Regularization Paths for Conditional Logistic Regression: TheclogitL1Package

Shrinking Targets for Regularization of Logistic Regression

Bias reduction in conditional logistic regression.

We employ a general bias preventive approach developed by Firth (Biometrika 1993; 80:27-38) to reduce the bias of an estimator of the log-odds ratio parameter in a matched case-control study by solving a modified score equation. We also propose a method to calculate the standard error of the resultant estimator. A closed-form expression for the estimator of the log-odds ratio parameter is deriv...

Efficient Monte Carlo Methods for Conditional Logistic Regression

Exact inference for the logistic regression model is based on generating the permutation distribution of the sufficient statistics for the regression parameters of interest conditional on the sufficient statistics for the remaining (nuisance) parameters. Despite the availability of fast numerical algorithms for the exact computations, there are numerous instances where a data set is too large t...

Sample size determination for logistic regression

The problem of sample size estimation is important in medical applications, especially in cases of expensive measurements of immune biomarkers. This paper describes the problem of logistic regression analysis with the sample size determination algorithms, namely the methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regr...

Multiclass Bounded Logistic Regression – Efficient Regularization with Interior Point Method

Logistic regression has been widely used in classification tasks for many years. Its optimization in case of linear separable data has received extensive study due to the problem of a monoton likelihood. This paper presents a new approach, called bounded logistic regression (BLR), by solving the logistic regression as a convex optimization problem with constraints. The paper tests the accuracy ...