cvxreg: Estimation of an increasing and convex function

In Sshaped: Nonparametric, Tuning-Free Estimation of S-Shaped Functions

Estimation of an increasing and convex function

Description

This function computes the least squares increasing and convex regression estimator by a sequential mixed primal-dual bases algorithm.

Usage

cvxreg(x,y)

Arguments

x	a numeric vector that contains all the design points. NB. for the current version, we require all values to be distinct (but not necessarily in ascending order).
y	a numeric vector that contains the values of the response with respect to the design points.

Details

For more details, see Sshaped.

Value

An object of class sshaped, which contains the following fields:

x	covariates copied from input
y	response copied from input
fitted	the fitted values of the regression function with respect to the design points.
rss	the value of the minimised residual sum of squares of the fit
inflection	the location of the inflection point, which equals max(x) here
shape	the shape enforced in the fit, here equals "convex"

Author(s)

• Oliver Y. Feng

• Yining Chen

• Qiyang Han

• Raymond J. Carroll

• Richard J. Samworth

Examples

x<-seq(0,5,0.01)
y<-x^2 + rnorm(length(x))
output<-cvxreg(x,y)
plot(output)
xnew=rexp(5); predict(output,xnew)

Sshaped documentation built on Oct. 13, 2022, 5:05 p.m.