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This paper describes an R package, glmnetcr, that provides a function for fitting a penalized continuation ratio model when interest lies in predicting an ordinal response. The function, glmnet.cr uses the coordinate descent fitting algorithm as implemented in glmnet and described by (Friedman, Hastie, and Tibshirani 2010). Methods for extracting all estimated coefficients, extracting non-zero ...

We explore the use of proper priors for variance parameters of certain sparse Bayesian regression models. This leads to a connection between sparse Bayesian learning (SBL) models (Tipping, 2001) and the recently proposed Bayesian Lasso (Park and Casella, 2008). We outline simple modifications of existing algorithms to solve this new variant which essentially uses type-II maximum likelihood to f...

Algorithms for simultaneous shrinkage and selection in regression and classification provide attractive solutions to knotty old statistical challenges. Nevertheless, as far as we can tell, Tibshirani’s Lasso algorithm has had little impact on statistical practice. Two particular reasons for this may be the relative inefficiency of the original Lasso algorithm, and the relative complexity of mor...

This paper presents an implementation, using the SAS System, of the “cluster scoring” method proposed by Tibshirani, Hastie, Balasubramanian, Eisen, Sherlock, Brown and Botstein (2002) for use in microarray experiments. The program is designed in a modular fashion, using SAS Macro Language, so that implementations for specific experimental cases can be added with ease. Current development accom...

This paper describes an R package, glmpathcr, that provides a function for fitting a penalized continuation ratio model when interest lies in predicting an ordinal response. The function, glmpath.cr uses the coordinate descent fitting algorithm as implemented in glmpath and described by (Park and Hastie 2007a). Methods for extracting all estimated coefficients, extracting non-zero coefficient e...

1. Introduction. This is a fascinating paper on an important topic: the choice of predictor variables in large-scale linear models. A previous paper in these pages attacked the same problem using the " LARS " algorithm (Efron, Hastie, Johnstone and Tibshirani [3]); actually three algorithms including the Lasso as middle case. There are tantalizing similarities between the Dantzig Selector (DS) ...

This paper presents a method for obtaining class membership probability estimates for multiclass classification problems by coupling the probability estimates produced by binary classifiers. This is an extension for arbitrary code matrices of a method due to Hastie and Tibshirani for pairwise coupling of probability estimates. Experimental results with Boosted Naive Bayes show that our method p...

This article reviews flexible statistical methods that are useful for characterizing the effect of potential prognostic factors on disease endpoints. Applications to survival models and binary outcome models are illustrated.