mig_rlcv: Robust likelihood cross-validation for kernel density...

In mig: Multivariate Inverse Gaussian Distribution

Robust likelihood cross-validation for kernel density estimation for MIG

Description

Given a data matrix over a half-space defined by beta, compute the log density using leave-one-out cross validation, taking in turn an observation as location vector and computing the density of the resulting mixture.

Usage

mig_rlcv(x, beta, Omega, an, xsamp, dxsamp, mckern = TRUE)

Arguments

x	n by d matrix of quantiles
beta	d vector \boldsymbol{\beta} defining the half-space through \boldsymbol{\beta}^{\top}\boldsymbol{\xi}>0
Omega	d by d positive definite scale matrix \boldsymbol{\Omega}
xsamp	matrix of points at which to evaluate the integral
dxsamp	density of points

Value

the value of the likelihood cross-validation criterion

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