Add to ORKGFor the problem of model selection, full cross-validation has been proposed as an alternative criterion to the traditional cross-validation, particularly in cases where the latter is not well defined. To justify the use of the new proposal we show that under some conditions, both criteria share the same asymptotic optimality property when selecting among linear regression models.Cross-validation Full cross-validation Model selection Prediction Asymptotic optimality
Several model selection criteria which generally can be classied as the penalized robust method are ...
Cross-validation (CV) methods are popular for selecting the tuning parameter in the high-dimensional...
AbstractIn this paper we propose a cross-validation selection criterion to determine asymptotically ...
For the problem of model selection, full cross-validation has been proposed as alternative criterion...
Summary For the problem of model selection full crossvalidation has been proposed as alternative c...
We consider the problem of model (or variable) selection in the classical regression model based on ...
AbstractIn this paper we propose a cross-validation selection criterion to determine asymptotically ...
For the problem of model selection, full cross-validation has been proposed as alternative criterion...
This article gives a robust technique for model selection in regression models, an important aspect ...
In this paper we propose a cross-validation selection criterion to determine asymptotically the corr...
For the problem of model selection, full cross-validation has been proposed as alternative criterion...
The problem considered here is that of using a data-driven procedure to select a good estimate from ...
that leave-one-out cross-validation is not subject to the “no-free-lunch ” criticism. Despite this o...
Model selection in nonparametric and semiparametric regression is of both theoretical and practical ...
In this paper, the cross-validation methods namely the $$C_{p}$$Cp, PRESS and GCV are presented unde...
Several model selection criteria which generally can be classied as the penalized robust method are ...
Cross-validation (CV) methods are popular for selecting the tuning parameter in the high-dimensional...
AbstractIn this paper we propose a cross-validation selection criterion to determine asymptotically ...
For the problem of model selection, full cross-validation has been proposed as alternative criterion...
Summary For the problem of model selection full crossvalidation has been proposed as alternative c...
We consider the problem of model (or variable) selection in the classical regression model based on ...
AbstractIn this paper we propose a cross-validation selection criterion to determine asymptotically ...
For the problem of model selection, full cross-validation has been proposed as alternative criterion...
This article gives a robust technique for model selection in regression models, an important aspect ...
In this paper we propose a cross-validation selection criterion to determine asymptotically the corr...
For the problem of model selection, full cross-validation has been proposed as alternative criterion...
The problem considered here is that of using a data-driven procedure to select a good estimate from ...
that leave-one-out cross-validation is not subject to the “no-free-lunch ” criticism. Despite this o...
Model selection in nonparametric and semiparametric regression is of both theoretical and practical ...
In this paper, the cross-validation methods namely the $$C_{p}$$Cp, PRESS and GCV are presented unde...
Several model selection criteria which generally can be classied as the penalized robust method are ...
Cross-validation (CV) methods are popular for selecting the tuning parameter in the high-dimensional...
AbstractIn this paper we propose a cross-validation selection criterion to determine asymptotically ...