PI: The plug-in bandwidth selection for weighted data

In elghouch/cenROC: Estimation of the Time-Dependent ROC Curve and AUC for Censored Survival Data

Description

This function computes the data-driven bandwidth for smoothing the ROC (or distribution) function using the PI method of Beyene and El Ghouch (2019). This is an extension of the classical (unweighted) direct plug-in bandwith selection method to the case of weighted data.

Usage

PI(X, wt, ktype = "normal")

Arguments

X	The numeric vector of random variable.
wt	The non-negative weight vector.
ktype	A character string giving the type kernel to be used: "normal", "epanechnikov", "biweight", or "triweight". By default, the "normal" kernel is used.

Details

See Beyene and El Ghouch (2019) for details.

Value

Returns the computed value for the bandwith parameter.

Author(s)

Kassu Mehari Beyene, Catholic University of Louvain. <[e-mail]>

Anouar El Ghouch, Catholic University of Louvain. <[e-mail]>

References

Beyene, K. M. and El Ghouch A. (2019). Smoothed time-dependent ROC curves for right-censored survival data. <https://dial.uclouvain.be/pr/boreal/object/boreal:219643>.

Examples

library(cenROC)

X <- rnorm(100) # random data vector
wt <- runif(100) # weight vector

## Plug-in bandwidth selection
PI(X = X, wt = wt)$bw

elghouch/cenROC documentation built on April 2, 2020, 5:03 a.m.