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Found 5 papers, 1 papers with code
Spike and slab variable selection: Frequentist and Bayesian strategies
In this paper we introduce a variable selection method referred to as a rescaled spike and slab model.
Statistics Theory Statistics Theory 62J07 (Primary) 62J05. (Secondary)
A Machine Learning Alternative to P-values
By a simple modification to the bootstrap data involving "noising up" a variable, the OOB method yields a variable importance (VIMP) index, which directly measures how much a specific variable contributes to the prediction precision of a model.
Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods
We find accurate estimation of individual treatment effects is possible even in complex heterogeneous settings but that the type of RF approach plays an important role in accuracy.
Random Forest Missing Data Algorithms
Using a large, diverse collection of data sets, performance of various RF algorithms was assessed under different missing data mechanisms.
Boosted nonparametric hazards with time-dependent covariates
Given functional data from a survival process with time-dependent covariates, we derive a smooth convex representation for its nonparametric log-likelihood functional and obtain its functional gradient.
