hsgauss_kdens: Gaussian kernel density estimator on half-space

In mig: Multivariate Inverse Gaussian Distribution

Gaussian kernel density estimator on half-space

Description

Given a data matrix over a half-space defined by beta, compute an homeomorphism to \mathbb{R}^d and perform kernel smoothing based on a Gaussian kernel density estimator, taking each turn an observation as location vector.

Usage

hsgauss_kdens(x, newdata, Sigma, beta, log = TRUE, ...)

Arguments

x	n by d matrix of quantiles
newdata	matrix of new observations at which to evaluated the kernel density
Sigma	scale matrix
beta	d vector \boldsymbol{\beta} defining the half-space through \boldsymbol{\beta}^{\top}\boldsymbol{\xi}>0
log	logical; if TRUE, returns log probabilities
...	additional arguments, currently ignored

Value

a vector containing the value of the kernel density at each of the newdata points

mig documentation built on April 11, 2025, 5:45 p.m.