Small-area estimation in the presence of area-level correlated responses

The Fay-Herriot area-level model for correlated response data is augmented with a between-groups-of-domains effect. Correlated-response parameters of small-area estimates no longer need the assumption of spatial contiguity. A simulation shows that area-level correlated-response observations increase the efficiency of the estimates, but do not reduce the biases.     

Robust modelling for asset management

We argue that the principal way to achieve robustness is through good elicitation. This means asking the right questions, to elicit only those judgements which can be given reliably. Bayes linear methods minimise the number of prior judgements employed in the analysis. Only first- and second-order moments are specified. We present an example in which a complex problem is modelled in terms of a relatively small number of meaningful prior judgements. Sensitivity to variations in those judgements is explored here and in Goldstein and Wooff (1992).     

Bounds on posterior expectations for density bounded class with constant bandwidth

We consider a certain (simplified) version of the density bounded class; see Lavine (J. Amer. Statist. Assoc. 86 (1991) 400–403). We find that this class has certain advantages over many other similar classes in terms of elicitation, ease of calculation and other characteristics of interest. Bounds on posterior expectations are considered. In particular, we show how certain bounds may be found in the (multidimensional) normal linear model problem.     

迭代阵特征值模界的估计

线性方程组Ａｘ＝ｂ的数值求解是许多数值问题的核心，其迭代法的收敛性分析是众多计算数学学者的研究焦点。本文一般性地抽象出矩阵分裂迭代阵，用简单的方法给出迭代阵特征值模的界的估计，将给许多问题的讨论带来方便，且应用起来直接明了。     

含有零泛器的线性网络的稀疏表分析法

本文提出了含有零泛器的线性网络的稀疏表分析法（ＳＴＡ），讨论了其算法实现。对比所提出的方法与降阶的节点法的特点后得出结论：本文算法是可行的。     

Parametrically Guided Non-parametric Regression

We present a new approach to regression function estimation in which a non-parametric regression estimator is guided by a parametric pilot estimate with the aim of reducing the bias. New classes of parametrically guided kernel weighted local polynomial estimators are introduced and formulae for asymptotic expectation and variance, hence approximated mean squared error and mean integrated squared error, are derived. It is shown that the new classes of estimators have the very same large sample variance as the estimators in the standard non-parametric setting, while there is substantial room for reducing the bias if the chosen parametric pilot function belongs to a wide neighbourhood around the true regression line. Bias reduction is discussed in light of examples and simulations.     

Risk Comparison of Inequality Constrained Estimators in the Heteroscedastic Linear Model

There exist many studies which treat the inequality and/or interval constraints on coefficients in the homoscedastic linear regression model. However, the sampling performance of the inequality constrained estimators in the heteroscedastic linear model has not been examined. This paper considers the inequality constrained estimators in the heteroscedastic linear regression model and derives their risks under a quadratic loss function. Furthermore, using the inequality constrained estimators, we introduce a pre-test estimator which might be employed after the test for homoscedasticity and derive its risk. In addition, the risk performance of these estimators is evaluated numerically.     

Estimative influence measures for the multivariate general linear model

Within the context of the multiviriate general linear model, and using a Bayesian formulation and Kullback-Leibler divergences this paper provides a framework and the resultant methods for the problem of detecting and characterizing influential subsets of observations when the goal is to estimate parameters. It is further indicated how these influence measures inherently depend upon one's exact estimative intent. The relationship to previous work on observations influential in estimation is discussed. The estimative influence measures obtained here are also compared with predictive influence functions previously obtained. Several examples are presented illustrating the methodology.     

Quadratic nuisance-parameter-free goodness-of-fit tests in the presence of location and scale parameters

The asymptotic theory of a quadratic nuisance-parameter-free goodness-of-fit procedure is studied. The present techniques are applied in testing for the exponential, the logistic, the extreme value, and the Weibull distributions.     

对多媒体辅助大学英语教学的思考与建议

与传统英语教学方式比较,多媒体技术在鼓励学生学习兴趣、增加互动、节约教学资源方面存在优势,目前使用中存在的课件制作技术重内容、师生主辅作用不分等问题,可以从转变教学观念、创新教学方式及分类施教方面加以改进。