Cubic smoothing spline (${\hat{g}}_{\lambda }=N(I+\lambda \Gamma {)}^{-1}\mathbf{Y}$)

For $\lambda >0$ and at least two distinct design points, the unique $C^2[a,b]$ minimizer of squared residuals plus $\lambda J(g)$ is $N(I+\lambda \Gamma {)}^{-1}\mathbf{Y}$. The minimum roughness property of the natural cubic spline interpolant reduces the optimization to the value vector. The matrix $I+\lambda \Gamma$ is a positive-definite matrix, so the normal equations have a unique solution; the interpolation equality case gives uniqueness as a function. Linear tails are part of the natural boundary convention.

For distinct ordered observations $x_i$, let $s_u$ be their natural cubic spline interpolant with values $u_i$. Its roughness is a quadratic form, $\int (s_u^{\prime \prime }{)}^2=u^{T}Ku$, where $K$ is positive semidefinite and its nullspace consists of vectors sampled from affine functions. Minimizing penalized least squares $\Vert y-u{\Vert }^2+\lambda u^{T}Ku$ gives $\hat{u}=(I+\lambda K{)}^{-1}y$. Thus the smoothing matrix is $A_{\lambda }=(I+\lambda K{)}^{-1}$ and its effective degrees of freedom are $\mathrm{tr}(A_{\lambda })$. As $\lambda \downarrow 0$ the fit approaches interpolation and the trace approaches $n$; as $\lambda \to \infty$ it approaches the least-squares affine line and the trace approaches $2$.