Penalized Likelihood for General Semi-Parametric Regression Models.
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WISCONSIN UNIV-MADISON MATHEMATICS RESEARCH CENTER
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This paper examines maximum 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 formulation is adopted, using a suitable 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. Additional keywords algorithms smoothing goodness of fit tests nonlinear repression. Author
- Statistics and Probability