PRIM for estimating highest density regions (HDR) for high-dimensional
regression-type data.
Arguments
Details
The data are
$(\bold{X}_1, Y_1), \dots, (\bold{X}_n, Y_n)$ where $\bold{X}_i$ is d-dimensional and $Y_i$ is a
scalar response. We wish to find the modal (and/or anti-modal) regions
in the conditional
expectation $m(\bold{x}) = \bold{E} (Y | \bold{x}).$
These regions are also known as the highest density regions (HDR).
PRIM is a bump-hunting technique introduced by Friedman & Fisher
(1999), taken from data mining.
PRIM estimates are a sequence of nested hyper-rectangles (boxes). The
boxes which exceed a threshold comprise the HDR estimate.
For an overview of this package, see vignette("prim") for PRIM
estimation for 2- and 5-dimensional data.
References
Friedman, J.H. & Fisher, N.I. (1999) Bump-hunting for high
dimensional data, Statistics and Computing, 9, 123--143.
Hyndman, R.J. Computing and graphing highest density
regions. The American Statistician, 50, 120--126.