Structured Secant Methods to Select Smoothing Parameters for General Smooth Models
一般平滑模型中选择平滑参数的结构化割线方法
General smooth models replace parameters of a regular likelihood with additive models. The models can include parametric terms, Gaussian random effects, and smooth functions of covariates. The latter are parameterized via a reduced-rank spline basis and regularized via weighted quadratic penalties placed on the basis coefficients. Estimates for these weights (i.e., smoothing parameters) can be obtained by optimizing the Laplace-approximate Bayesian marginal likelihood.…