Subject-specific Bradley–Terry–Luce models with implicit variable selection

Subject-specific Bradley-Terry-Luce Models with Implicit Variable Selection

The Bradley-Terry-Luce (BTL) model for paired comparison data is able to obtain a ranking of the objects that are compared pairwise by subjects. The task of each subject is to make preference decisions in favor of one of the objects. This decision is binary when subjects prefer either the first object or the second object, but can also be ordinal when subjects make their decisions on a Likert s...

Multinomial logit models with implicit variable selection

Multinomial logit models which are most commonly used for the modeling of unordered multi-category responses are typically restricted to the use of few predictors. In the high-dimensional case maximum likelihood estimates frequently do not exist. In this paper we are developing a boosting technique called multinomBoost that performs variable selection and fits the multinomial logit model also w...

Generalized Additive Models with Implicit Variable Selection by Likelihood-Based Boosting

We examine the GAMBoost method and R package of Tutz and Binder (2006), and its effectiveness. Whilst in many examples the algorithm performs relatively well, we find significant difficulties with the approach taken, particularly in terms of computational time, automatic smoothing parameter selection and the claimed ‘implicit’ variable selection. We also find that GAMBoost performs particularly...

Scalable Bayesian Kernel Models with Variable Selection

Nonlinear kernels are used extensively in regression models in statistics and machine learning since they often improve predictive accuracy. Variable selection is a challenge in the context of kernel based regression models. In linear regression the concept of an effect size for the regression coefficients is very useful for variable selection. In this paper we provide an analog for the effect ...

Improvement of EEG classification with a subject-specific feature selection