The results of numerical experiments are presented.TomaszGakowskiGałkowski, T.: Kernel estimation of regression functions in the boundary regions. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS (...
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Kernel estimation of distribution functions and quantiles with missing data A distribution-free imputation procedure based on nonparametric kernel regression is proposed to estimate the distribution function and quantiles of a random variable that is incompletely observed. Assuming the baseline missing-at-ran...
Kernel Estimation of the Derivative of the Regression Function Using Repeated-Measurements DataNonparametric statisticsRegression analysisCorrelationFunctions(Mathematics)EstimatesBandwidthOptimizationLimitationsGraphsGrowth(General)In fixed design kernel nonparametic regression, there has been a paucity of results for...
In this paper, we investigate a modified kernel estimation of the regression function r(x)=E(Y|X=x). The main goal of this paper is to establish the consistency and the rate of convergence of such a modified kernel estimator for strong mixing functional data with only E|Y|<∞, which ...
Journal of the Royal Statistical Society: Series B (Methodological), 58(3), 551-563. Deng, H., & Wickham, H. (2011). Density estimation in R. Electronic publication. Gasser, T., & Müller, H. G. (1979). Kernel estimation of regression functions. In Smoothing techniques for curve ...
Local Regression Local Likelihood Kernel Density estimation Naive Bayes Radial Basis Functions Mixture Models and EM 6.1一维核平滑器(One-Dimensional Kernel Smoothers) K-近邻平均(k–nearest-neighbor average,KNN)可以作为条件期望的估计: 其中Nk是L2距离最近的k个点。想法来源于放松条件期望的定义,计算目标点附...
Structured Regression Functions 由于是使用f来拟合 E(Y|X)=F(X_1,X_2,...,X_p) 所以一种很直接的想法是使用ANOVA中主效应、交互效应加和的分解方法: f(X_1,X_2,\ldots,X_p)=\alpha+\sum_jg_j(X_j)+\sum_{k<\ell}g_{k\ell}(X_k,X_\ell)+\cdots 当然其中高阶的交互项并不会纳...
A distribution-free imputation procedure based on nonparametric kernel regression is proposed to estimate the distribution function and quantiles of a random variable that is incompletely observed. Assuming the baseline missing-at-random model for nonrespondence, we discuss consistent estimation via estimatin...
This paper considers estimation of the regression function and its derivatives in nonparametric regression with fractional time series errors. We focus on investigating the properties of a kernel dependent function V (δ) in the asymptotic variance and finding closed form formula of it, where δ is...