自适应有限元和后验误差估计——等价估计

近年来,自适应有限元计算得到了广泛的研究.后验误差估计是实现自适应计算的基础.70年代末,I. Babuska和 W.Rheinboldt给出了建立等价后验估计的一般原则和方法,在一维情形给出了完整的结果.80年代初,I.Babuska和A.Miller对平面弹性问题正方形双线性元,给出了一种后验估计的方法,并证明了其等价性和渐近准确     

有限元的渐近准确误差估计和局部超收敛性

[1—3]曾系统讨论有限元的局部(内部)超收敛理论,指出:一个局部区域只要剖分好而且解光滑,那么有限元逼近在该区域就有超收敛性。Babuska曾讨论某几种有限元的后验估计和渐近误差估计,但这些可算的后验估计量(也叫误差指示子error estima-tor)表达式复杂,计算麻烦,作自适应处理并不方便。实际上,后验估计与局部超收敛性有着天然的联系。本文证明,凡是有超收敛性的地方都可进行渐近准确误差估计,这种可     

Wilson元插值误差渐近估计

Wilson元是工程界常用的一种有限元计算方法,但在理论分析中插值误差估计的常数只知道存在,不知道具体值.本文给出了在L~2、H~1范数意义下Wilson元在参考单元和一般单元上插值误差渐近估计,导出了主要常数.这种精确的估计为有限元后验误差估计和自适应计算提供保障.     

利用依赖单元剖分范数的有限元解敛速估计

它们分别作为有限元涡函数和流函数逼近的收敛性尺度,获得了最佳敛速估计和混合刚度模型的计算格式. 最近,Babuska和Osborn对于常微分两点边值问题,在[1]中构造的一般理论的基础上,用类似于范数(1.1),(1.2)的一维L_p范数,讨论了经典有限元逼近的敛速估计,得到了一些有意思的结果.各种型式的有限元分析可纳入统一的图式之中.     

一维有限元的h-p方法的后验误差估计

有限元的h-p方法,是指在增加有限元空间的维数时,既加密某些单元的网格,同时也增加某些单元的次数.对h-p方法,人们希望得到O(h~mp~(-n))(m,n>0)形状的误差估计.这种误差估计的结果包括了对传统的h方法以及p方法的结果.关于h-p方法的     

一维有限元的h-p方法的后验误差估计

有限元的h-p方法,是指在增加有限元空间的维数时,既加密某些单元的网格,同时也增加某些单元的次数.对h-p方法,人们希望得到O(h~mp~(-n))(m,n>0)形状的误差估计.这种误差估计的结果包括了对传统的h方法以及p方法的结果.关于h-p方法的     

特征值问题混合有限元法的一个误差估计

设（λh,σh,uh）是一个混合有限元特征对.Babuska和Osborn建立了（λh,uh）的误差估计.本文导出了σh的抽象误差估计式.并把该估计式应用于二阶椭圆特征值问题Raviart-Thomas混合有限元格式和重调和算子特征值问题Ciarlet-Raviart混合有限元格式,得到了一些新的误差估计.     

双偶次有限元的渐近准确误差估计

当我们用有限元方法近似求解偏微分方程的边值问题时,常对近似解有一定的精度要求.于是仅在初始网格上进行一次计算是不够的,往往要进行一系列的计算.如何根据对已有计算结果的分析来控制下一步计算,导致自适应方法的出现.自适应方法的基础是对有限元近似解作后验误差估计.在h型自适应有限元方法中,通过加细剖分来达     

鞍点有限元格式的等价定理和强Babuska-Brezzi条件判别准则

在Lax型等价定理的形式下,本文研究了较大一类鞍点有限元格式解的存在唯一和收敛速度的估计.所建立的理论包含了作为特殊情况的Brezzi理论.提出了两个分片检查型判别法则,便于强Babuska条件的实践验证.     

椭圆边值问题的一个估计及其对有限元的应用

本文对二维Poisson方程的齐次第一边值问题考虑以下四个问题:首先对此边值问题的解建立L_p估计(p=1或∞情形);由此在正规剖分条件下建立有限元的L_p估计 (1≤p≤∞);利用这些结果得到有限元的L_∞内估计;最后导出一个超收敛估计。这里所得的结果可以推广到多维情形及一般(线性与拟线性)椭圆型方程。     

Second derivative estimation using harmonic analysis

Simulation sensitivity analysis is an important problem for simulation practitioners analyzing complex systems. The significance of this problem has resulted in the development of various gradient estimators that can be used to address this issue. Although higher derivative estimators have been discussed concurrently, less attention has been given to assess the efficiency and feasibility of computing such estimators. In this paper, two second derivative estimators are presented. The first estimators, called the HFD estimators, combine harmonic gradient estimators with finite differences second derivative estimators. The resulting hybrid estimators requireO(p) fewer simulation runs to implement compared to the straightforward finite differences approach, wherep is the number of input parameters in the simulation model. The second estimators, called the HA estimators, incorporate harmonic analysis directly, requiring one or two simulation runs to implement, depending on whether a control variate simulation run is made. Expressions for the bias and the variance of the HFD and the HA estimators (with and without variance reduction techniques) are derived. Optimal mean squared error convergence rates are also discussed. In particular, the convergence rates for both these estimators are shown to be the same, though the computational performance of the HFD estimators is better than that for the HA estimators on anM/M/1 queue simulation model. Computational results for the HFD estimators on an (s, S) inventory system simulation model are also included.     

Safe and tight linear estimators for global optimization

Global optimization problems are often approached by branch and bound algorithms which use linear relaxations of the nonlinear constraints computed from the current variable bounds. This paper studies how to derive safe linear relaxations to account for numerical errors arising when computing the linear coefficients. It first proposes two classes of safe linear estimators for univariate functions. Class-1 estimators generalize previously suggested estimators from quadratic to arbitrary functions, while class-2 estimators are novel. When they apply, class-2 estimators are shown to be tighter theoretically (in a certain sense) and almost always tighter numerically. The paper then generalizes these results to multivariate functions. It shows how to derive estimators for multivariate functions by combining univariate estimators derived for each variable independently. Moreover, the combination of tight class-1 safe univariate estimators is shown to be a tight class-1 safe multivariate estimator. Finally, multivariate class-2 estimators are shown to be theoretically tighter (in a certain sense) than multivariate class-1 estimators.     

An improved Afriat–Diewert–Parkan nonparametric production function estimator

Recent developments in the production frontier literature include nonparametric estimators with shape constraints. A few of these estimators rely on the Afriat inequalities to provide piecewise linear approximations to the production function/frontier. We show in this paper that these Afriat–Diewert–Parkan (ADP) estimators have deficiencies in the presence of moderate statistical noise including overfitting and a relatively high estimator variance. We propose new estimators with lower variance and a relatively low bias. We consider such alternative estimators based on (weighted) averages of random hinge functions with parameter restrictions. Small sample properties of the estimators are presented that show our new estimators outperform the existing ADP estimators when moderate to large amounts of noise are present.     

A note on superkernel density estimators

It is well known that so-called superkernel density estimators have better asymptotic properties than conventional kernel estimators (and generally finite-order estimators) in the case when the density to be estimated is very smooth. In this note, we study asymptotic behavior of the mean integrated square error of superkernel density estimators in the case when the density to be estimated is not very smooth. It turns out that in this case, superkernel estimators still have better asymptotics than finite-order estimators.     

混合模型中方差分量估计的容许性及非负估计

对含有两个方差分量的线性混合模型, 本文构造了方差分量的一个线性估计类, 它包含许多常见的方差分量估计. 在这个类中我们建立了容许性的必要条件, 据此得到了两个新的改进估计. 最后我们讨论了方差分量的非负估计, 得到了优于方差分析估计和Tatsuya估计的正估计.     

Flexible Multivariate Density Estimation With Marginal Adaptation

This article is concerned with multivariate density estimation. We discuss deficiencies in two popular multivariate density estimators—mixture and copula estimators, and propose a new class of estimators that combines the advantages of both mixture and copula modeling, while being more robust to their weaknesses. Our method adapts any multivariate density estimator using information obtained by separately estimating the marginals. We propose two marginally adapted estimators based on a multivariate mixture of normals and a mixture of factor analyzers estimators. These estimators are implemented using computationally efficient split-and-elimination variational Bayes algorithms. It is shown through simulation and real-data examples that the marginally adapted estimators are capable of improving on their original estimators and compare favorably with other existing methods. Supplementary materials for this article are available online.     

Improvement in estimating the population mean in simple random sampling

This paper proposes some estimators for the population mean using the ratio estimators presented in [C. Kadilar, H. Cingi, Ratio estimators in simple random sampling, Applied Mathematics and Computation 151 (2004) 893–902] and shows that all proposed estimators are always more efficient than the ratio estimators. This result is also supported by a numerical example.     

Estimating hazard ratios in nested case-control studies by mantel-haenszel method

1. IntroductionConsider a follow-up study which is carried out to investigate the association betweenexposure variables and mortality rate in a cohort. In the case where the cohort is of 1argesise, the complete follow-up ndght be too expensive or difficult, and various nested samplingmethod8 have been suggested by Thomas[l], Prenti..[2] 5 Goldstein and Langholzl'] and otherauthors. Most of the authors employ Coxl4] regression mode1 for estimating the hazard ratio8of exposures.Now a well-reco…     

Simultaneous Estimation of Several Intraclass Correlation Coefficients

Based on shrinkage and preliminary test rules, various estimators are proposed for estimation of several intraclass correlation coefficients when independent samples are drawn from multivariate normal populations. It is demonstrated that the James-Stein type estimators are asymptotically superior to the usual estimators. Furthermore, it is also indicated through asymptotic results that none of the preliminary test and shrinkage estimators dominate each other, though they perform relatively well as compared to the classical estimator. The relative dominance picture of the estimators is presented. A Monte Carlo study is performed to appraise the properties of the proposed estimators for small samples.     

Folded overlapping variance estimators for simulation

We propose and analyze a new class of estimators for the variance parameter of a steady-state simulation output process. The new estimators are computed by averaging individual estimators from “folded” standardized time series based on overlapping batches composed of consecutive observations. The folding transformation on each batch can be applied more than once to produce an entire set of estimators. We establish the limiting distributions of the proposed estimators as the sample size tends to infinity while the ratio of the sample size to the batch size remains constant. We give analytical and Monte Carlo results showing that, compared to their counterparts computed from nonoverlapping batches, the new estimators have roughly the same bias but smaller variance. In addition, these estimators can be computed with order-of-sample-size work.