Asymptotic optimality of generalized cross-validation for choosing the regularization parameter

1993 ◽  
Vol 66 (1) ◽  
pp. 41-66 ◽  
Author(s):  
Mark A. Lukas
Keyword(s):  
Cross Validation ◽  
Regularization Parameter ◽  
Asymptotic Optimality ◽  
Generalized Cross Validation

On generalized cross validation for stable parameter selection in disease models

2015 ◽  
Vol 23 (5) ◽  
Author(s):  
Alexandra Smirnova ◽  
Maia Martcheva ◽  
Hui Liu
Keyword(s):  
Inverse Problem ◽  
Operator Equation ◽  
Recovery Rate ◽  
Cross Validation ◽  
Regularization Parameter ◽  
Parameter Selection ◽  
Time Dependent ◽  
Disease Models ◽  
Generalized Cross Validation ◽  
Irregular Operator

AbstractIn this paper we study advantages and limitations of the Generalized Cross Validation (GCV) approach for selecting a regularization parameter in the case of a partially stochastic linear irregular operator equation. The research has been motivated by an inverse problem in epidemiology, where the goal was to reconstruct a time dependent treatment recovery rate for

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