利用CATE曲线选择最优治疗方案（英文）

本文基于因果推断理论,提出根据病人的生物标记物进行最优治疗方案选择的统计方法.这种方法是基于CATE (conditional average treatment effect)曲线以及CATE曲线的置信带(SCB)的. CSTE曲线表示给定生物标记物(协变量)的条件下,处理组的条件平均处理效应.同时, CATE曲线及其SCB可以被用于对特定的治疗方案选择适宜的病人.文中利用B样条方法估计CATE曲线及其CSB,并推导了其近似大样本性质.文中还通过模拟比较研究了CATE曲线的置信带的有限样本性质,并阐述了CATE曲线及其置信带在真实数据中如何选择最优治疗方案.     

连续时间下局部光滑统计量的a.s.收敛速度和边界效应

本文在连续时间场合研究回归函数的非参数估计量之一———局部光滑统计量的性质 .不仅给出其a.s.收敛的一个速度 ,而且证明了该统计量不受边界效应影响的优良性质 ;同时指出了在连续时间的场合 ,估计量中窗宽的选择和样本的轨道性质都对该估计量的性质产生重要的影响     

Composite method for bias-corrected and robust kernel estimator

相对于参数估计的均方收敛速度O(n-1),核估计的收敛速度较慢且对窗宽的选择敏感.为了克服上述缺点,本文提出复合核估计法,首先选择不同的窗宽做相应的核估计,然后通过一个参数回归方法将备选的估计量重新组合,所得到的新估计量不采用高阶核即可获得较小的均方误差且对窗宽的选择稳健,从而改进了通常的核估计.模拟研究证实了上述方法的优越性.     

结局变量为二值时利用CSTE曲线选择最优治疗方案

本文对二值结局变量数据,基于因果推断理论,提出根据患者的生物标记物进行最优治疗方案选择的统计方法.这种方法基于CSTE(covariate-specific treatment effect)曲线和CSTE曲线的置信带(SCB).CSTE曲线表示在给定生物标记物(协变量)的条件下,处理组的条件平均处理效应.同时,CSTE曲线及其SCB可以被用于对特定的治疗方案选择适宜的患者.本文利用B-样条方法估计CSTE曲线及其CSB,并推导了其近似大样本性质.本文还通过模拟比较研究了CSTE曲线的置信带的有限样本性质,并阐述了CSTE曲线及其置信带在真实数据中如何选择最优治疗方案.     

变系数模型中的逐元B-Spline估计法

给出了一种用于估计变系数模型中未知函数的逐元B-Spline方法,建立了估计量的局部渐近偏差,方差和渐近正态分布,开发了一种快速选择估计量窗宽的方法,通过Monte Carlo模拟研究了估计量的有限样本性质.     

变系数模型中的逐元B-Spline估计法

给出了一种用于估计变系数模型中未知函数的逐元B-Spline方法,建立了估计量的局部渐近偏差,方差和渐近正态分布,开发了一种快速选择估计量窗宽的方法,通过Monte Carlo模拟研究了估计量的有限样本性质.     

非线性半参数回归模型中参数的经验似然置信域

该文考虑非线性半参数回归模型,构造了模型中未知参数的经验对数似然比统计量,证明了所提出的统计量具有渐近Χ²分布,由此结果可以用来构造未知参数的置信域.另外,该文也构造了未知参数 的最小二乘估计量,并证明了它的渐近性质.仅就置信域精度及其覆盖概率大小方面,通过模拟研究比较了经验似然方法与最小二乘法的优劣.     

带测量误差的线性分位数回归模型的工具变量估计（英文）

本文将工具变量法由研究带变量误差的均值回归模型推广到研究带变量误差的线性分位数回归模型.所得到的估计量是一致的且在一般条件下具有渐近正态分布.这种方法可行且易于操作.模拟研究表明该估计量在有限样本下性质表现非常好.最后这种方法被应用到实际问题,研究工资与教育程度之间的关系.     

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.     

一种改进的半参数极大似然法估计量的渐近性质

本文研究线性回归模型, Y=β'X+∈,并假设Y可被右删失,∈的分布函 数F0未知.本文证明,在某些条件下, β的一种改进的半参数极大似然法估计量β 有相合性. 同时证明,如果F0不连续,则P{β≠βi.o.}=0.这意味着以概率为一, 当样本很大时, β=β.文献中的现有估计量未见有关于这一性质的报道.相反,包括 Buckley-James估计量及M-估计量在内的大多数的估计量,都不满足这一性质.     

Bandwidth selection for the local polynomial estimator under dependence: A simulation study

Summary  Seven of the most popular methods for bandwidth selection in regression estimation are compared by means of a thorough simulation study, when the local polynomial estimator is used and the observations are dependent. The study is completed with two plug-in bandwidths for the generalized local polynomial estimator proposed by Vilar-Fernandez & Francisco-Fernández (2002).     

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.     

Bandwidth selection for a data sharpening estimator in nonparametric regression

This paper is concerned with data-based selection of the bandwidth for a data sharpening estimator in nonparametric regression. Two kinds of bandwidths are considered: a bandwidth vector which has a different bandwidth for each covariate, and a scalar bandwidth that is common for all covariates. A plug-in method is developed and its theoretical performance is fully investigated. The proposed plug-in method works efficiently in our simulation study.     

Weighted Local Nonparametric Regression with Dependent Errors: Study of Real Private Residential Fixed Investment in the USA

This paper presents an overview of the existing literature on the nonparametric local polynomial (LPR) estimator of the regression function and its derivatives when the observations are dependent. When the errors of the regression model are correlated and follow an ARMA process, Vilar-Fernández and Francisco-Fernández (2002) proposed a modification of the LPR estimator, called the generalized local polynomial (GLPR) estimator, based on, first, transforming the regression model to get uncorrelated errors and then applying the LPR estimator to the new model. Some of the most significant asymptotic properties of these two weighted local estimators, including some guidelines on how to select the bandwidth parameter, are reviewed. Finally, these techniques are used to study the real private residential fixed investment in the USA.     

Asymptotic normality of Powell’s kernel estimator

We establish asymptotic normality of Powell’s kernel estimator for the asymptotic covariance matrix of the quantile regression estimator for both i.i.d. and weakly dependent data. As an application, we derive the optimal bandwidth that minimizes the approximate mean squared error of the kernel estimator. We also derive the corresponding results to censored quantile regression.     

Frontier estimation with local polynomials and high power-transformed data

We present a new method for estimating the frontier of a sample. The estimator is based on a local polynomial regression on the power-transformed data. We assume that the exponent of the transformation goes to infinity while the bandwidth goes to zero. We give conditions on these two parameters for obtaining almost complete convergence. The asymptotic conditional bias and variance of the estimator are provided and its good performance is illustrated for some finite sample situations.     

Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors

Local polynomial smoothing for the trend function and its derivatives in nonparametric regression with long-memory, short-memory and antipersistent errors is considered. We show that in the case of antipersistence, the convergence rate of a nonparametric regression estimator is faster than for uncorrelated or short-range dependent errors. Moreover, it is shown that unified asymptotic formulas for the optimal bandwidth and the MSE hold for all of the three dependence structures. Also, results on estimation at the boundary are included. A bandwidth selector for nonparametric regression with different types of dependent errors is proposed. Its asymptotic property is investigated. The practical performance of the proposal is illustrated by simulated and real data examples.     

Robust to noise and outliers estimator of correlation dimension

The estimation of correlation dimension of continuous and discreet deterministic chaotic processes corrupted by an additive noise and outliers observations is investigated. In this paper we propose a new estimator of correlation dimension based on similarity between the evolution of Gaussian kernel correlation sum (Gkcs) and that of modified Boltzmann sigmoidal function (mBsf), this estimator is given by the maximum value of the first derivative of logarithmic transform of Gkcs against logarithmic transform of bandwidth, so the proposed estimator is independent of the choice of regression region like other regression estimators of correlation dimension. Simulation study indicates the robustness of proposed estimator to the presence of different types of noise such us independent Gaussian noise, non independent Gaussian noise and uniform noise for high noise level, moreover, this estimator is also robust to presence of 60% of outliers observations. Application of this new estimator with determination of their confidence interval using the moving block bootstrap method to adjusted closed price of S&P500 index daily time series revels the stochastic behavior of such financial time series.     

Strong convergence in nonparametric regression with truncated dependent data

In this paper we derive rates of uniform strong convergence for the kernel estimator of the regression function in a left-truncation model. It is assumed that the lifetime observations with multivariate covariates form a stationary α-mixing sequence. The estimation of the covariate’s density is considered as well. Under the assumption that the lifetime observations are bounded, we show that, by an appropriate choice of the bandwidth, both estimators of the covariate’s density and regression function attain the optimal strong convergence rate known from independent complete samples.