origami: A Generalized Framework for Cross-Validation in R

a framework for identifying and prioritizing factors affecting customers’ online shopping behavior in iran

the purpose of this study is identifying effective factors which make customers shop online in iran and investigating the importance of discovered factors in online customers’ decision. in the identifying phase, to discover the factors affecting online shopping behavior of customers in iran, the derived reference model summarizing antecedents of online shopping proposed by change et al. was us...

Generalized cross validation for wavelet thresholding

Noisy data are often fitted using a smoothing parameter, controling the importance of two objectives that are opposite to a certain extent. One of these two is smoothness and the other is closeness to the input data. The optimal value of this paramater minimizes the error of the result (as compared to the unknown, exact data), usually expressed in the L2 norm. This optimum cannot be found exact...

Generalized Cross-Validation for Correlated Data (GCVc)

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...

The generalized cross validation filter

Generalized cross validation (GCV) is one of the most important approaches used to estimate parameters in the context of inverse problems and regularization techniques. A notable example is the determination of the smoothness parameter in splines. When the data are generated by a state space model, like in the spline case, efficient algorithms are available to evaluate the GCV score with comple...

Generalized Correlated Cross-Validation (GCCV)