删失指标随机缺失下一类非参数函数的加权局部多项式估计及其应用

本文在右删失数据中删失指标部分随机缺失下,构造了一类非参数函数的校准加权局部多项式估计以及插值加权局部多项式估计,并建立了这些估计的渐近正态性;作为该方法的应用,导出了条件分布函数、条件密度函数以及条件分位数的加权局部线性双核估计和插值加权局部线性双核估计,并且得到了这些估计的渐近正态性;最后,在有限样本下对这些估计进行了模拟.     

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本文给出了条件三阶中心矩的局部线性估计, 并研究了估计的条件偏差和方差. 本文利用广义交叉核实法(GCV)进行窗宽选择. 我们通过模拟说明了该估计的实用性     

可加模型的无交叉分位回归曲线与房价问题研究

高维数据分析是当前研究的热点话题,而在对其进行分析时,非参数方法由于其灵活,无需对模型进行假定,得到了广泛的发展和认可。其中可加模型不仅能够有效地对变量进行降维,避免"维数灾难"的发生;而且能够得到各个变量的边际效应,具有很好的解释性。为了得到更加稳健的估计量,本文考虑利用分位回归方法对可加模型进行估计。分位回归方法由于其能够全面地刻画因变量在各个分位点上的变化趋势,并不受误差分布的限制,使得该方法具有更广泛的应用性。本文综合考虑以上优势,提出局部线性最小化检验函数估计方法和局部线性双核估计方法对可加模型进行估计。并且该方法能够有效地避免可加模型分位回归曲线的交叉问题.蒙特卡洛结果显示,与传统的均值估计法相比,不论误差分布的形式,我们提出的方法更具有优越性。用北京市二手房房价数据进行实证分析,进一步验证了本文提出的估计方法。     

变系数联立模型的局部线性广义矩变窗宽估计

在随机设计条件下,提出了一类变系数联立模型,运用局部线性广义矩变窗宽估计,对模型的变系数进行了估计,研究了估计量的大样本性质.利用概率论中大数定律和中心极限定理,证明了估计量的大样本性质,局部线性广义矩变窗宽估计具有相合性和渐进正态性.     

左截断数据下非参数回归模型的复合分位数回归估计

利用局部多项式方法研究了误差具有异方差结构的非参数回归模型,在左截断数据下构造了回归函数的复合分位数回归估计,并得到了该估计的渐近正态性结果,最后通过模拟,在服从一些非正态分布的误差下,得到该估计比局部线性估计更有效.     

纵向数据半参数回归模型的估计方法

本文考虑纵向数据半参数回归模型,通过考虑纵向数据的协方差结构,基于Profile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、回归函数和误差方差的估计量,来提高估计的有效性,在适当条件下给出了这些估计量的相合性.并通过模拟研究将该方法与最小二乘局部线性拟合估计方法进行了比较,表明了Profile最小二乘局部线性拟合方法在有限样本情况下具有良好的性质.     

非时齐扩散模型中时变参数的局部线性估计

本文主要研究非时齐扩散模型中时变的漂移参数和扩散参数的局部线性估计。基于非时齐扩散模型的离散观测样本,首先得到了漂移参数的局部线性估计及其标准误差。然后,考虑到扩散参数的非负性,本文利用局部对数线性拟合的方法得到了扩散参数的核函数加权估计,并讨论了扩散项估计的渐近偏差、渐近方差和渐近正态性。最后,通过模拟研究表明所得局部估计有很好的拟合效果。     

强相依数据的函数系数部分线性模型的估计

本文讨论在数据是强相依的情况下函数系数部分线性模型的估计．首先,采用局部线性方法,给出该模型函数项函数的估计;然后,使用两阶段方法给出系数函数的估计．并且讨论了函数项函数估计的渐近正态性,以及系数函数估计的弱相合性和渐近正态性.模拟研究显示,这些估计是较为理想的．     

数据缺失情形线性回归模型中误差方差的经验似然估计

在随机缺失(MAR)机制下利用经验似然方法构造了线性回归模型中误差方差的估计.并在一定条件下,证明了该估计的渐近正态性,由此得出当误差的分布不对称时,该估计的渐近方差比常用估计的渐近方差小.     

大气动力学方程的周期边界问题

在本文中,我们研究一类多自变量拟线性双曲方程组的周期边界问题。利用积分估计和Schauder不动点定理,在较弱的条件下,证明了其局部连续可微解的存在性与唯一性。     

Comparison of presmoothing methods in kernel density estimation under censoring

The behavior of the presmoothed density estimator is studied when different ways to estimate the conditional probability of uncensoring are used. The Nadaraya–Watson, local linear and local logistic approach are compared via simulations with the classical Kaplan–Meier estimator. While the local logistic presmoothing estimator presents the best performance, the relative benefits of the local linear versus the Nadaraya–Watson estimator depend very much on the shape of some underlying functions.     

Simple and efficient improvements of multivariate local linear regression

This paper studies improvements of multivariate local linear regression. Two intuitively appealing variance reduction techniques are proposed. They both yield estimators that retain the same asymptotic conditional bias as the multivariate local linear estimator and have smaller asymptotic conditional variances. The estimators are further examined in aspects of bandwidth selection, asymptotic relative efficiency and implementation. Their asymptotic relative efficiencies with respect to the multivariate local linear estimator are very attractive and increase exponentially as the number of covariates increases. Data-driven bandwidth selection procedures for the new estimators are straightforward given those for local linear regression. Since the proposed estimators each has a simple form, implementation is easy and requires much less or about the same amount of effort. In addition, boundary corrections are automatic as in the usual multivariate local linear regression.     

Estimation locale linéaire de la densité conditionnelle pour des données fonctionnelles

In this Note, we introduce the local linear estimation of the conditional density of a scalar response variable given a random variable taking values in a semi-metric space. Under some general conditions, we establish the pointwise and uniform almost complete convergences with rates of this estimator. Moreover, as an application, we use the obtained results to derive some asymptotic properties for the local linear estimator of the conditional mode.     

Nonparametric estimation of conditional medians for linear and related processes

We consider nonparametric estimation of conditional medians for time series data. The time series data are generated from two mutually independent linear processes. The linear processes may show long-range dependence. The estimator of the conditional medians is based on minimizing the locally weighted sum of absolute deviations for local linear regression. We present the asymptotic distribution of the estimator. The rate of convergence is independent of regressors in our setting. The result of a simulation study is also given.     

Data sharpening methods in multivariate local quadratic regression

This paper is concerned with the conditional bias and variance of local quadratic regression to the multivariate predictor variables. Data sharpening methods of nonparametric regression were first proposed by Choi, Hall, Roussion. Recently, a data sharpening estimator of local linear regression was discussed by Naito and Yoshizaki. In this paper, to improve mainly the fitting precision, we extend their results on the asymptotic bias and variance. Using the data sharpening estimator of multivariate local quadratic regression, we are able to derive higher fitting precision. In particular, our approach is simple to implement, since it has an explicit form, and is convenient when analyzing the asymptotic conditional bias and variance of the estimator at the interior and boundary points of the support of the density function.     

Z3-CONNECTIVITY OF 4-EDGE-CONNECTED TRIANGULAR GRAPHS

A graph G is k-triangular if each of its edge is contained in at least k triangles. It is conjectured that every 4-edge-connected triangular graph admits a nowhere-zero 3-flow. A triangle-path in a graph G is a sequence of distinct triangles T_1 T_2··· T_k in G such that for 1 ≤ i ≤ k-1, |E(T_i) ∩ E(T_(i+1))| = 1 and E(T_i) ∩ E(T_j) = ? if j i + 1. Two edges e, e′∈ E(G) are triangularly connected if there is a triangle-path T_1, T_2, ···, T_k in G such that e ∈ E(T_1)and e′∈ E(T_k). Two edges e, e′∈ E(G) are equivalent if they are the same,parallel or triangularly connected. It is easy to see that this is an equivalent relation. Each equivalent class is called a triangularly connected component.In this paper, we prove that every 4-edge-connected triangular graph G is Z_3-connected, unless it has a triangularly connected component which is not Z_3-connected but admits a nowhere-zero 3-flow.     

Uniform law of the logarithm for the local linear estimator of the conditional distribution function

In this Note, we first recall the results of the behaviour of the nonparametric estimator of the conditional distribution function which we can find in the literature. We establish exact rate of strong uniform consistency for the local linear estimator of the conditional distribution function. Our methods of proofs are based upon modern empirical process theory in the spirit of the results of Einmahl and Mason (2000) [5] and Deheuvels and Mason (2004) [3].     

约束线性模型的条件部分根方估计

对于线性约束下的线性回归模型,针对设计矩阵的病态问题,提出一种条件部分根方估计.并在均方误差矩阵准则和Pitman Closeness准则下,比较了条件部分根方估计相对于约束最小二乘估计的优良性.     

Confidence Intervals of Variance Functions in Generalized Linear Model

In this paper we introduce an appealing nonparametric method for estimating variance and conditional variance functions in generalized linear models （GLMs）, when designs are fixed points and random variables respectively, Bias-corrected confidence bands are proposed for the （conditional） variance by local linear smoothers. Nonparametric techniques are developed in deriving the bias-corrected confidence intervals of the （conditional） variance. The asymptotic distribution of the proposed estimator is established and show that the bias-corrected confidence bands asymptotically have the correct coverage properties. A small simulation is performed when unknown regression parameter is estimated by nonparametric quasi-likelihood. The results are also applicable to nonparamctric autoregressive times series model with heteroscedastic conditional variance.