Meier L, Geer SVD, Buhlmann P (2008) The group lasso for logistic regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 70(1):51-71L. Meier, S. Geer, and P. Buhlmann, "The group lasso for logistic regression," J. R. Statist. Soc. B, vol. 70, ...
lassologisticgroupregression套索回归 The Group Lasso for Logistic Regression Lukas Meier, Sara van de Geer and Peter Bühlmann Presenter: Lu Ren ECE Dept., Duke University Sept. 19, 2008 Outline • From lasso to group lasso • logistic group lasso • Algorithms for the logistic group lasso...
The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparameterizations. We extend the group lasso to logistic regression models ...
The group Lasso for logistic regression The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have t... L Meier,S van de Geer,P Bhlmann,... - 《Journal of the Royal Statistical Society》 被引...
These scalar projections are then used as predictors in an efficient group-penalized17 multinomial logistic regression model for maximum marginal a posteriori estimation within a Bayesian hierarchical classification model18 (see Methods). The model also allows inclusion of additional individual parameters ...
Table 3 Lasso logistic regression and multivariate logistic regression analysis of the risk factors for in-hospital death in TTS. Full size table Figure 2 The nomogram for predicting the risk of in-hospital mortality in takotsubo syndrome patients. The top row of the ‘Points’ represents a scale...
(6) testing the statistical validity of the LASSO and logistic regression models using functional form tests;(7) testing the significance of the 62 omitted variables from the original 72; (8) validating the predicted critical illnesses using the mortality rates of critically ill patients;(9) ...
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We provide extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression (gaussian), multi-task gaussian, logistic and multinomial regression models (grouped or not), Poisson regression and the Cox model. The algorithm uses cyclical coordinate ...
The LASSO and random forest both had an area under the curve (AUC) of ~0.78 (95% CI 0.740–0.806 for Random Forest; 0.745–0.810 95% CI for LASSO) (Fig. 2a). This is above moderate prediction performance, reflecting that our data to a considerable extent can predict post-COVID cases...