纵向数据半参数混合效应模型中的估计方法

本文将Tao等(1999)提出的线性混合效应模型推广为半参数混合效应模型,给出了模型参数、回归函数和随机效应密度的估计,并研究了估计的渐近性质.统计模拟表明我们给出的估计方法是可行的.     

一类非参数回归模型核估计的渐近性质

研究一类新的非参数回归模型回归函数的核估计问题,其中误差项为一阶非参数自回归方程.通过重复利用Watson-Nadaraya核估计方法,构造了回归函数及误差回归函数的估计量分别为m(.)和ρ(.),在适当的条件下,证明了估计量m(.)和ρ(.)的渐近正态性.     

纵向数据下半参数混合效应模型的估计

考虑纵向数据下一类半参数混合效应模型.应用核权函数法以及矩估计法给出了总体效应和个体效应的估计.在一般的条件下,证明了总体效应估计的渐近正态性,并给出该估计的置信区域.对总体效应和个体效应的估计进行了模拟研究,模拟显示估计效果较好.     

纵向数据混合效应模型的统计分析

本文研究了Tao等人在1999年提出的半参数混合效应模型,在不假设随机效应服从正态分布的条件下,用傅立叶变换的方法构造了随机效应的光滑非参数密度估计,给出了密度估计的公式,研究了其渐近性质,还构造了半参数混合效应模型中参数的估计方法并研究了其大样本性质。     

纵向数据混合效应模型的统计分析

本文研究了Tao等人在1999年提出的半参数混合效应模型,在不假设随机效应服从正态分布的条件下,用傅立叶变换的方法构造了随机效应的光滑非参数密度估计,给出了密度估计的公式,研究了其渐近性质,还构造了半参数混合效应模型中参数的估计方法并研究了其大样本性质.     

纵向数据半参数回归模型的最小二乘局部线性估计

本文考虑纵向数据半参数回归模型:Yij=XiTjβ+g(Tij)+iεj,基于最小二乘法和局部线性拟合的方法建立了模型中参数分量β,回归函数g(.)和误差方差σ2的估计,在适当条件下给出了估计量的相合性,通过模拟研究说明了该方法在有限样本情况下具有良好的性质。     

半参数混合效应模型中的估计及其强相合性

将Tao等(1999)提出的线性混合效应模型推广为半参数混合效应模型,给出了模型参数、回归函数和随机效应密度的估计,并研究了估计的强相合性及部分强相合速度.统计模拟表明我们给出的估计方法是可行的.     

固定设计下非参数回归模型估计的渐近性质

考虑固定设计下具有非参数AR(1)的非参数回归模型,综合最小二乘和非参数核估计法,定义了非参数函数的估计量,在适当的条件下,研究了它们的渐近性质.     

ψ—混合误差下非参数回归函数加权核估计的相合性

本文在ψ－混合误差下讨论Ｐｒｉｅｓｔｌｅｙ，Ｍ．Ｂ．和Ｃｈａｏ，Ｍ．Ｔ．［１］提出的一类非参数回归函数加权核估计的相合性。在较弱的条件下证明了它的完全收敛性和强相合性。这些结论改进了现有的独立情形和相依情形的相应结论。     

混合样本下非参数回归模型误差的矩相合估计

要考虑非参数回归模型Yi=g(Xi) εi，i=1，2…其中误差（ε，ii≥1)为φ混合随机变量列，且有共同的未知密度f(x)，g(x)=E(Y|X=x)为未知的回归函数，本由基于g(x)的非参数估计gn（x）定义的残差，然后再由基于残差构造的f(x)的估计fn(x)，在适当条件下，证明了fn(x)具有r(1相似文献   

基于函数型非参数方法的气温数据预测分析

基于函数型非参数核回归估计的方法分析安徽省1955年至2010年月度平均气温数据,建立函数型非参数回归模型,并对2010年气温数据进行实证研究.同时,与经典的非参数回归模型的预测结果相比,本文方法的预测均方误差明显优于经典的非参数回归方法,体现出函数型非参数模型的优越性.     

Stahel-Donoho kernel estimation for fixed design nonparametric regression models

This paper reports a robust kernel estimation for fixed design nonparametric regression models. A Stahel-Donoho kernel estimation is introduced, in which the weight functions depend on both the depths of data and the distances between the design points and the estimation points. Based on a local approximation, a computational technique is given to approximate to the incomputable depths of the errors. As a result the new estimator is computationally efficient. The proposed estimator attains a high breakdown point and has perfect asymptotic behaviors such as the asymptotic normality and convergence in the mean squared error. Unlike the depth-weighted estimator for parametric regression models, this depth-weighted nonparametric estimator has a simple variance structure and then we can compare its efficiency with the original one. Some simulations show that the new method can smooth the regression estimation and achieve some desirable balances between robustness and efficiency.     

单相关回归模型的效率及其模拟分析

本文研究一类单相关回归模型的效率及其应用,证明了对单相关回归模型的任一可估函数ｃ′β＝ｃ′（Ｘ′Ｘ）－Ｘ′Ｙ的最小二乘估计（ＬＳ）都是最佳线性一致无偏估计（ＢＬＵ）,给出了这类模型的均方误差比效率的下确界（ｉｎｆρＭＳＥＲ）．同时研究了用最小二乘估计代替最佳线性一致无偏估计时应注意的问题     

Robust nonparametric regression estimation

In this paper we define a robust conditional location functional without requiring any moment condition. We apply the nonparametric proposals considered by C. Stone (Ann. Statist. 5 (1977), 595–645) to this functional equation in order to obtain strongly consistent, robust nonparametric estimates of the regression function. We give some examples by using nearest neighbor weights or weights based on kernel methods under no assumptions whatsoever on the probability measure of the vector (X,Y). We also derive strong convergence rates and the asymptotic distribution of the proposed estimates.     

我国通货膨胀的非参数回归模型

本文首先讨论非参数回归模型的局部核权最小二乘估计 ,然后建立我国通货膨胀非参数回归模型 ,最后研究了反映出口与通货膨胀关系的弹性系数     

Asymptotic relative Pitman efficiency in group models

The notion of asymptotic efficacy due to Hannan for multivariate statistics in a location problem is reformulated for manifolds. The matrices used in Hannan's definition are reformulated as Riemannian metrics on a manifold and hence are seen not to depend upon the particular parameterization of the manifold used to make the calculations. Conditions under which that efficacy does not depend upon basepoint and direction are derived. This leads to the extension of Pitman asymptotic relative efficiency to location parameters in group models. Under stronger conditions, that of a two-point homogeneous space, we introduce a notion of rank and sign and show that, under the null distribution, the sign is uniformly distributed on a suitably defined sphere and that the rank is independent of the sign. This work generalizes previous definitions of Neeman and Chang, Hössjer and Croux. For group models, a definition of a regression group model is given. Unlike the usual linear model, a location model is not a subcase of a regression group model. Nevertheless, it is shown that the Riemannian metrics for the regression model can be derived from those of the location model and hence, in many cases, the asymptotic relative efficiencies coincide for group and location models. As examples, rank score statistics for spherical and Procrustes regressions are derived. The Procrustes regression model arises in problems of image registration.     

On the role of the shrinkage parameter in local linear smoothing

Summary. It has been shown that local linear smoothing possesses a variety of very attractive properties, not least being its mean square performance. However, such results typically refer only to asymptotic mean squared error, meaning the mean squared error of the asymptotic distribution, and in fact, the actual mean squared error is often infinite. See Seifert and Gasser (1996). This difficulty may be overcome by shrinking the local linear estimator towards another estimator with bounded mean square. However, that approach requires information about the size of the shrinkage parameter. From at least a theoretical viewpoint, very little is known about the effects of shrinkage. In particular, it is not clear how small the shrinkage parameter may be chosen without affecting first-order properties, or whether infinitely supported kernels such as the Gaussian require shrinkage in order to achieve first-order optimal performance. In the present paper we provide concise and definitive answers to such questions, in the context of general ridged and shrunken local linear estimators. We produce necessary and sufficient conditions on the size of the shrinkage parameter that ensure the traditional mean squared error formula. We show that a wide variety of infinitely-supported kernels, with tails even lighter than those of the Gaussian kernel, do not require any shrinkage at all in order to achieve traditional first-order optimal mean square performance. Received: 22 May 1995 / In revised form: 23 January 1997