半变系数模型的变窗宽和一步局部M-估计

讨论了半变系数模型的变窗宽一步局部M-估计.用一步局部M-估计给出了未知函数的估计,用平均法给出了未知参数的估计,并在其中嵌入一个变窗宽加以提高,得到了未知函数和未知参数的渐近正态性.     

变窗宽和一步局部M-估计 *

研究稳健的变窗宽局部线性回归 .所提出的方法继承了局部多项式回归的优点并且克服了最小二乘方法缺乏稳健性的缺点 .变窗宽的使用提高了所得到的局部M- 估计的可塑性并使得它们能成功地处理空间非齐性曲线、异方差性及非均匀设计密度 .在合适的正规条件下 ,所提出的估计是存在的且是渐近正态的 .基于稳健的估计方程 ,引进了一步局部M- 估计以减少计算负担 .只要初始估计足够好 ,一步局部估计将具有与整个迭代的M- 估计相同的渐近分布 .换句话说 ,一步局部M- 估计显著地减少整个迭代M- 估计的计算负担而不降低其执行效果 .模拟也说明了这个事实.     

部分线性回归模型的M-估计

本文讨论部分线性回归模型的M-估计．用局部线性方法给出未知函数的M-估计，用两步估计方法给出参数的M-估计．进一步证明了未知函数的M-估计的弱一致性和渐近正态性，参数的M-估计的弱一致性．     

变系数模型变窗宽和一步局部M-估计

用变窗宽和一步局部M-估计对变系数模型的系数参数进行估计,得到了估计的渐近正态性.     

可加模型的两阶段局部M-估计

研究了可加模型分量回归函数的局部M-估计, 针对分量回归函数及其导数提出了两阶段局部M-估计的方法. 在较广泛的条件下建立了估计量的渐近正态性理论, 估计量具有先知性质(oracle property), 即在估计某一分量回归函数时,其他分量回归函数是否已知不影响估计量的渐近性质. 渐近理论包括了两类常用的估计量,即最小二乘估计和最小一乘估计. 当ψ是连续的且是非线性时,估计量的实施非常耗时,为了减轻计算的负担, 提出了一步局部M-估计量, 并证明了在初始估计量足够好的情形下, 一步局部M-估计量与完全迭代所得到的估计量具有相同的渐近估计效率, 这使得两阶段局部M-估计的方法较为实用. 两阶段局部M-估计量继承了局部多项式估计的优点, 同时克服了其在最小二乘准则下不稳健的缺点. 另外, 还讨论了估计方法实施方面的细节及有关参数的选择方法. 数值模拟结果及实际例子说明了两阶段局部M-估计方法的优点及实用性.     

变系数模型变窗宽局部M-估计的相合性

用变窗宽局部M-估计对变系数模型的系数函数进行估计,得到了估计的相合性和渐近正态性.所采用的方法继承了局部多项式回归的优点并且克服了最小二乘方法缺乏稳健性的缺点.变窗宽的使用提高了局部M-估计的可塑性,并使得它们能成功地处理空间非齐性曲线、异方差性及非均匀设计密度.     

p混合过程下变窗宽局部M-估计的强相合性

考虑到在实际应用中,运用变窗宽局部M-估计进行非参数估计时,所收集到的数据有时并非独立样本,而可能是一些混合样本.因此,本文就观测数据为ρ混合过程的条件下,讨论了变窗宽局部M-估计的强相合性,并给出两个具有较弱假设条件的定理.     

严格对角占优M-矩阵的逆矩阵的无穷大范数的新上界

给出了严格对角占优M-矩阵的逆矩阵的无穷大范数上界新的估计式,进而给出严格对角占优M-矩阵的最小特征值下界的估计式.新估计式改进了已有文献的结果.     

M-群逆的位移结构

本文讨论方阵的M-群逆,利用方阵的M-群逆给出AW#U-VAW#的位移秩估计.     

非奇异M-矩阵的逆矩阵和M-矩阵的Hadamard积的最小特征值下界估计

给出非奇异M-矩阵的逆矩阵和M-矩阵的Hadamard积的最小特征值下界新的估计式,这些估计式都只依赖于矩阵的元素.数值例子表明,新估计式在一定条件下改进了Fiedler和Markham的猜想,也改进了其它已有的结果.     

Two-stage local M-estimation of additive models

This paper studies local M-estimation of the nonparametric components of additive models.A two-stage local M-estimation procedure is proposed for estimating the additive components and their derivatives.Under very mild conditions,the proposed estimators of each additive component and its derivative are jointly asymptotically normal and share the same asymptotic distributions as they would be if the other components were known.The established asymptotic results also hold for two particular local M-estimations:the local least squares and least absolute deviation estimations.However,for general two-stage local M-estimation with continuous and nonlinear ψ-functions,its implementation is time-consuming.To reduce the computational burden,one-step approximations to the two-stage local M-estimators are developed.The one-step estimators are shown to achieve the same effciency as the fully iterative two-stage local M-estimators,which makes the two-stage local M-estimation more feasible in practice.The proposed estimators inherit the advantages and at the same time overcome the disadvantages of the local least-squares based smoothers.In addition,the practical implementation of the proposed estimation is considered in details.Simulations demonstrate the merits of the two-stage local M-estimation,and a real example illustrates the performance of the methodology.     

Sieve M-estimation for semiparametric varying-coefficient partially linear regression model

This article considers a semiparametric varying-coefficient partially linear regression model.The semiparametric varying-coefficient partially linear regression model which is a generalization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable.A sieve M-estimation method is proposed and the asymptotic properties of the proposed estimators are discussed.Our main object is to estimate the nonparametric component and the unknown parameters simultaneously.It is easier to compute and the required computation burden is much less than the existing two-stage estimation method.Furthermore,the sieve M-estimation is robust in the presence of outliers if we choose appropriate ρ( ).Under some mild conditions,the estimators are shown to be strongly consistent;the convergence rate of the estimator for the unknown nonparametric component is obtained and the estimator for the unknown parameter is shown to be asymptotically normally distributed.Numerical experiments are carried out to investigate the performance of the proposed method.     

系数函数光滑度互不相同的变系数模型的一步估计法

该文提出了一种一步估计方法用以估计变系数模型中具有互不相同光滑度的未知函数, 所有未知函数和它们的导数的估计量由 一次极小化得到. 给出了估计量的渐近性质, 包括渐近偏差、方差和渐近分布, 一步估计量被证明达到了最优收敛速度.     

Efficient Quantile Estimation for Functional-Coefficient Partially Linear Regression Models

The quantile estimation methods are proposed for functional-coefficient partially linear regression (FCPLR) model by combining nonparametric and functional-coefficient regression (FCR) model. The local linear scheme and the integrated method are used to obtain local quantile estimators of all unknown functions in the FCPLR model. These resulting estimators are asymptotically normal, but each of them has big variance. To reduce variances of these quantile estimators, the one-step backfitting technique is used to obtain the efficient quantile estimators of all unknown functions, and their asymptotic normalities are derived. Two simulated examples are carried out to illustrate the proposed estimation methodology.     

Efficient estimation of functional-coefficient regression models with different smoothing variables

In this article,a procedure for estimating the coefficient functions on the functional-coefficient regression models with different smoothing variables in different coefficient functions is defined.First step,by the local linear technique and the averaged method,the initial estimates of the coefficient functions are given.Second step,based on the initial estimates,the efficient estimates of the coefficient functions are proposed by a one-step back-fitting procedure.The efficient estimators share the same asymptotic normalities as the local linear estimators for the functional-coefficient models with a single smoothing variable in different functions.Two simulated examples show that the procedure is effective.     

不同自变量的变系数模型的估计

讨论具有不同自变量的变系数模型的函数系数的估计及其大样本性质。使用局部线性方法和积分方法,得到函数系数的积分估计;由于该估计有较大的方差,进一步使用回切法改进这一估计,获得了函数系数的改进估计;同时,研究了改进估计的渐近正态性。最后,用模拟例子说明提出的估计方法是有效的。     

One-step estimation for varying coefficient models

A one-step method is proposed to estimate the unknown functions in the varying coefficient models, in which the unknown functions admit different degrees of smoothness. In this method polynomials of different orders are used to approximate unknown functions with different degrees of smoothness. As only one minimization operation is employed, the required computation burden is much less than that required by the existing two-step estimation method. It is shown that the one-step estimators also achieve the optimal convergence rate. Moreover this property is obtained under conditions milder than that imposed in the two-step estimation method. More importantly, as only one minimization operation is employed, the full asymptotic properties, not only the asymptotic bias and variance, but also the asymptotic distributions of the estimators can be derived. The asymptotic distribution results will play a key role for making statistical inference.     

Variable bandwidth and one-step local M-estimator

A robust version of local linear regression smoothers augmented with variable bandwidth is studied. The proposed method inherits the advantages of local polynomial regression and overcomes the shortcoming of lack of robustness of least-squares techniques. The use of variable bandwidth enhances the flexibility of the resulting local M-estimators and makes them possible to cope well with spatially inhomogeneous curves, heteroscedastic errors and nonuniform design densities. Under appropriate regularity conditions, it is shown that the proposed estimators exist and are asymptotically normal. Based on the robust estimation equation, one-step local M-estimators are introduced to reduce computational burden. It is demonstrated that the one-step local M-estimators share the same asymptotic distributions as the fully iterative M-estimators, as long as the initial estimators are good enough. In other words, the one-step local M-estimators reduce significantly the computation cost of the fully iterative M-estimators without deteriorating their performance. This fact is also illustrated via simulations.     

基于纵向数据的半参数变系数部分线性回归模型

纵向数据是数理统计研究中的复杂数据类型之一0,在生物、医学和经济学中具有广泛的应用．在实际中经常需要对纵向数据进行统计分析和建模．文章讨论了纵向数据下的半参数变系数部分线性回归模型,这里的纵向数据的在纵向观察在时间上可以是不均等的,也可看成是按某一随机过程来发生．所研究的半参数变系数模型包括了许多半参数模型,比如部分线性模型和变系数模型等．利用计数过程理论和局部线性回归方法,对于纵向数据下半参数变系数进行了统计推断,给出了参数分量和非参数分量的profile最小二乘估计,研究了这些估计的渐近性质,获得这些估计的相合性和渐近正态性．