Add to ORKGAn improved AIC-based criterion is derived for model selection in general smoothing-based modeling, including semiparametric models and additive models. Examples are provided of applications to goodness-of-fit, smoothing parameter and variable selection in an additive model and semiparametric models, and variable selection in a model with a nonlinear function of linear terms.Statistics Working Papers Serie
One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive m...
One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive m...
This article considers the problem of order selection of the vector autoregressive moving-average mo...
In semiparametric regression models, we have developed a small-sample criterion, AICC, for the selec...
We derive an improved version of the Akaike information criterion (AICC) for quasi-likelihood models...
Various aspects of statistical model selection are discussed from the view point of a statistician. ...
Wager C, Vaida F, Kauermann G. Model selection for penalized spline smoothing using akaike informati...
Estimation of the expected Kullback-Leibler information is the basis for deriving the Akaike informa...
<p>This article discusses a general framework for smoothing parameter estimation for models with reg...
For regression and time series model selection, Hurvich and Tsai (1989) obtained a bias correction A...
This paper discusses a general framework for smoothing parameter estimation for models with regular ...
The Akaike information criterion, AIC, is widely used for model selection. Using the AIC as the esti...
The Akaike information criterion (AIC) is a widely used tool for model selection. AIC is derived as ...
This article considers the problem of order selection of the vector autoregressive moving-average mo...
In statistical settings such as regression and time series, we can condition on observed informatio...
One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive m...
One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive m...
This article considers the problem of order selection of the vector autoregressive moving-average mo...
In semiparametric regression models, we have developed a small-sample criterion, AICC, for the selec...
We derive an improved version of the Akaike information criterion (AICC) for quasi-likelihood models...
Various aspects of statistical model selection are discussed from the view point of a statistician. ...
Wager C, Vaida F, Kauermann G. Model selection for penalized spline smoothing using akaike informati...
Estimation of the expected Kullback-Leibler information is the basis for deriving the Akaike informa...
<p>This article discusses a general framework for smoothing parameter estimation for models with reg...
For regression and time series model selection, Hurvich and Tsai (1989) obtained a bias correction A...
This paper discusses a general framework for smoothing parameter estimation for models with regular ...
The Akaike information criterion, AIC, is widely used for model selection. Using the AIC as the esti...
The Akaike information criterion (AIC) is a widely used tool for model selection. AIC is derived as ...
This article considers the problem of order selection of the vector autoregressive moving-average mo...
In statistical settings such as regression and time series, we can condition on observed informatio...
One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive m...
One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive m...
This article considers the problem of order selection of the vector autoregressive moving-average mo...