次序统计量给出的线性Bayes估计

本文讨论了位置尺度参数族的参数线性函数的线性Ｂａｙｅｓ估计问题，给出了基于次序统计是的线性Ｂａｙｅｓ估计，并就特殊分布给出了例子。     

函数型数据的近邻域估计

主要讨论函数型数据的近邻域估计的渐近性质.在α-混合条件及一些正则性假设下,我们讨论了函数空间上非参数回归函数的k阶近邻域估计的相合性和渐近正态性.通过模拟分析几组不同误差分布的函数型数据,并与核估计方法进行比较,验证了有限样本下,近邻域估计方法的有效性,并得出近邻域估计在稳健性方面更有优势.     

稳健估计和检验的若干进展

本文介绍近几年在稳健估计和稳健检验方面的若干进展，包括：（１）一维位置Ｍ估计的崩溃点，特别是重降Ｍ估计的样本崩溃点的渐近性质；（２）多元位置和散布阵的ＰＰ型估计及其性质；（３）统计检验的崩溃点，以及ｔ检验，Ｔ＾２检验，Ｍ检验，得分检验，ｘ＾２检验等一些常用检验方法的崩溃稳健性。此外，本文还简要叙述了稳健性的基本概述以及上述三方面的主要理论。     

一类函数型回归模型的局部扰动分析

本文对一类特殊的函数型回归模型,提出连续型局部扰动分析的概念;对自变量、因变量和权重函数等三种情形下的扰动分析,基于泛函优化理论,推导出扰动函数的表达式.作为应用,本文考虑常微分方程的统计诊断问题,发现了常微分方程两步估计的边界效应,从而为两步估计中权重函数的选取提供了理论基础;利用模拟和实际数据验证了统计诊断的有效性.     

至多一个分布变点的非参数统计推断

本文研究了连续分布函数变点的非参数统计推断问题.利用秩统计量和次序统计量,获得了变点的一种估计,不仅论证了点估计的强相合性,而且讨论了假设检验和区间估计.     

两均匀分布总体参数之比的估计

给出了两均匀分布的最大次序统计量的密度函数,并讨论了两均匀分布参数之比的通常区间估计、最短区间估计及假设检验方法.最后,根据实例求出了这两种区间估计及其区间长度,并得出了相应的结论.     

偏态自适型投影深度

对常规投影深度进行了改进,得到了一类新的可自适应反映多元数据云偏态的统计投影深度.新的深度函数仍然具有凸的深度域,满足一般统计深度函数所应具备的全部基本特点.此外,还考虑了新投影深度函数的计算问题,证得在任意维空间里,它及由它诱导的深度域均是可以精确计算的,从技术角度讲,其处理比常规投影深度更加简便.最后,提供了一些数据示例用以展示此偏态自适型投影深度的有限样本性质.     

非参数固定设计回归模型中的Stahel-Donoho核估计

研究非参数固定设计回归模型中的稳健核估计. 提出了一种Stahel-Donoho核估计, 在此核估计中, 权重函数既依赖于数据深度, 又依赖于设计点和估计点之间的距离. 对不可直接计算的误差深度, 利用局部近似, 给出了一种近似计算方法, 使得新的估计是计算有效的. 新的估计获得较高的崩溃点值, 并有渐近正态和均方收敛等良好的大样本性质. 与参数模型中的深度加权估计不同的是,这种深度加权非参数估计有简单的方差结构,于是,人们可以比较新旧估计的有效性.数据模拟结果表明,新的方法可以平滑回归估计,并获得稳健性和有效性的良好平衡.     

LS，LAD组合损失的高维统计性质分析

主要针对损失函数为最小二乘LS（Least Squaresl和最小绝对偏差LAD（Least Absolute Deviation）的凸组合形式,研究了观测数n和预测数P均趋于无穷大（lim p／n=k,k〉0n-∞）时,高维稳健统计性质和高维罚稳健统计性质,得到了稳健估计和罚稳健估计的显示表达．结果显示这种凸组合损失函数的模型集成了LS和LAD损失的优点,同时消弱了它们的不足,具有优良的高维统计性质．     

高维旋转曲面

本文考虑欧氏空间中一种余一维的高维旋转曲面,通过发展出一种全新的复合映射、维数分解与分块矩阵递推法,我们系统性地研究了同它的面积和曲率有关的一系列问题.当母函数是多元函数时,这种高维旋转曲面的概念尚属首次提出.我们给出了这种高维旋转曲面的面积公式以及它的一些简单应用.我们发现:在任一直径方向上,单位球面的面积分布和低一维单位球体的体积分布完全相同,并且当维数趋于无穷时它们的密度函数的极限都是狄拉克函数.通过研究相应面积泛函的变分问题,我们得到了所谓的极小旋转曲面方程.我们证明了:满足极小旋转曲面方程的母函数对应的旋转曲面的平均曲率等于零.这种极小旋转曲面方程推广了传统的极小曲面方程,并且为非参数极小曲面理论提供了新的更一般的研究框架;通过计算径向对称解对应的常微分方程,我们研究了它的一些简单的特解.我们也简单讨论了相应的预定平均曲率和预定高斯曲率问题.     

Projection based scatter depth functions and associated scatter estimators

In this article, we study a class of projection based scatter depth functions proposed by Zuo [Y. Zuo, Robust location and scatter estimators in multivariate analysis, The Frontiers in Statistics, Imperial College Press, 2005. Invited book chapter to honor Peter Bickel on his 65th Birthday]. In order to use the depth function effectively, some favorable properties are suggested for the common scatter depth functions. We show that the proposed scatter depth totally satisfies these desirable properties and its sample version possess strong and uniform consistency. Under some regularity conditions, the limiting distribution of the empirical process of the scatter depth function is derived. We also found that the aforementioned depth functions assess the bounded influence functions.A maximum depth based affine equivariant scatter estimator is induced. The limiting distributions as well as the strong and consistency of the sample scatter estimators are established. The finite sample performance of the related scatter estimator shows that it has a very high breakdown point and good efficiency.     

Asymptotically optimal estimation in the linear regression problem in the case of violation of some classical assumptions

We consider the problem of estimating the unknown parameters of linear regression in the case when the variances of observations depend on the unknown parameters of the model. A two-step method is suggested for constructing asymptotically linear estimators. Some general sufficient conditions for the asymptotic normality of the estimators are found, and an explicit form is established of the best asymptotically linear estimators. The behavior of the estimators is studied in detail in the case when the parameter of the regression model is one-dimensional.     

Robustifying principal component analysis with spatial sign vectors

In this paper, we apply orthogonally equivariant spatial sign covariance matrices as well as their affine equivariant counterparts in principal component analysis. The influence functions and asymptotic covariance matrices of eigenvectors based on robust covariance estimators are derived in order to compare the robustness and efficiency properties. We show in particular that the estimators that use pairwise differences of the observed data have very good efficiency properties, providing practical robust alternatives to classical sample covariance matrix based methods.     

A generative model and a generalized trust region Newton method for noise reduction

In practical applications related to, for instance, machine learning, data mining and pattern recognition, one is commonly dealing with noisy data lying near some low-dimensional manifold. A well-established tool for extracting the intrinsically low-dimensional structure from such data is principal component analysis (PCA). Due to the inherent limitations of this linear method, its extensions to extraction of nonlinear structures have attracted increasing research interest in recent years. Assuming a generative model for noisy data, we develop a probabilistic approach for separating the data-generating nonlinear functions from noise. We demonstrate that ridges of the marginal density induced by the model are viable estimators for the generating functions. For projecting a given point onto a ridge of its estimated marginal density, we develop a generalized trust region Newton method and prove its convergence to a ridge point. Accuracy of the model and computational efficiency of the projection method are assessed via numerical experiments where we utilize Gaussian kernels for nonparametric estimation of the underlying densities of the test datasets.     

Projection-pursuit approach to robust linear discriminant analysis

Discriminant analysis plays an important role in multivariate statistics as a prediction and classification method. It has been successfully applied in many fields of work and research. As it happens with other multivariate methods, discriminant analysis is highly vulnerable to the presence of outliers that commonly occur in many real world data sets. The lack of robustness of the classical estimators on which the linear discriminant function is based is a severe disadvantage and several authors have worked to find efficient ways to prevent the damage that outliers can cause. This paper focuses on the projection-pursuit approach to discriminant analysis. The projection-pursuit estimators are described and theoretical properties are deduced and their relevance is highlighted. These include Fisher consistency, affine equivariance, partial influence functions and asymptotic distributions. Application to real data and a simulation study reveal the robustness of the projection-pursuit approach. In both analyses the data relates to a large number of variables, a situation that is becoming common when new technology is applied to data gathering.     

Lower Rank Approximation of Matrices Based on Fast and Robust Alternating Regression

We introduce fast and robust algorithms for lower rank approximation to given matrices based on robust alternating regression. The alternating least squares regression, also called criss-cross regression, was used for lower rank approximation of matrices, but it lacks robustness against outliers in these matrices. We use robust regression estimators and address some of the complications arising from this approach. We find it helpful to use high breakdown estimators in the initial iterations, followed by M estimators with monotone score functions in later iterations towards convergence. In addition to robustness, the computational speed is another important consideration in the development of our proposed algorithm, because alternating robust regression can be computationally intensive for large matrices. Based on a mix of the least trimmed squares (LTS) and Huber's M estimators, we demonstrate that fast and robust lower rank approximations are possible for modestly large matrices.     

On a robust and efficient maximum depth estimator

The best breakdown point robustness is one of the most outstanding features of the univariate median. For this robustness property, the median, however, has to pay the price of a low efficiency at normal and other light-tailed models. Affine equivariant multivariate analogues of the univariate median with high breakdown points were constructed in the past two decades. For the high breakdown robustness, most of them also have to sacrifice their efficiency at normal and other models, nevertheless. The affine equivariant maximum depth estimator proposed and studied in this paper turns out to be an exception. Like the univariate median, it also possesses a highest breakdown point among all its multivariate competitors. Unlike the univariate median, it is also highly efficient relative to the sample mean at normal and various other distributions, overcoming the vital low-efficiency shortcoming of the univariate and other multivariate generalized medians. The paper also studies the asymptotics of the estimator and establishes its limit distribution without symmetry and other strong assumptions that are typically imposed on the underlying distribution. This work was supported by Natural Science Foundation of USA (Grant Nos. DMS-0071976, DMS-0234078) and by the Southwestern University of Finance and Economics Third Period Construction Item Funds of the 211 Project (Grant No. 211D3T06)     

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.     

Robust Estimation of Tail Parameters for Two-Parameter Pareto and Exponential Models via Generalized Quantile Statistics

Robust estimation of tail index parameters is treated for (equivalent) two-parameter Pareto and exponential models. These distributions arise as parametric models in actuarial science, economics, telecommunications, and reliability, for example, as well as in semiparametric modeling of upper observations in samples from distributions which are regularly varying or in the domain of attraction of extreme value distributions. New estimators of generalized quantile type are introduced and compared with several well-established estimators, for the purpose of identifying which estimators provide favorable trade-offs between efficiency and robustness. Specifically, we examine asymptotic relative efficiency with respect to the (efficient but nonrobust) maximum likelihood estimator, and breakdown point. The new estimators, in particular the generalized median types, are found to dominate well-established and popular estimators corresponding to methods of trimming, least squares, and quantiles. Further, we establish that the least squares estimator is actually deficient with respect to both criteria and should become disfavored. The generalized median estimators manifest a general principle: smoothing followed by medianing produces a favorable trade-off between efficiency and robustness.     

A posteriori error analysis for nonconforming approximations of an anisotropic elliptic problem

We develop in this article an a posteriori error estimator for the P₁‐nonconforming finite element approximation, for a diffusion‐reaction equation. We adopt the error in a constitutive law approach in two and three dimensional space, for not necessary piecewise constant data of problems. The efficiency and the reliability of our estimators are proved, neither Helmholtz decomposition of the error nor saturation assumption. The constants are explicitly given, which prove the robustness of these estimators. © 2014 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 31: 950–976, 2015