SpectralShift

Matrix <code>L</code> is assumed to have eigenvalues that are either all positive, or very
close to zero. If any eigenvalues are less than less than <code>threshold</code>, a positive
quantity is added to the diagonal.

internal

Implements methods for obtaining kernel density estimates
subject to a variety of shape constraints (unimodality, bimodality,
symmetry, tail monotonicity, bounds, and constraints on the number of
inflection points). Enforcing constraints can eliminate unwanted waves or
kinks in the estimate, which improves its subjective appearance and can
also improve statistical performance. The main function scdensity() is
very similar to the density() function in 'stats', allowing
shape-restricted estimates to be obtained with little effort. The
methods implemented in this package are described in Wolters and Braun
(2017) <doi:10.1080/03610918.2017.1288247>, Wolters (2012)
<doi:10.18637/jss.v047.i06>, and Hall and Huang (2002)
<https://www3.stat.sinica.edu.tw/statistica/j12n4/j12n41/j12n41.htm>.
See the scdensity() help for for full citations.

Mark A Wolters

scdensity

Shape-Constrained Kernel Density Estimation

SpectralShift function

<dl><dt>L</dt>
<dd>A square numeric matrix.</dd>
<dt>threshold</dt>
<dd>The eigenvalue threshold. Default 1E-10.</dd></dl>

Arguments

Performs the spectral shift on a matrix to make it numerically positive definite. — SpectralShift

<dl>

<dt>L</dt>
<dd>A square numeric matrix.</dd>


<dt>threshold</dt>
<dd>The eigenvalue threshold. Default 1E-10.</dd>

</dl>

SpectralShift: Performs the spectral shift on a matrix to make it numerically positive definite.

Description

Usage

Value

Arguments