Randomized boosting with multivariable base-learners for high-dimensional variable selection and prediction
Background Statistical boosting is a computational approach to select and estimate
interpretable prediction models for high-dimensional biomedical data, leading to implicit …
interpretable prediction models for high-dimensional biomedical data, leading to implicit …
Learning from limited temporal data: Dynamically sparse historical functional linear models with applications to Earth science
Scientists and statisticians often want to learn about the complex relationships that connect
two variables that vary over time. Recent work on sparse functional historical linear models …
two variables that vary over time. Recent work on sparse functional historical linear models …
High-dimensional variable selection via low-dimensional adaptive learning
A stochastic search method, the so-called Adaptive Subspace (AdaSub) method, is
proposed for variable selection in high-dimensional linear regression models. The method …
proposed for variable selection in high-dimensional linear regression models. The method …
A Metropolized adaptive subspace algorithm for high-dimensional Bayesian variable selection
A Metropolized Adaptive Subspace Algorithm for High-Dimensional Bayesian Variable Selection
Page 1 Bayesian Analysis (2024) 19, Number 1, pp. 261–291 A Metropolized Adaptive …
Page 1 Bayesian Analysis (2024) 19, Number 1, pp. 261–291 A Metropolized Adaptive …
High-dimensional variable selection via low-dimensional adaptive learning
Nowadays one often faces the challenging scenario in which high-dimensional data is
observed. In a high-dimensional setting, where the number of explanatory variables p is …
observed. In a high-dimensional setting, where the number of explanatory variables p is …
Boosting with random selection of weak learners for variable selection in high-dimensional models
Statistical boosting is a promising alternative to popular regularization methods such as the
Lasso [1] for fitting high-dimensional models with many possible explanatory variables: early …
Lasso [1] for fitting high-dimensional models with many possible explanatory variables: early …