# SDHazardRateEst: Kernel Second Derivative Hazard Rate Estimation In NPHazardRate: Nonparametric Hazard Rate Estimation

## Description

Implements the kernel estimate of the second derivative of the hazard rate for right censored data defined - based on the estimate of Tanner and Wong (1983). The implementation is based on the second derivative of the Biweight Kernel.

## Usage

 1 SDHazardRateEst(xin, xout, h, ci) 

## Arguments

 xin A vector of data points. Missing values not allowed. xout A vector of grid points at which the estimates will be calculated. h A scalar, the bandwidth to use in the estimate. ci A vector of censoring indicators: 1's indicate uncensored observations, 0's correspond to censored obs.

## Details

The function SDHazardRateEst implements the kernel estimate of the second derivative of the hazard rate estimator, given by

\hat λ_2(x;h) = ∑_{i=1}^n \frac{K_h''(x-X_{(i)})δ_{(i)}}{n-i+1}

where K is taken to be the Biweight kernel. The function is used for estimation of the functional R(λ'') in PlugInBand so a default bandwidth rule is used for h provided in (16), Hua, Patil and Bagkavos (2018).

## Value

A vector with the second derivative of the hazard rate at the designated points xout.

## References

NPHazardRate documentation built on May 2, 2019, 10:24 a.m.