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91.
Hu Yang 《统计学通讯:理论与方法》2013,42(22):4078-4085
In this article, we first present four matrix norm Kantorovich-type inequalities involving non negative definite matrix. Then, based on these inequalities, we propose four new efficiency criteria and present their lower bounds to make efficiency comparisons between the ordinary least squares estimator and the best linear unbiased estimator in a singular linear model. 相似文献
92.
Ancop Chaturvedi 《统计学通讯:理论与方法》2013,42(8):2275-2284
The present paper considers a family of ordinary ridge regression estimators in the linear regression model when the disturbances covariance matrix depends upon a few unknown parameters. An asymptotic expansion for the distribution of the ridge regression estimator is developed and under the quadratic loss function its asymptotic risk is compared with that of the feasible GLS estimator. 相似文献
93.
Two often-quoted necessary and sufficient conditions for ordinary least squares estimators to be best linear unbiased estimators are described. Another necessary and sufficient condition is described, providing an additional tool for checking to see whether the covariance matrix of a given linear model is such that the ordinary least squares estimator is also the best linear unbiased estimator. The new condition is used to show that one of the two published conditions is only a sufficient condition. 相似文献
94.
A regression approach to principal component analysis is presented in this note. We provide an alternative interpretation of principal components that illustrates the relation between the extra sum of squares in regression analysis and the eigenvalues associated with the principal components. 相似文献
95.
The count data model studied in the paper extends the Poisson model by al-lowing for overdispersion and serial correlation. Alternative approaches to esti-mate nuisance parameters, required for the correction of the Poisson maximum likelihood covariance matrix estimator and for a quasi-likelihood estimator, are studied. The estimators are evaluated by finite sample Monte Carlo experi-mentation. It is found that the Poisson maximum likelihood estimator with corrected covariance matrix estimators provide reliable inferences for longer time series. Overdispersion test statistics are wellbehaved, while conventional portmanteau statistics for white noise have too large sizes. Two empirical illustrations are included. 相似文献
96.
In some crossover experiments, particularly in medical applications, subjects may fail to complete their sequences of treatments for reasons unconnected with the treatments received. A method is described of assessing the robustness of a planned crossover design, with more than two periods, to subjects leaving the study prematurely. The method involves computing measures of efficiency for every possible design that can result, and is therefore very computationally intensive. Summaries of these measures are used to choose between competing designs. The computational problem is reduced to a manageable size by a software implementation of Polya theory. The method is applied to comparing designs for crossover studies involving four treatments and four periods. Designs are identified that are more robust to subjects dropping out in the final period than those currently favoured in medical and clinical trials. 相似文献
97.
Peter M. Hooper 《Revue canadienne de statistique》2001,29(3):343-364
The author proposes a new method for flexible regression modeling of multi‐dimensional data, where the regression function is approximated by a linear combination of logistic basis functions. The method is adaptive, selecting simple or more complex models as appropriate. The number, location, and (to some extent) shape of the basis functions are automatically determined from the data. The method is also affine invariant, so accuracy of the fit is not affected by rotation or scaling of the covariates. Squared error and absolute error criteria are both available for estimation. The latter provides a robust estimator of the conditional median function. Computation is relatively fast, particularly for large data sets, so the method is well suited for data mining applications. 相似文献
98.
Abstract. The marginal density of a first order moving average process can be written as a convolution of two innovation densities. Saavedra & Cao [Can. J. Statist. (2000), 28, 799] propose to estimate the marginal density by plugging in kernel density estimators for the innovation densities, based on estimated innovations. They obtain that for an appropriate choice of bandwidth the variance of their estimator decreases at the rate 1/ n . Their estimator can be interpreted as a specific U -statistic. We suggest a slightly simplified U -statistic as estimator of the marginal density, prove that it is asymptotically normal at the same rate, and describe the asymptotic variance explicitly. We show that the estimator is asymptotically efficient if no structural assumptions are made on the innovation density. For innovation densities known to have mean zero or to be symmetric, we describe improvements of our estimator which are again asymptotically efficient. 相似文献
99.
In many situations the applied researcher wishes to combine different data sources without knowing
the exact link and merging rule. This paper considers different cartographic interpolation methods for
interpolating attributes from German employment office districts to German counties and vice versa. In
particular, we apply dasymetric weighting as an alternative to simple area weighting, both of which are
based on estimated intersection areas. We also present conditions under which the choice of interpolation
method does not matter and confirm the theoretical results with a simulation study. Our application to
German administrative data suggests robustness of estimation results of interpolated attributes with respect
to the choice of interpolation method. We provide weighting matrices for regional data sources of the two
largest German data producers. 相似文献
100.
Minimax estimation of a binomial probability under LINEX loss function is considered. It is shown that no equalizer estimator
is available in the statistical decision problem under consideration. It is pointed out that the problem can be solved by
determining the Bayes estimator with respect to a least favorable distribution having finite support. In this situation, the
optimal estimator and the least favorable distribution can be determined only by using numerical methods. Some properties
of the minimax estimators and the corresponding least favorable prior distributions are provided depending on the parameters
of the loss function. The properties presented are exploited in computing the minimax estimators and the least favorable distributions.
The results obtained can be applied to determine minimax estimators of a cumulative distribution function and minimax estimators
of a survival function. 相似文献