Pages that link to "Item:Q3757198"

The following pages link to How Biased is the Apparent Error Rate of a Prediction Rule? (Q3757198):

Displaying 50 items.

• Nearly unbiased variable selection under minimax concave penalty (Q117379) (← links)
• Reluctant generalized additive modeling (Q141337) (← links)
• Flexible and Interpretable Models for Survival Data (Q144105) (← links)
• Sparse estimation via nonconcave penalized likelihood in factor analysis model (Q261015) (← links)
• Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks (Q306638) (← links)
• A lasso for hierarchical interactions (Q366961) (← links)
• Degrees of freedom in lasso problems (Q447864) (← links)
• Degrees of freedom in low rank matrix estimation (Q525906) (← links)
• Ideal point discriminant analysis (Q578808) (← links)
• A note on the generalized degrees of freedom under the \(L_{1}\) loss function (Q607177) (← links)
• Cross validation model selection criteria for linear regression based on the Kullback-Leibler discrepancy (Q713660) (← links)
• A multistage algorithm for best-subset model selection based on the Kullback-Leibler discrepancy (Q736651) (← links)
• Model evaluation, discrepancy function estimation, and social choice theory (Q737003) (← links)
• On the association between a random parameter and an observable (Q882924) (← links)
• Statistical properties of convex clustering (Q887272) (← links)
• Variable selection for generalized linear mixed models by \(L_1\)-penalized estimation (Q892458) (← links)
• Bootstrap-based model selection criteria for beta regressions (Q905106) (← links)
• The negative correlations between data-determined bandwidths and the optimal bandwidth (Q913404) (← links)
• An assumption for the development of bootstrap variants of the Akaike information criterion in mixed models (Q945775) (← links)
• A survey of cross-validation procedures for model selection (Q975579) (← links)
• Bootstrap variants of the Akaike information criterion for mixed model selection (Q1023532) (← links)
• Estimation of the conditional risk in classification: the swapping method (Q1023660) (← links)
• Asymptotic bootstrap corrections of AIC for linear regression models (Q1048800) (← links)
• On model selection via stochastic complexity in robust linear regression (Q1299010) (← links)
• Appropriate penalties in the final prediction error criterion: A decision theoretic approach (Q1314700) (← links)
• Using specially designed exponential families for density estimation (Q1354440) (← links)
• Is \(C_{p}\) an empirical Bayes method for smoothing parameter choice? (Q1423096) (← links)
• P-splines with an \(\ell_1\) penalty for repeated measures (Q1616325) (← links)
• Tuning parameter selection in sparse regression modeling (Q1621202) (← links)
• Model selection criteria based on cross-validatory concordance statistics (Q1642996) (← links)
• Extending AIC to best subset regression (Q1643010) (← links)
• Smoothing spline ANOVA models for large data sets with Bernoulli observations and the randomized GACV. (Q1848843) (← links)
• Selection criteria for scatterplot smoothers (Q1848868) (← links)
• Least angle regression. (With discussion) (Q1879940) (← links)
• Evaluating the impact of exploratory procedures in regression prediction: A pseudosample approach (Q1896164) (← links)
• Model selection by resampling penalization (Q1951992) (← links)
• Asymptotic optimality of full cross-validation for selecting linear regression models (Q1962131) (← links)
• A large-sample model selection criterion based on Kullback's symmetric divergence (Q1962213) (← links)
• Automated data-driven selection of the hyperparameters for total-variation-based texture segmentation (Q2051545) (← links)
• Degrees of freedom for off-the-grid sparse estimation (Q2137058) (← links)
• Degrees of freedom and model selection for \(k\)-means clustering (Q2189599) (← links)
• A significance test for the lasso (Q2249837) (← links)
• Discussion: ``A significance test for the lasso'' (Q2249838) (← links)
• Rejoinder: ``A significance test for the lasso'' (Q2249839) (← links)
• Local behavior of sparse analysis regularization: applications to risk estimation (Q2252165) (← links)
• Distance-based linear discriminant analysis for interval-valued data (Q2282269) (← links)
• Additive models with trend filtering (Q2284363) (← links)
• Assessing prediction error at interpolation and extrapolation points (Q2286369) (← links)
• Compressed covariance estimation with automated dimension learning (Q2300095) (← links)
• Optimality of training/test size and resampling effectiveness in cross-validation (Q2317259) (← links)