A natural technique to select variables in the context of generalized linear models is to use a stepŵise procedure. It is natural, but contreversial, as discussed by Frank Harrell in a great post, clearly worth reading. Frank mentioned about 10 points against a stepwise procedure. It yields R-squared values that are badly biased to be high. The F and chi-squared tests quoted next to each variable on the printout do not have the claimed distribution. The method yields confidence intervals fo..
Variable selection is an important means to construct a model that predicts a target/responsible var...
A model is usually only an approximation of underlying reality. To access this reality in an adequat...
Longitudinal models are commonly used for studying data collected on individuals repeatedly through...
Stepwise methods for variable selection are frequently used to determine the predictors of an outcom...
This article gives a robust technique for model selection in regression models, an important aspect ...
that leave-one-out cross-validation is not subject to the “no-free-lunch ” criticism. Despite this o...
Cross-validation (CV) methods are popular for selecting the tuning parameter in the high-dimensional...
In this paper, we compare the method of Gunter et al. (2011) for variable selection in treatment com...
In this paper, we compare the method of Gunter et al. (2011) for variable selection in treatment com...
This paper investigates two types of results that support the use of Generalized Cross Validation (G...
We consider the problem of model (or variable) selection in the classical regression model based on ...
Longitudinal models are commonly used for studying data collected on individuals repeatedly through ...
Variable selection criteria in a linear regression model are analyzed, considering consistency and e...
In applied statistical studies, it is common to collect data on a large pool of candidate variables ...
In this paper, we extend to generalized linear models the robust model selection methodology of Müll...
Variable selection is an important means to construct a model that predicts a target/responsible var...
A model is usually only an approximation of underlying reality. To access this reality in an adequat...
Longitudinal models are commonly used for studying data collected on individuals repeatedly through...
Stepwise methods for variable selection are frequently used to determine the predictors of an outcom...
This article gives a robust technique for model selection in regression models, an important aspect ...
that leave-one-out cross-validation is not subject to the “no-free-lunch ” criticism. Despite this o...
Cross-validation (CV) methods are popular for selecting the tuning parameter in the high-dimensional...
In this paper, we compare the method of Gunter et al. (2011) for variable selection in treatment com...
In this paper, we compare the method of Gunter et al. (2011) for variable selection in treatment com...
This paper investigates two types of results that support the use of Generalized Cross Validation (G...
We consider the problem of model (or variable) selection in the classical regression model based on ...
Longitudinal models are commonly used for studying data collected on individuals repeatedly through ...
Variable selection criteria in a linear regression model are analyzed, considering consistency and e...
In applied statistical studies, it is common to collect data on a large pool of candidate variables ...
In this paper, we extend to generalized linear models the robust model selection methodology of Müll...
Variable selection is an important means to construct a model that predicts a target/responsible var...
A model is usually only an approximation of underlying reality. To access this reality in an adequat...
Longitudinal models are commonly used for studying data collected on individuals repeatedly through...