辅助信息下的逆抽样设计算法

通过将逆抽样设计视为一种特殊的二重抽样,建立了二重抽样和为回归估计的二重抽样的一般形式,得到了逆抽样设计算法下的回归估计.模拟分析的结果表明,以回归估计的形式引入较为合适的辅助信息,能够在估计精度上对逆抽样设计算法做出改进.     

基于局部多项式回归的模型校准抽样估计研究

在抽样估计中,当研究变量与辅助变量之间呈非线性关系时,传统的校准估计方法效果较差,基于非参数回归方法的模型校准估计量则可以很好地解决这一问题。首先,建立描述研究变量和辅助变量之间关系的超总体回归模型,使用非参数中的局部多项式方法得出模型参数的拟合值,并结合校准估计得出局部多项式模型校准估计量,同时给出其方差和方差估计量公式,证明了该估计量具有渐近无偏性、一致性和渐近正态性等优良的统计性质。然后,使用仿真模拟的方法证明在研究变量与研究变量之间呈非线性关系时,该估计量有良好的估计效果。最后,对该估计量在我国政府统计中的应用进行简单的介绍。     

基于岭回归模型大数据最优子抽样算法研究

随着大数据时代的来临,为了提高计算效率,Wang等(2018)提出基于logistic回归的最优子抽样算法,在保证参数估计精度的前提下,节省了大量的运算时间.为解决变量间的多重共线性,文章提出基于岭回归模型的最优子抽样算法,并证明岭回归模型中参数估计的一致性与渐近正态性.利用数值模拟与实证分析对最优子抽样算法进行评估,...     

半参数回归估计的相合条件

设有半参数回归模型ｙｉ＝ｘ’，β＋ｆ（ｔｉ）＋ｅｉ，ｉ＝１，．．．ｎ，其中｛ｔｉ｝为常数列，本文对｛ｘｉ｝为设计点列的情形，给出了β、ｆ（ｔ）有相合估计的条件；在｛ｘｉ｝为随机的情形，建立了ｆ（ｔ）估计的相合性。     

基于局部多项式展开的多元非参数模型贝叶斯带宽选择

在多元非参数模型中带宽和阶的选择对局部多项式估计量的表现十分重要。本文基于交叉验证准则提出一个自适应贝叶斯带宽选择方法。在给定的误差密度函数下,该方法可推导出对应的似然函数,并构造带宽参数的后验密度函数。随后,通过带宽的后验期望可同时获得阶和带宽的估计。数值模拟的结果表明,该方法不仅比大拇指准则方法精确,且比交叉验证方法耗时更少。与此同时,与Nadaraya-Watson估计相比,所提带宽选择方法对多元非参数模型的适应性要更好。最后,本文通过一组实际数据说明有限样本下所提贝叶斯带宽选择的表现很好。     

解非线性最小二乘问题的锥模型算法的局部收敛性

1 引言 对于非线性最小二乘问题 minf(x)=(1/2)sum from x=1 to m (r_i(x))~2=(1/2)R(x)~TR(x), (1.1)其中R(x)=(r_1(x),…,r_m(x))~T:DR~n→R~m,m≥n,有 g(x)=f'(x)=J(x)~TR(x), (1.2) H(x)=f(x)=J(x)~TJ(x)+sum from x=1 to m r_i(x)r_i(x), (1.3)其中J(x)=((r_i(x))/x_j)·Gauss-Newton方法,及Dennis等的改进方法,都是采用二次模型     

基于两步子抽样算法的多目标抽样统计推断研究

针对海量数据,子抽样算法是当前一种流行的简化计算和降低计算成本的方法。现阶段的研究主要集中于单目标变量的估计上。多目标抽样也是现实生活中经常遇到的问题。本文提出基于广义线性模型,多目标抽样的均值两步子抽样算法。两步子抽样算法是Wang等(2018)^[1]提出的基于L-最优和A-最优的思想,确定每个抽样单元的入样概率。本文在此基础上,定义多目标抽样的各单元的入样概率,并推导模型参数估计量的渐近性质,最后用模拟数据和实际例子对均值两步子抽样算法和多目标两步子抽样方法进行比较。结果表明,在样本量相同时,A-最优准则下均值两步子抽样算法在估计精度上优于基于两步子抽样算法的MPPS抽样和L-最优准则下均值多目标两步子抽样算法。在计算效率上也较全样本估计有显著的提高,节约了计算时间。     

确定高精度参数问题的评注

给出了2006年全国研究生数学建模B赛题的背景,对参赛者在该赛题中所出现的普遍问题进行了分析,并给出了求解思路和参考答案.     

非参数计量经济联立模型的局部线性两阶段最小二乘估计

联立方程模型在经济政策制定，经济结构分析和经济预测方面起重要作用，本在随机设计（模型中所有变量为随机变量）下，提出了非参数计量经济联立模型的局部线性两阶段最小二乘估计并利用概率论中大数定理和中心极限定理在内点处研究了它的大样本性质，证明了它的一致性和渐近正态性，它在内点处的收敛速度达到了非参数函数估计的最优收敛速度。     

The Fast RODEO for Local Polynomial Regression

An open challenge in nonparametric regression is finding fast, computationally efficient approaches to estimating local bandwidths for large datasets, in particular in two or more dimensions. In the work presented here, we introduce a novel local bandwidth estimation procedure for local polynomial regression, which combines the greedy search of the regularization of the derivative expectation operator (RODEO) algorithm with linear binning. The result is a fast, computationally efficient algorithm, which we refer to as the fast RODEO. We motivate the development of our algorithm by using a novel scale-space approach to derive the RODEO. We conclude with a toy example and a real-world example using data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite validation study, where we show the fast RODEO’s improvement in accuracy and computational speed over two other standard approaches.     

Polynomial Regression with Censored Data based on Preliminary Nonparametric Estimation

Consider the polynomial regression model , where σ²(X)=Var(Y|X) is unknown, and ε is independent of X and has zero mean. Suppose that Y is subject to random right censoring. A new estimation procedure for the parameters β₀,...,β _p is proposed, which extends the classical least squares procedure to censored data. The proposed method is inspired by the method of Buckley and James (1979, Biometrika, 66, 429–436), but is, unlike the latter method, a noniterative procedure due to nonparametric preliminary estimation of the conditional regression function. The asymptotic normality of the estimators is established. Simulations are carried out for both methods and they show that the proposed estimators have usually smaller variance and smaller mean squared error than the Buckley–James estimators. The two estimation procedures are also applied to a medical and an astronomical data set.     

Recursive Estimation of Regression Functions by Local Polynomial Fitting

The recursive estimation of the regression function m(x) = E(Y/X = x) and its derivatives is studied under dependence conditions. The examined method of nonparametric estimation is a recursive version of the estimator based on locally weighted polynomial fitting, that in recent articles has proved to be an attractive technique and has advantages over other popular estimation techniques. For strongly mixing processes, expressions for the bias and variance of these estimators are given and asymptotic normality is established. Finally, a simulation study illustrates the proposed estimation method.     

A Note on the Nonparametric Least-squares Test for Checking a Polynomial Relationship

Recently, Gijbels and Rousson[6] suggested a new approach, called nonparametric least-squares test, to check polynomial regression relationships. Although this test procedure is not only simple but also powerful in most cases, there are several other parameters to be chosen in addition to the kernel and bandwidth.As shown in their paper, choice of these parameters is crucial but sometimes intractable. We propose in this paper a new statistic which is based on sample variance of the locally estimated pth derivative of the regression function at each design point. The resulting test is still simple but includes no extra parameters to be determined besides the kernel and bandwidth that are necessary for nonparametric smoothing techniques. Comparison by simulations demonstrates that our test performs as well as or even better than Gijbels and Rousson‘s approach.Furthermore, a real-life data set is analyzed by our method and the results obtained are satisfactory.     

A Fast and Stable Updating Algorithm for Bivariate Nonparametric Curve Estimation

Abstract

An updating algorithm for bivariate local linear regression is proposed. Thereby, we assume a rectangular design and a polynomial kernel constrained to rectangular support as weight function. Results of univariate regression estimators are extended to the bivariate setting. The updates are performed in a way that most of the well-known numerical instabilities of a naive update implementation can be avoided. Some simulation results illustrate the properties of several algorithms with respect to computing time and numerical stability.     

Universal Consistency of Local Polynomial Kernel Regression Estimates

Regression function estimation from independent and identically distributed data is considered. The L ₂ error with integration with respect to the design measure is used as an error criterion. It is shown that suitably defined local polynomial kernel estimates are weakly and strongly universally consistent, i.e., it is shown that the L ₂ errors of these estimates converge to zero almost surely and in L ₁ for all distributions.     

删失数据下线性回归模型的参数估计

主要叙述在数据观测不完全的情况下,采用最小二乘法对线性回归模型回归系数的估计及估计量的渐进性质,并给出数据模拟．     

Linear Least Squares Estimation of Regression Models for Two-Dimensional Random Fields

We consider the problem of estimating regression models of two-dimensional random fields. Asymptotic properties of the least squares estimator of the linear regression coefficients are studied for the case where the disturbance is a homogeneous random field with an absolutely continuous spectral distribution and a positive and piecewise continuous spectral density. We obtain necessary and sufficient conditions on the regression sequences such that a linear estimator of the regression coefficients is asymptotically unbiased and mean square consistent. For such regression sequences the asymptotic covariance matrix of the linear least squares estimator of the regression coefficients is derived.