Strong uniform consistency and the weak convergence of the normalized process are also proved.doi:10.1007/BF02613509J.K. GhoraiV. SusarlaPhysica-VerlagMetrikaGhorai, JK, Susarla, V (1990) Kernel estimation of a smooth distribution function based on censored data. Metrika 37: pp. 71-86...
Jin, Z. and Shao, Y. (1999), "On kernel estimation of a multivariate distribution function," Statist. Probab. Lett., 41, 163-168.On kernel estimation of a multivariate distribution function - Jin, Shao - 1999 () Citation Context ...]. Its properties in the context of the missing ...
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...
Kernel probability distribution object expand all in page Description A KernelDistribution object consists of parameters, a model description, and sample data for a nonparametric kernel-smoothing distribution. The kernel distribution is a nonparametric estimation of the probability density function (pdf) of...
How to Generate random sampling from a... Learn more about ksdensity, monte carlo, kernel density estimation, probability density function, sampling
Estimation of Probability Densities Stochastic Processes Book2004, Stochastic Processes Explore book 2.6.4. Kernel Estimators Kernel estimators were first proposed by Nadaraya [56] and Watson [82]. The methods related to the estimation of densities are closely related to this estimator. Nadaraya and...
We study estimation of a finite population cumulative distribution function when sample units are measured with error. We propose a simple, bias-adjusted e... L. A. Stefanski and J. M. Bay - 《Biometrika》 被引量: 92发表: 1996年 Simulation extrapolation deconvolution of finite population cumu...
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...
kdensity — Univariate kernel density estimation 3 Options £ £ Main kernel(kernel) specifies the kernel function for use in calculating the kernel density estimate. The default kernel is the Epanechnikov kernel (epanechnikov). bwidth(#) specifies the half-width of the kernel, the ...
2.2.1.2 Kernel density estimation There have been various attempts to generalize the kernel home range estimator to incorporate the time dependence between the observed locations. Nevertheless, there are two important issues that should be remarked: first, the definition of the kernel density estimator...