Kernel density estimation (KDE) is a technique that can help you estimate the probability density function (PDF) of a random variable in machine learning (ML). PDFs are useful for describing the ...

Parametric maximum likelihood (ML) estimators of probability density functions (pdfs) are widely used today because they are efficient to compute and have several nice properties such as consistency, ...

In this article, our proposed kernel estimator, named as Gumbel kernel, which broadened the class of non-negative, asymmetric kernel density estimators. Such kernel estimator can be used in ...

Recently for unknown multidimensional distribution density function the kernel estimation is constructed and similar problem is studied by Muminov [5,6]. Several authors have considered the rate of ...

Objective To understand how often ‘breakthroughs,’ that is, treatments that significantly improve health outcomes, can be developed. Design We applied weighted adaptive kernel density estimation to ...

Parametric maximum likelihood (ML) estimators of probability density functions (pdfs) are widely used today because they are efficient to compute and have several nice properties such as consistency, ...