add_kernel

Add a new kernel to <code>kernels_environment</code>.

internal

Handles univariate non-parametric density estimation with
parametric starts and asymmetric kernels in a simple and flexible way.
Kernel density estimation with parametric starts involves fitting a
parametric density to the data before making a correction with kernel
density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>.
Asymmetric kernels make kernel density estimation more efficient on bounded
intervals such as (0, 1) and the positive half-line. Supported asymmetric
kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>,
the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the
copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>.
User-supplied kernels, parametric starts, and bandwidths are supported.

Jonas Moss

kdensity

Kernel Density Estimation with Parametric Starts and Asymmetric
Kernels

Martin Tveten

add_kernel function

<dl><dt>kernel_str</dt>
<dd>A string giving the name of the density.</dd>
<dt>kernel</dt>
<dd>The kernel function.</dd></dl>

Arguments

Add a new kernel to kernels_environment. — add_kernel

<dl>

<dt>kernel_str</dt>
<dd>A string giving the name of the density.</dd>


<dt>kernel</dt>
<dd>The kernel function.</dd>

</dl>

add_kernel: Add a new kernel to `kernels_environment`.

Description

Usage

Value

Arguments