Generalized Cross-Validation for Correlated Data (GCVc)
نویسندگان
چکیده
Since its introduction by Stone (1974) and Geisser (1975), cross-validation has been studied and improved by several authors including Burman et al. the most widely used and best known is generalized cross-validation (GCV) (Craven & Wahba, 1979), which establishes a single-pass method that penalizes the fit by the trace of the smoother matrix assuming independent errors. We propose an extension to GCV in the context of correlated errors that has important implications about the definition for residual degrees of freedom, even in the independent case. The efficacy of the new method is demonstrated by simulation and application with concluding remarks about the heteroscedastic case and a potential maximum likelihood framework.
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