Given: \(w_i = \frac{1}{i}\) is a weight-vector that scales with the dimensionality.
Simulates n points from \(Quadratic(X, Y) \in \mathbf{R}^d \times \mathbf{R}\) where:
$$X \sim {U}(a, b)^d$$,
$$Y = (w^TX)^2 + \kappa\epsilon N(0, 1)$$
and \(\kappa = 1\textrm{ if }d = 1, \textrm{ and 0 otherwise}\) controls the noise for higher dimensions.
For more details see the help vignette:
vignette("sims", package = "mgc")