ON JACKKNIFE VARIANCE ESTIMATOR FOR KERNEL DENSITY ESTIMATOR AND ITS APPLICATION

Kernel Estimator and Bandwidth Selection for Density and its Derivatives

In statistics, the univariate kernel density estimation (KDE) is a non-parametric way to estimate the probability density function f(x) of a random variable X, is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. This techniques are widely used in various inference procedures such as signal processing, data mining and econometric...

Statistically and Computationally Efficient Variance Estimator for Kernel Ridge

Using Kernel Density Estimator in Nonlinear Mixture

Generally, blind separation of sources from their nonlinear mixtures is rather difficult. This nonlinear mapping, constituted by unsupervised linear mixing followed by unknown and invertible nonlinear distortion, is found in many signal processing cases. We propose using a kernel density estimator incorporated within an equivariant gradient algorithm to separate the nonlinear mixed sources. The...

An Empirical Bayesian Kernel Density Estimator

Often times there is a need to infer the true underlying probability based on the observations, such as in, including but not limited to, data-mining, optimizing the process control parameters etc., Histograms, very rudimentary empirical density estimators, divide the whole data range into either equal or unequal sub intervals (bins) and then obtain the frequency of occurrence of each bin. They...

Consistency of the kernel density estimator

Various consistency proofs for the kernel density estimator have been developed over the last few decades. Important milestones are the pointwise consistency and almost sure uniform convergence with a fixed bandwidth on the one hand and the rate of convergence with a fixed or even a variable bandwidth on the other hand. While considering global properties of the empirical distribution functions...