Nearest neighborhoods for kernel smoothing
Nearest neighborhoods for the values of a continuous predictor. The result is used for the conditional Kaplan-Meier estimator and other conditional product limit estimators.
neighborhood(x, bandwidth = NULL, kernel = "box")
x |
Numeric vector – typically the observations of a continuous random variate. |
bandwidth |
Controls the distance between neighbors in a neighborhood.
It can be a decimal, i.e.\ the bandwidth, or the string ‘"smooth"’, in which
case |
kernel |
Only the rectangular kernel ("box") is implemented. |
An object of class 'neighborhood'. The value is a list that
includes the unique values of ‘x’ (values) for which a neighborhood,
consisting of the nearest neighbors, is defined by the first neighbor
(first.nbh) of the usually very long vector neighbors and the
size of the neighborhood (size.nbh).
Further values are the arguments bandwidth, kernel, the total
sample size n and the number of unique values nu.
Thomas Gerds
Stute, W. "Asymptotic Normality of Nearest Neighbor Regression Function Estimates", The Annals of Statistics, 1984,12,917–926.
d <- SimSurv(20) neighborhood(d$X2)
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