الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
.Density estimation performs a vital role in statistics, where it has been applied in many fields, including hydrology, archaeology, banking, climatology, economics, genetics and physiology. Therefore, there is a need to construct an estimate of the unknown probability density function (pdf) from the observed data of a quantitative variable of interest. In this regard, there are two main types of estimation: parametric and nonparametric estimation, besides the semi parametric one. Parameter estimation is a branch of statistics that involves using sample data to estimate the parameters of a distribution. In parametric estimation,the distribution of the variable of interest is supposed to belong to a knownfamily of distributions, such as the normal, gamma and other distributionsinvolving some unknown parameters. The observed data are thus used toestimate the unknown parameters of the distribution through well-establishedmethods, such as moments, maximum likelihood estimator (MLE), etc.