hazard.MBC {DOvalidation} | R Documentation |
Multiplicative Bias Corrected Hazard Estimator
Description
Multiplicatively bias corrected local linear estimator of the unidimensional hazard with natural weighting introduced by Nielsen and Tanggaard (2001).
Usage
hazard.MBC(xi, Oi, Ei, x, b, K="sextic", Ktype="symmetric")
Arguments
xi |
Vector of time points where the counts data are given. |
Oi |
Vector with the number of occurrences observed at each time point ( |
Ei |
Vector with the observed exposure at each time point ( |
x |
Vector (or scalar) with the (time) grid points where the hazard estimator will be evaluated. |
b |
A positive scalar used as the bandwidth. |
K |
Indicates the kernel function to be considered in the estimator. Choose between values |
Ktype |
Indicates the type of kernel to be used. Choose among |
Details
The estimator is calculated assuming that the data are given as count data i.e. number of occurrences and exposures.
The function allows to consider two different kernels using the argument K
. These are: Epanechnikov, K(u)=.75*(1-u^2)*(abs(u)<1), and sextic K(u)=(3003/2048)*(1-(u)^2)^6)*(abs(u)<1). The argument Ktype
will define the usual estimator with whole support kernel as it is defined by K
or the one-sided versions using left-sided kernel, 2*K(u)*(u<0), or right-sided kernel 2*K(u)*(u>0). See more details in Gamiz et al. (2017).
Value
x |
Vector (or scalar) with the (time) grid points where the hazard estimator has been evaluated. |
hMBC |
Vector (or scalar) with the resulting hazard estimates at grid points |
Author(s)
Gamiz, M.L., Martinez-Miranda, M.D. and Nielsen, J.P.
References
Gamiz, M.L., Martinez-Miranda, M.D. and Nielsen, J.P. (2017). Multiplicative local linear hazard estimation and best one-sided cross-validation. Available at http://arxiv.org/abs/1710.05575
Nielsen, J.P. and Tanggaard, C. (2001). Boundary and bias correction in kernel hazard estimation. Scandinavian Journal of Statistics,28, 675-698.
See Also
Examples
data(Iceland)
Oi<-Iceland$D
Ei<-Iceland$E
ti<-40:110 # time is age and it goes from 40 to 110 years
res<-hazard.MBC(xi=ti,Oi=Oi,Ei=Ei,x=ti,b=48.7)
plot(ti,res$hMBC,main='Hazard estimate',xlab='age',ylab='',type='l',lwd=2)