回归系数和参数的可容许性估计——对正态条件下具有两个方差分量的一般随机效应线性模型

本文考虑具有两个方差分量的一般随机效应线性模型Y=Xβ e,对该模型随机回归系数和参数的可估函数Sα Qβ,在矩阵损失与二次损失函数下,分别给出其线性估计LY α在一切估计类中是Sα Qβ的可容许估计的充分条件。     

基于递推最小二乘支持向量回归估计的建模与预报

提出一种新的递推最小二乘支持向量回归估计算法（RLS-SVR），该算法具有实时性高、更新速度快的特点．充分应用RLS-SVR在线学习和训练的实时性，避免辨识模型的维数过高而降低估计精度，本文进一步提出了基于RLS-SVR的混合训练—估计辨识结构．TE过程的组分软测量建模和预报验证了该方法的有效性和优越性．     

Network tomography from aggregate loss reports

Multicast applications and network monitors can potentially benefit from the ability to infer the loss rates along links within a multicast tree. Estimators, known generically by minc or multicast inference of network characteristics, have been developed to provide this ability. They consider multicast data packets to be probes, and conduct inference based upon reports of which probes reached each receiver. In practice, gathering reports from receivers in real time is a non-trivial task that presents scaling problems as the number of receivers increases. Prior work has led to an extension of the RTP data transport protocol to permit receivers to report per-probe information in packets known as RTCP XR packets.

This paper demonstrates how minc inference can, in fact, be conducted using only a default RTP packet format known as RTCP RR. RTCP RR packets contain summary information rather than per-probe information. They thus offer bandwidth savings, although this comes at the expense of an increase in estimator convergence time. Furthermore, this technique can be used by the observer of any standard RTP session, whereas estimation based upon per-probe information is only possible when a session explicitly employs the extended reporting format.     

用于自适应天线的稳健的RLS算法

抗干扰通信是电子战的重要组成部分，自适应天线不仅具有很强的抗干扰能力而且可以与其它通信抗干扰技术相兼容，自适应算法是自适应天线的核心。本文把应用数学学科研究的热门方法之一“稳健估计（RE）”应用于RLS算法中，得到稳健的RLS算法（RRLS），理论分析与计算机模拟结果都证明了RRLS算法基本保持了RLS算法的优点，同时在抗突出值干扰方面，优于RLS算法，提高了RLS算法的稳健性。     

Difference estimators of quantiles in finite populations

This paper deals with the inference of finite populations quantiles by using auxiliary information. The population information considered on the proposed estimatiors is a population quantile of the auxiliary variable with the same order as that of the quantile of the main variable to be estimated. A simulation study based on three real finite populations is performed and comparisons of the proposed estimators with other common estimators for quantile estimation are carried out.     

A common conjugate prior structure for several randomized response models

In this paper we present a common Bayesian approach to four randomized response models, including Warner's (1965) and other modification for it that appeared thereafter in the literature. Suitable truncated beta distributions are used throughout in a common conjugate prior structure to obtain the Bayes estimates for the proportion of a “sensitive” attribute in the population of interest. The results of this common conjugate prior approach are contrasted with those of Winkler and Franklin's (1979), in which non-conjugate priors have been used in the context of Warner's model. The results are illustrated numerically in several cases and exemplified further with data reported in Liu and Chow (1976) concerning incidents of induced abortions.     

Convex Digital Polygons,Maximal Digital Straight Segments and Convergence of Discrete Geometric Estimators

Discrete geometric estimators approach geometric quantities on digitized shapes without any knowledge of the continuous shape. A classical yet difficult problem is to show that an estimator asymptotically converges toward the true geometric quantity as the resolution increases. For estimators of local geometric quantities based on Digital Straight Segment (DSS) recognition this problem is closely linked to the asymptotic growth of maximal DSS for which we show bounds both about their number and sizes on Convex Digital Polygons. These results not only give better insights about digitized curves but indicate that curvature estimators based on local DSS recognition are not likely to converge. We indeed invalidate a conjecture which was essential in the only known convergence theorem of a discrete curvature estimator. The proof involves results from arithmetic properties of digital lines, digital convexity, combinatorics and continued fractions.     

Empirical Bayes estimators of the random intercept in multilevel analysis: Performance of the classical, Morris and Rao version

For a linear multilevel model with 2 levels, with equal numbers of level-1 units per level-2 unit and a random intercept only, different empirical Bayes estimators of the random intercept are examined. Studied are the classical empirical Bayes estimator, the Morris version of the empirical Bayes estimator and Rao's estimator. It is unclear which of these estimators performs best in terms of Bayes risk. Of these three, the Rao estimator is optimal in case the covariance matrix of random coefficients may be negative definite. However, in the multilevel model this matrix is restricted to be positive semi-definite. The Morris version, replaces the weights of the empirical Bayes estimator by unbiased estimates. This correction, however, is based on known level-1 variances, which in many empirical settings are unknown. A fourth estimator is proposed, a variant of Rao's estimator which restricts the estimated covariance matrix of random coefficients to be positive semi-definite. Since there are no closed-form expressions for estimators involved in the empirical Bayes estimators (except for the Rao estimator), Monte Carlo simulations are done to evaluate the performance of these different empirical Bayes estimators. Only for small sample sizes there are clear differences between these estimators. As a consequence, for larger sample sizes the formula for the Bayes risk of the Rao estimator can be used to calculate the Bayes risk for the other estimators proposed.     

Generalized likelihood ratio test for varying-coefficient models with different smoothing variables

Varying-coefficient models are popular multivariate nonparametric fitting techniques. When all coefficient functions in a varying-coefficient model share the same smoothing variable, inference tools available include the F-test, the sieve empirical likelihood ratio test and the generalized likelihood ratio (GLR) test. However, when the coefficient functions have different smoothing variables, these tools cannot be used directly to make inferences on the model because of the differences in the process of estimating the functions. In this paper, the GLR test is extended to models of the latter case by the efficient estimators of these coefficient functions. Under the null hypothesis the new proposed GLR test follows the χ²-distribution asymptotically with scale constant and degree of freedom independent of the nuisance parameters, known as Wilks phenomenon. Further, we have derived its asymptotic power which is shown to achieve the optimal rate of convergence for nonparametric hypothesis testing. A simulation study is conducted to evaluate the test procedure empirically.     

Estimating the width of a uniform distribution when data are measured with additive normal errors with known variance

The problem of estimating the width of the symmetric uniform distribution on the line when data are measured with normal additive error is considered. The main purpose is to discuss the efficiency of the maximum likelihood estimator and the moment method estimator. It is shown that the model is regular and that the maximum likelihood estimator is more efficient than the moment method estimator. A sufficient condition is also given for the existence of both estimators.