MonoMat

numeric vector of locations where monotonicity constraint is to be enforced

xgrid

local polynomial degree, can be either 0 or 1

This computes a matrix of coefficients of 
the first derivatives of monotonic local linear sharpening problem.

models

Functions and data sets inspired by data sharpening -
data perturbation to achieve improved performance in
nonparametric estimation, as described in Choi, E., Hall, P.
and Rousson, V. (2000).
Capabilities for enhanced local linear regression function
and derivative estimation are included, as well as an
asymptotically correct iterated data sharpening estimator
for any degree of local polynomial regression estimation.
A cross-validation-based bandwidth selector is included which,
in concert with the iterated sharpener, will often provide
superior performance, according to a median integrated squared
error criterion.  Sample data sets are provided to illustrate
function usage.

MonoMat: Matrix of derivative coefficients for local polynomial estimates

Description

Usage

Arguments

Value