. 相似文献
17.
James H Albert 《Journal of multivariate analysis》1981,11(3):400-417
In the simultaneous estimation of means from independent Poisson distributions, an estimator is developed which incorporates a prior mean and variance for each Poisson mean estimated. This estimator possesses substantially smaller risk than the usual estimator in a region of the parameter space and seems superior to other estimators proposed to estimate p Poisson means. It is indicated through two asymptotic results that, unlike the conjugate Bayes estimator, the risk of the estimator does not greatly exceed the risk of the usual estimator outside of the region of risk improvement. 相似文献
18.
S. Ejaz Ahmed 《Linear algebra and its applications》2009,430(10):2734-2748
The Poisson distribution is often a good approximation to the underlying sampling distribution and is central to the study of categorical data. In this paper, we propose a new unified approach to an investigation of point properties of simultaneous estimations of Poisson population parameters with general quadratic loss functions. The main accent is made on the shrinkage estimation. We build a series of estimators that could be represented as a convex combination of linear statistics such as maximum likelihood estimator (benchmark estimator), restricted estimator, composite estimator, preliminary test estimator, shrinkage estimator, positive rule shrinkage estimator (James-Stein type estimator). All these estimators are represented in a general integrated estimation approach, which allows us to unify our investigation and order them with respect to the risk. A simulation study with numerical and graphical results is conducted to illustrate the properties of the investigated estimators. 相似文献
19.
In this paper moving-average processes with no parametric assumption on the error distribution are considered. A new convolution-type estimator of the marginal density of a MA(1) is presented. This estimator is closely related to some previous ones used to estimate the integrated squared density and has a structure similar to the ordinary kernel density estimator. For second-order kernels, the rate of convergence of this new estimator is investigated and the rate of the optimal bandwidth obtained. Under limit conditions on the smoothing parameter the convolution-type estimator is proved to be
-consistent, which contrasts with the asymptotic behavior of the ordinary kernel density estimator, that is only
-consistent. 相似文献
20.
Pao-sheng Shen 《Annals of the Institute of Statistical Mathematics》2011,63(6):1207-1219
Patilea and Rolin (Ann Stat 34(2):925–938, 2006) proposed a product-limit estimator of the survival function for twice censored data. In this article,
based on a modified self-consistent (MSC) approach, we propose an alternative estimator, the MSC estimator. The asymptotic
properties of the MSC estimator are derived. A simulation study is conducted to compare the performance between the two estimators.
Simulation results indicate that the MSC estimator outperforms the product-limit estimator and its advantage over the product-limit
estimator can be very significant when right censoring is heavy. 相似文献