This morning during the lecture on nonlinear regression, I mentioned (very) briefly the case of convex regression. Since I forgot to mention the codes in R, I will publish them here. Assume that \(y_i=m(\mathbf{x}_i)+\varepsilon_i\) where \(m:\mathbb{R}^d\rightarrow \mathbb{R}\) is some convex function.

Then \(m\) is convex if and only if \(\forall\mathbf{x}_1,\mathbf{x}_2\in\mathbb{R}^d\), \(\forall t\in[0,1]\), \(m(t\mathbf{x}_1+[1-t]\mathbf{x}_2) \leq tm(\mathbf{x}_1)+[1-t]m(\mathbf{x}_2)\)Hidreth (1954) proved that if\( m^\star=\underset{m \text{ convex}}{\text{argmin}}\left\lbrace\sum_{i=1}^n \big(y_i-m(\mathbf{x_i})\big)^2\right\rbrace\)then \(\mathbf{\theta}^\star=(m^\star(\mathbf{x_1}),\cdots,m^\star(\mathbf{x_n}))\) is unique.

Let \(\mathbf{y}=\mathbf{\theta}+\mathbf{\varepsilon}\), then \(\mathbf{\theta}^\star=\underset{\mathbf{\theta}\in \mathcal{K}}{\text{argmin}}\left\lbrace\sum_{i=1}^n \big(y_i-\theta_i)\big)^2\right\rbrace\)where\(\mathcal{K}=\{\mathbf{\theta}\in\mathbb{R}^n:\exists m\text{ convex },m(\mathbf{x}_i)=\theta_i\}\). I.e. \(\mathbf{\theta}^\star\) is the projection of \(\mathbf{y}\) onto the (closed) convex cone \(\mathcal{K}\). The projection theorem gives existence and unicity.

For convenience, in the application, we will consider the real-valued case, \(m:\mathbb{R}\rightarrow \mathbb{R}\), i.e. \(y_i=m(x_i)+\varepsilon_i\). Assume that observations are ordered \(x_1\leq x_2\leq\cdots \leq x_n\). Here \(\mathcal{K}=\left\lbrace\mathbf{\theta}\in\mathbb{R}^n:\frac{\theta_2-\theta_1}{x_2-x_1}\leq \frac{\theta_3-\theta_2}{x_3-x_2}\leq \cdots \leq \frac{\theta_n-\theta_{n-1}}{x_n-x_{n-1}}\right\rbrace\)

Hence, quadratic program with \(n-2\) linear constraints.

\(m^\star\) is a piecewise linear function (interpolation of consecutive pairs \((x_i,\theta_i^\star)\)).

If \(m\) is differentiable, \(m\) is convex if \(m(\mathbf{x})+ \nabla m(\mathbf{x})^{\text{T}}\cdot[\mathbf{y}-\mathbf{x}] \leq m(\mathbf{y})\)

More generally, if \(m\) is convex, then there exists \(\xi_{\mathbf{x}}\in\mathbb{R}^n\) such that \(m(\mathbf{x})+ \xi_{\mathbf{x}}^{\text{ T}}\cdot[\mathbf{y}-\mathbf{x}] \leq m(\mathbf{y})\)
\(\xi_{\mathbf{x}}\) is a subgradient of \(m\) at \({\mathbf{x}}\). And then \(\partial m(\mathbf{x})=\big\lbrace m(\mathbf{x})+ \xi^{\text{ T}}\cdot[\mathbf{y}-\mathbf{x}] \leq m(\mathbf{y}),\forall \mathbf{y}\in\mathbb{R}^n\big\rbrace\)

Hence, \(\mathbf{\theta}^\star\) is solution of \(\text{argmin}\big\lbrace\|\mathbf{y}-\mathbf{\theta}\|^2\big\rbrace\)\(\text{subject to }\theta_i+\xi_i^{\text{ T}}[\mathbf{x}_j-\mathbf{x}_i]\leq\mathbf{\theta}_j,~\forall i,j\) and \(\xi_1,\cdots,\xi_n\in\mathbb{R}^n\). Now, to do it for real, use cobs package for constrained (b)splines regression,

library(cobs)

To get a convex regression, use

plot(cars)
x = cars$speed
y = cars$dist
rc = conreg(x,y,convex=TRUE)
lines(rc, col = 2)

Here we can get the values of the knots

rc

Call:  conreg(x = x, y = y, convex = TRUE) 
Convex regression: From 19 separated x-values, using 5 inner knots,
     7,    8,    9,   20,   23.
RSS =  1356; R^2 = 0.8766;
 needed (5,0) iterations

and actually, if we use them in a linear-spline regression, we get the same output here

reg = lm(dist~bs(speed,degree=1,knots=c(4,7,8,9,,20,23,25)),data=cars)
u = seq(4,25,by=.1)
v = predict(reg,newdata=data.frame(speed=u))
lines(u,v,col="green")

Let us add vertical lines for the knots

abline(v=c(4,7,8,9,20,23,25),col="grey",lty=2)