ComputeBW: Data-driven bandwidth vector

In curstatCI: Confidence Intervals for the Current Status Model

Description

The function ComputeBW computes the bandwidth that minimizes the pointwise Mean Squared Error using the subsampling principle in combination with undersmoothing.

Usage

ComputeBW(data, x)

Arguments

data

Dataframe with three variables: t Observation points t sorted in ascending order. All observations need to be positive. The total number of unique observation points equals length(t). freq1 Frequency of observation t satisfying x ≤ t. The total number of observations with censoring indicator δ =1 equals sum(freq1). freq2 Frequency of observation t. The sample size equals sum(freq2). If no tied observations are present in the data length(t) equals sum(freq2).

x

numeric vector containing the points where the confidence intervals are computed.

Value

bw data-driven bandwidth vector of size length(x) containing the bandwidth value for each point in x.

References

Groeneboom, P. and Hendrickx, K. (2017). The nonparametric bootstrap for the current status model. Electronic Journal of Statistics 11(2):3446-3848.

See Also

vignette("curstatCI")

Examples

library(Rcpp)
library(curstatCI)

# sample size
n <- 1000

# truncated exponential distribution on (0,2)
set.seed(100)
t <- rep(NA, n)
delta <- rep(NA, n)
for(i in (1:n) ){
  x<-runif(1)
  y<--log(1-(1-exp(-2))*x)
  t[i]<-2*runif(1);
  if(y<=t[i]){ delta[i]<-1}
  else{delta[i]<-0}}

A<-cbind(t[order(t)], delta[order(t)], rep(1,n))

# x vector
grid<-seq(0.1,1.9 ,by = 0.1)

# data-driven bandwidth vector
bw <- ComputeBW(data =A, x = grid)
plot(grid, bw)

curstatCI documentation built on May 2, 2019, 6:35 a.m.