Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/40647
Título: Explaining the generalized cross-validation on linear models
Palavras-chave: Circulant Matrices
PRESS Statistics
Prediction Error
Validação cruzada
Modelos lineares
Matrizes Circulantes
Estatística PRESS
Erro de previsão
Data do documento: 2019
Editor: Science Publications
Citação: CHAVES, L. M. et al. Explaining the generalized cross-validation on linear models. Journal of Mathematics and Statistics, Dubai, v. 15, p. 298-307, 2019.
Resumo: Cross-Validation is a model validation method widely used by the scientific community. The Generalized Cross-Validation (GCV) is an invariant version of the usual Cross-Validation method. This generalization was obtained using the non usual theory of circulant complex matrices. In this work we intend to give a clear and complete exposition concerning the linear algebra assumptions required by the theory. The aim was to make this text accessible to a wide audience of statisticians and non-statisticians who use the Cross-Validation method in their research activities. It is also intended to supply the absence of a basic reference on this topic in the literature.
URI: http://repositorio.ufla.br/jspui/handle/1/40647
Aparece nas coleções:DEX - Artigos publicados em periódicos

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