Correlation and variable importance in random forests
B Gregorutti, B Michel, P Saint-Pierre - Statistics and Computing, 2017 - Springer
This paper is about variable selection with the random forests algorithm in presence of
correlated predictors. In high-dimensional regression or classification frameworks, variable …
correlated predictors. In high-dimensional regression or classification frameworks, variable …
Bayesian linear regression with sparse priors
We study full Bayesian procedures for high-dimensional linear regression under sparsity
constraints. The prior is a mixture of point masses at zero and continuous distributions …
constraints. The prior is a mixture of point masses at zero and continuous distributions …
On the prediction performance of the lasso
Although the Lasso has been extensively studied, the relationship between its prediction
performance and the correlations of the covariates is not fully understood. In this paper, we …
performance and the correlations of the covariates is not fully understood. In this paper, we …
Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
ZY Algamal, MH Lee - Computers in biology and medicine, 2015 - Elsevier
Cancer classification and gene selection in high-dimensional data have been popular
research topics in genetics and molecular biology. Recently, adaptive regularized logistic …
research topics in genetics and molecular biology. Recently, adaptive regularized logistic …
Lassoing the HAR model: A model selection perspective on realized volatility dynamics
Realized volatility computed from high-frequency data is an important measure for many
applications in finance, and its dynamics have been widely investigated. Recent notable …
applications in finance, and its dynamics have been widely investigated. Recent notable …
Machine learning and statistical analysis for materials science: stability and transferability of fingerprint descriptors and chemical insights
P Pankajakshan, S Sanyal, OE de Noord… - Chemistry of …, 2017 - ACS Publications
In the paradigm of virtual high-throughput screening for materials, we have developed a
semiautomated workflow or “recipe” that can help a material scientist to start from a raw data …
semiautomated workflow or “recipe” that can help a material scientist to start from a raw data …
Ordered weighted l1 regularized regression with strongly correlated covariates: Theoretical aspects
M Figueiredo, R Nowak - Artificial Intelligence and Statistics, 2016 - proceedings.mlr.press
This paper studies the ordered weighted L1 (OWL) family of regularizers for sparse linear
regression with strongly correlated covariates. We prove sufficient conditions for clustering …
regression with strongly correlated covariates. We prove sufficient conditions for clustering …
Predictive modeling of treatment resistant depression using data from STAR* D and an independent clinical study
Identification of risk factors of treatment resistance may be useful to guide treatment
selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) …
selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) …
Grouped variable selection with discrete optimization: Computational and statistical perspectives
Grouped variable selection with discrete optimization: Computational and statistical
perspectives Page 1 The Annals of Statistics 2023, Vol. 51, No. 1, 1–32 https://doi.org/10.1214/21-AOS2155 …
perspectives Page 1 The Annals of Statistics 2023, Vol. 51, No. 1, 1–32 https://doi.org/10.1214/21-AOS2155 …
Feature adaptation for sparse linear regression
Sparse linear regression is a central problem in high-dimensional statistics. We study the
correlated random design setting, where the covariates are drawn from a multivariate …
correlated random design setting, where the covariates are drawn from a multivariate …