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781.
In non-parametric function estimation selection of a smoothing parameter is one of the most important issues. The performance of smoothing techniques depends highly on the choice of this parameter. Preferably the bandwidth should be determined via a data-driven procedure. In this paper we consider kernel estimators in a white noise model, and investigate whether locally adaptive plug-in bandwidths can achieve optimal global rates of convergence. We consider various classes of functions: Sobolev classes, bounded variation function classes, classes of convex functions and classes of monotone functions. We study the situations of pilot estimation with oversmoothing and without oversmoothing. Our main finding is that simple local plug-in bandwidth selectors can adapt to spatial inhomogeneity of the regression function as long as there are no local oscillations of high frequency. We establish the pointwise asymptotic distribution of the regression estimator with local plug-in bandwidth. 相似文献
782.
Measurement error is an important problem that has not been studied very well in the context of functional data analysis. To the best of our knowledge, there are no existing methods that address the presence of functional measurement errors in generalized functional linear models. In this article, a novel approach is proposed to estimate the slope function in the presence of measurement error in the generalized functional linear model with a scalar response. This work significantly advances the existing conditional score method to accommodate the case where both the measurement error and independent variables lie in infinite dimensional spaces. Asymptotic results are established for the proposed estimate, and its behaviour is studied via simulations, where the response is continuous or binary. Analysis of Canadian Weather data highlights the practical utility of our method. The Canadian Journal of Statistics 48: 238–258; 2020 © 2020 Statistical Society of Canada 相似文献
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785.
This paper focuses on a situation in which a set of treatments is associated with a response through a set of supplementary variables in linear models as well as discrete models. Under the situation, we demonstrate that the causal effect can be estimated more accurately from the set of supplementary variables. In addition, we show that the set of supplementary variables can include selection variables and proxy variables as well. Furthermore, we propose selection criteria for supplementary variables based on the estimation accuracy of causal effects. From graph structures based on our results, we can judge certain situations under which the causal effect can be estimated more accurately by supplementary variables and reliably evaluate the causal effects from observed data. 相似文献
786.
Seemingly unrelated regression (SUR) method is applied to the instrumental variable (IV) estimation of the canonical contagion models. A finite sample Monte Carlo experiment shows that the resulting estimator, IV-SUR estimator, is substantially better than the existing IV estimator in terms of both bias and mean squares error under diverse circumstance of instrument, conditional heteroscedasticity, and cross-section correlation. 相似文献
787.
一个园区网上的用户信息管理系统 总被引:2,自引:0,他引:2
作为一个连入Internet的大型园区网络AMMSNet,用户通过MSProxy2.0作为代理服务器对Internet进行访问,以MSExchange5.5提供邮件服务。在这种模式下,如何对用户的基本信息进行维护管理,对用户的访问实施监控并对其访问信息进行长期保存,完成用户数据流量的统计,实现用户管理的自动化,一直是Internet服务商和企业级网络管理员需要解决的首要问题。本文以某大型园区网络为例,介绍了利用C Builder与SQLServer数据库开发的Internet用户信息管理系统,以实现对用户访问Internet的一系列自动化管理。 相似文献
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789.
非参数计量经济联立模型的变窗宽估计理论 总被引:4,自引:0,他引:4
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. 文章将非参
数回归模型的局部线性估计方法与传统联立方程模型估计方法相结合,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工具变量变窗宽估计并利
用概率论中大数定理和中心极限定理等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 相似文献
790.
Variable selection is a very important tool when dealing with high dimensional data. However, most popular variable selection methods are model based, which might provide misleading results when the model assumption is not satisfied. Sufficient dimension reduction provides a general framework for model-free variable selection methods. In this paper, we propose a model-free variable selection method via sufficient dimension reduction, which incorporates the grouping information into the selection procedure for multi-population data. Theoretical properties of our selection methods are also discussed. Simulation studies suggest that our method greatly outperforms those ignoring the grouping information. 相似文献