This paper examines penalized likelihood estimation in the context of general regression problems, characterized as probability models with composite likelihood functions. The emphasis is on the common situation where a parametric model is considered satisfactory but for inhomogeneity with respect to a few extra variables. A finite-dimensional set of basis functions. Appropriate definitions of deviance, degrees of freedom, and residual are provided, and the method of cross-validation for choice of the tuning constant is discussed. Quadratic approximations are derived for all the required statistics.
CITATION STYLE
Carmona, R. (2014). Parametric Regression (pp. 199–276). https://doi.org/10.1007/978-1-4614-8788-3_4
Mendeley helps you to discover research relevant for your work.