loglike.KHall: Evaluate a log likelihood using the Hall Kernel

In naveedmerchant/BayesScreening: Classical and Bayesian Screening for Binary Classification

Evaluate a log likelihood using the Hall Kernel

Description

Evaluate a log likelihood using the Hall Kernel

Usage

loglike.KHall(h, y, x)

Arguments

h	A bandwidth or vector of bandwidths tot ry out
y	A validation set to evaluate the KDE on
x	A training set to build the Hall kernel density estimate on

Value

-log likelihood evaluation, where the likelihood is constructed using the training data

Examples

dataset1 = rnorm(100)
DT = dataset1[1:50]
DV = dataset1[51:100]
loglike.Khall(.01, DT, DV)

naveedmerchant/BayesScreening documentation built on June 13, 2024, 7:56 a.m.