kernel_density

Functions to perform the estimation of probability density function (pdf) by kernel estimators (with a non-gaussian kernel).

Implements techniques to estimate the unknown quantities related
to two-component admixture models, where the two components can belong to any
distribution (note that in the case of multinomial mixtures, the two components
must belong to the same family). Estimation methods depend on the assumptions
made on the unknown component density (see Bordes and Vandekerkhove (2010) <doi:10.3103/S1066530710010023>;
Patra and Sen (2016) <doi:10.1111/rssb.12148>); Milhaud, Pommeret, Salhi and Vandekerkhove
(2022) <doi:10.1016/j.jspi.2021.05.010>). In practice, one can estimate both the
mixture weight and the unknown component density in a wide variety of frameworks.
On top of that, hypothesis tests can be performed in one and two-sample contexts
to test the unknown component density (see Milhaud, Pommeret, Salhi, Vandekerkhove (2023)).
Finally, clustering of unknown mixture components is also feasible in a K-samples setting.

Xavier Milhaud

admix

Package Admix for Admixture (aka Contamination) Models

Pierre Vandekerkhove

Denys Pommeret

Yahia Salhi

kernel_density function

<dl><dt>u</dt>
<dd>the point at which the estimation is made.</dd>
<dt>h</dt>
<dd>window of the kernel estimation.</dd></dl>

Arguments

Xavier Milhaud <a href="/link/xavier.milhaud.research%40gmail.com?package=admix&version=2.1-3" data-mini-rdoc="admix::xavier.milhaud.research@gmail.com">xavier.milhaud.research@gmail.com</a>

Author

Kernel estimation — kernel_density

<dl>

<dt>u</dt>
<dd>the point at which the estimation is made.</dd>


<dt>h</dt>
<dd>window of the kernel estimation.</dd>

</dl>

Xavier Milhaud <a href='mailto:xavier.milhaud.research@gmail.com'>xavier.milhaud.research@gmail.com</a>

kernel_density: Kernel estimation

Description

Usage

Value

Arguments

Author

Examples