HRR_sbsp_est: Subsampling Hazard Rate Ratio Point Estimations
In zhicongz/AnomDetct: Anomaly Detection via Scan Statistics

Description Usage Arguments Details Value Author(s) See Also Examples

This function returns point Estimations of probability density function and hazard rate ratio function.

1 2	HRR_sbsp_est(pt_int,cdf,kernel = "gaussian",hazard_bandwidth = NA, knn = NA, n_hz_sample, n_hz_size = length(cdf))

`pt_int`	a vector of estimated points.
`cdf`	a sorted vector that needs to be estimated.
`kernel`	a character string giving the smoothing kernel to be used. This must partially match one of "`gaussian`", "`rectangular`", "`triangular`" or "`knn`", with default "`gaussian`".
`hazard_bandwidth`	the smoothing bandwidth to be used.
`knn`	number of neighbor points to be considered in smoothing for the "`knn`" kernel.
`n_hz_sample`	number of replicates.
`n_hz_size`	resampled size. Default to be same as `length(cdf)`.

A smaller n_hz_size can significantly cut down the running time while the accuracy is maintained by large n_hz_sample. It is similar as the "bagging" method in machine learning.

This function returns a list with components:

`fhat`	A function performing the linear interpolation of smoothed probability density function of given data points.
`HRR`	A function performing the linear interpolation of smoothed hazard rate ratio point estimations.

Zhicong Zhao

HRR_pt_est for each replicate.

temp <- HRR_sbsp_est(pt_int = seq(0,1,0.1),
                     cdf = sort(rbeta(5000,2,5)),
                     kernel = "triangular",
                     hazard_bandwidth = 0.1,
                     n_hz_sample = 30)
## plot ##
plot(temp$fhat,col = "blue",xlab = NA,ylab = NA)
points(seq(0,1,0.1),dbeta(seq(0,1,0.1),2,5),type = "l",col = "red")
legend("top",legend = c("estimated density","population density"),
       lty = 1, col = c("blue","red"))