entropy as a measure of uncertainty was used to represent the results of the wavelet technique in electrochemical noise analysis. the experimental signals were obtained by recording the electrochemical potential and current noise of 7075 aluminum alloy in 3.5% nacl solution. the electrochemical potential and current noise were decomposed into 16 levels using daubechies wavelets. wavelet output ...

‎In this article‎, ‎we develop two nonparametric smoothing estimators for parameter of a time-variant parametric model‎. ‎This parameter can be from any parametric family or from any parametric or semi-parametric regression model‎. ‎Estimation is based on a two-step procedure‎, ‎in which we first get the raw estimate of the parameter at a set of disjoint time...

This paper discusses modiications of the convolution type kernel regression estimator. One modiication uses kernel quantile estimators and is analyzed more detailed. This regression estimator combines advantages of local polynomial and kernel regression estimators and can be applied for small to large sample size. Its properties are illustrated by simulation results and asymptotic theory. Espec...

We propose a local linear estimator of a smooth distribution function. This estimator applies local linear techniques to observations from a regression model in which the value of the empirical distribution function equals the value of true distribution plus an error term. We show that, for most commonly used kernel functions, our local linear estimator has a smaller asymptotic mean integrated ...

The accuracy of the binned kernel density estimator is studied for general binning rules. We derive mean squared error results for the closeness of this estimator to both the true density and the unbinned kernel estimator. The binning rule and smoothness of the kernel function are shown to innuence the accuracy of the binned kernel estimators. Our results are used to compare commonly used binni...

We discuss a robust data sharpening method for rendering a standard kernel estimator, with a given bandwidth, unimodal. It has theoretical and numerical properties of the type that one would like such a technique to enjoy. In particular, we show theoretically that, with probability converging to 1 as sample size diverges, our technique alters the kernel estimator only in places where the latter...

The performance of a kernel HAC estimator depends on the accuracy of the estimation of the normalized curvature, an unknown quantity in the optimal bandwidth represented as the spectral density and its derivative. This paper proposes to estimate it with a general class of kernels. The AMSE of the kernel estimator and the AMSE-optimal bandwidth are derived. It is shown that the optimal bandwidth...