‎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 presents a non-parametric MAP framework for tracking non-rigid video objects. We formulate the region tracking problem as a maximum a posteriori probability (MAP) problem and define the probabilistic models in terms of the distances between the intensity distribution of the object and that of its spatialand temporal-neighborhood. Furthermore, in order to better model the complex inte...

In this paper, we developed the parametric estimation and the self-tuning control problem of the nonlinear systems which are described by discrete-time nonlinear mathematical models, with unknown, time-varying parameters, and operative in a stochastic environment. The parametric estimation is realized by using the prediction error method and the recursive least squares techniques. The self-tuni...

The new health and usage monitoring systems will out perform the current ones in term of accuracy and computing time. The data therefore need to be better processed. Data in frequency domain, or spectra, are widely used thank for the richness of information. To estimate the spectrum such methods as non parametric and parametric ones may be used. This paper discusses about parametric signal spec...

We consider the classical problem of missing-mass estimation, which deals with estimating total probability unseen elements in a sample. The estimation has various applications machine learning, statistics, language processing, ecology, sensor networks, and others. naive, constrained maximum likelihood (CML) estimator is inappropriate for this since it tends to overestimate observed elements. S...