- Referenced in 482 articles
- elastic-net regularization path for linear regression, logistic and multinomial regression models, poisson regression...
- Referenced in 132 articles
- predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression...
- Referenced in 135 articles
- large-scale linear classification. It supports logistic regression and linear support vector machines. We provide...
- Referenced in 88 articles
- package rms: Regression Modeling Strategies , Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting ... contains functions for binary and ordinal logistic regression models and the Buckley-James multiple regression ... estimation for logistic and ordinary linear models. rms works with almost any regression model ... work with binary or ordinal logistic regression, Cox regression, accelerated failure time models, ordinary linear...
- Referenced in 74 articles
- PrivateLR: Differentially Private Regularized Logistic Regression. PrivateLR implements two differentially private algorithms for estimating ... regularized logistic regression coefficients. A randomized algorithm F is epsilon-differentially private (C. Dwork, Differential...
- Referenced in 40 articles
- from Hosmer, Lemeshow and Sturdivant, ”Applied Logistic Regression” (3rd ed.) This package is a unofficial ... companion to the textbook ”Applied Logistic Regression” by D.W. Hosmer, S. Lemeshow and R.X. Sturdivant...
- Referenced in 37 articles
- matching; Multiple imputation; Multiple linear regression; Logistic regression; Univariate and multivariate censored regression...
- Referenced in 50 articles
- absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial...
- Referenced in 44 articles
- versatility of the method, including logistic regression, negative binomial regression, nonlinear mixed-effect models...
- Referenced in 38 articles
- with multiple regression and goes to logistic regression and generalized linear models. It has discussions...
- Referenced in 26 articles
- Algorithm for Gene Selection using Sparse Logistic Regression. Motivation: This paper gives ... efficient algorithm for the sparse logistic regression problem. The proposed algorithm is based...
- Referenced in 31 articles
- Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals...
- Referenced in 31 articles
- more model covariates logistic regression coefficients, their standard errors, and odds ratios and 95% confidence...
- Referenced in 25 articles
- implements maximum-likelihood estimation in the logistic regression with both binary (logit model) and multinomial ... based on MCMC in the logistic and Poisson regression model with random effects whose distribution...
- Referenced in 27 articles
- consensus problems. Numerical experiments solving sparse logistic regression problems are presented...
- Referenced in 20 articles
- epsilon insensitive regression, least mean square, logistic regression, least absolute deviation regression (see package examples...
- Referenced in 10 articles
- LOGISTIC procedure fits linear logistic regression models for discrete response data by the method ... likelihood. It can also perform conditional logistic regression for binary response data and exact logistic ... regression for binary and nominal response data. The maximum likelihood estimation is carried out with ... logit link function in the logistic regression models can be replaced by the probit function...
- Referenced in 17 articles
- group-lasso penalized least squares, logistic regression, Huberized SVM and squared...
- Referenced in 16 articles
- LogXact® 11: Exact Inference for Logistic Regression. The complexity of conducting regression analysis over multiple...
- Referenced in 11 articles
- discuss the results for distributed ridge regression, logistic regression and binary classification with a smoothed...