Add to ORKGGeneralized degrees of freedom measure the complexity of a modeling procedure; a modeling procedure is a combination of model selection and model fitting. In this manuscript, we consider two definitions of generalized degrees of freedom for a modeling procedure under the L1 loss function, and investigate the connections between those two definitions. We also propose the extended Akaike information criterion, the adaptive model selection, and the extended generalized cross-validation under the L1 loss function. Finally, we extend the results to M-estimation
2. 公表論文 (1) Consistent selection of working correlation structure in GEE analysis based on Stein&apo...
Degrees of freedom is a fundamental concept in statistical modeling, as it provides a quan-titative ...
Model selection is difficult to analyse yet theoretically and empirically important, especially for ...
The concept of degrees of freedom plays an important role in statistical model-ing and is commonly u...
Model selection is of fundamental importance to high dimensional modelling featured in many contempo...
Overparametrized interpolating models have drawn increasing attention from machine learning. Some re...
Penalized inference of Gaussian graphical models is a way to assess the conditional independence str...
Penalized inference of Gaussian graphical models is a way to assess the conditional independence str...
Penalized inference of Gaussian graphical models is a way to assess the conditional independence str...
A hierarchical Bayesian formulation in Generalized Linear Models (GLMs) is proposed in this disserta...
Penalized inference of Gaussian graphical models is a way to assess the conditional independence str...
Penalized inference of Gaussian graphical models is a way to assess the conditional independence str...
Model selection is difficult to analyse yet theoretically and empirically important, especially for ...
選擇模型來解釋資料的方法有很多種, 像是AIC (Akaike 1974), BIC (Schwarz 1978), 以及Mallows’ Cp. 當考慮線性 迴歸模型選取時, 可將上述的模型選取法則...
In general model selection so far considered in literature, the parameter estimation loss and the pr...
2. 公表論文 (1) Consistent selection of working correlation structure in GEE analysis based on Stein&apo...
Degrees of freedom is a fundamental concept in statistical modeling, as it provides a quan-titative ...
Model selection is difficult to analyse yet theoretically and empirically important, especially for ...
The concept of degrees of freedom plays an important role in statistical model-ing and is commonly u...
Model selection is of fundamental importance to high dimensional modelling featured in many contempo...
Overparametrized interpolating models have drawn increasing attention from machine learning. Some re...
Penalized inference of Gaussian graphical models is a way to assess the conditional independence str...
Penalized inference of Gaussian graphical models is a way to assess the conditional independence str...
Penalized inference of Gaussian graphical models is a way to assess the conditional independence str...
A hierarchical Bayesian formulation in Generalized Linear Models (GLMs) is proposed in this disserta...
Penalized inference of Gaussian graphical models is a way to assess the conditional independence str...
Penalized inference of Gaussian graphical models is a way to assess the conditional independence str...
Model selection is difficult to analyse yet theoretically and empirically important, especially for ...
選擇模型來解釋資料的方法有很多種, 像是AIC (Akaike 1974), BIC (Schwarz 1978), 以及Mallows’ Cp. 當考慮線性 迴歸模型選取時, 可將上述的模型選取法則...
In general model selection so far considered in literature, the parameter estimation loss and the pr...
2. 公表論文 (1) Consistent selection of working correlation structure in GEE analysis based on Stein&apo...
Degrees of freedom is a fundamental concept in statistical modeling, as it provides a quan-titative ...
Model selection is difficult to analyse yet theoretically and empirically important, especially for ...