56 citations · 118 across the 14 of their papers we have counts for
16 papers
Robust and Scalable Sure Screening of Fixed effects in Ultrahigh-dimensional Linear Mixed Models
Abhik Ghosh, Magne Thoresen
In modern applications of linear mixed models, the number of candidate fixed-effects covariates can grow exponentially with the sample size, while dependence induced by random effe…
penalizedclr: an R package for penalized conditional logistic regression for integration of multiple omics layers
Vera Djordjilović, Erica Ponzi, Therese Haugdahl Nøst +1
The matched case-control design, up until recently mostly pertinent to epidemiological studies, is becoming customary in biomedical applications as well. For instance, in omics stu…
A Computationally Efficient Approach to False Discovery Rate Control and Power Maximisation via Randomisation and Mirror Statistic
Marco Molinari, Magne Thoresen
Simultaneously performing variable selection and inference in high-dimensional regression models is an open challenge in statistics and machine learning. The increasing availabilit…
Exponential Consistency of M-estimators in Generalized Linear Mixed Models
Andrea M. Bratsberg, Magne Thoresen, Abhik Ghosh
Generalized linear mixed models are powerful tools for analyzing clustered data, where the unknown parameters are classically (and most commonly) estimated by the maximum likelihoo…
RaJIVE: Robust Angle Based JIVE for Integrating Noisy Multi-Source Data
Erica Ponzi, Magne Thoresen, Abhik Ghosh
With increasing availability of high dimensional, multi-source data, the identification of joint and data specific patterns of variability has become a subject of interest in many…
On optimal two-stage testing of multiple mediators
Vera Djordjilović, Jesse Hemerik, Magne Thoresen
Mediation analysis in high-dimensional settings often involves identifying potential mediators among a large number of measured variables. For this purpose, a two-step familywise e…
Robust Sure Independence Screening for Non-polynomial dimensional Generalized Linear Models
Abhik Ghosh, Erica Ponzi, Torkjel Sandanger +1
We consider the problem of variable screening in ultra-high dimensional generalized linear models (GLMs) of non-polynomial orders. Since the popular SIS approach is extremely unsta…
A robust variable screening procedure for ultra-high dimensional data
Abhik Ghosh, Magne Thoresen
Variable selection in ultra-high dimensional regression problems has become an important issue. In such situations, penalized regression models may face computational problems and…
Permutation testing in high-dimensional linear models: an empirical investigation
Jesse Hemerik, Magne Thoresen, Livio Finos
Permutation testing in linear models, where the number of nuisance coefficients is smaller than the sample size, is a well-studied topic. The common approach of such tests is to pe…
Optimal two-stage testing of multiple mediators
Vera Djordjilović, Jesse Hemerik, Magne Thoresen
Mediation analysis in high-dimensional settings often involves identifying potential mediators among a large number of measured variables. For this purpose, a two step familywise e…
Consistent Fixed-Effects Selection in Ultra-high dimensional Linear Mixed Models with Error-Covariate Endogeneity
Abhik Ghosh, Magne Thoresen
Recently, applied sciences, including longitudinal and clustered studies in biomedicine require the analysis of ultra-high dimensional linear mixed effects models where we need to…
Non-Concave Penalization in Linear Mixed-Effects Models and Regularized Selection of Fixed Effects
Abhik Ghosh, Magne Thoresen
Mixed-effect models are very popular for analyzing data with a hierarchical structure, e.g. repeated observations within subjects in a longitudinal design, patients nested within c…
Integrative clustering of high-dimensional data with joint and individual clusters, with an application to the Metabric study
Kristoffer Hellton, Magne Thoresen
When measuring a range of different genomic, epigenomic, transcriptomic and other variables, an integrative approach to analysis can strengthen inference and give new insights. Thi…
Covariate Selection in High-Dimensional Generalized Linear Models With Measurement Error
Øystein Sørensen, Arnoldo Frigessi, Magne Thoresen
In many problems involving generalized linear models, the covariates are subject to measurement error. When the number of covariates p exceeds the sample size n, regularized method…
When and why are principal component scores a good tool for visualizing high-dimensional data?
Kristoffer Hellton, Magne Thoresen
Principal component analysis (PCA) is a popular dimension reduction technique often used to visualize high-dimensional data structures. In genomics, this can involve millions of va…
Measurement Error in Lasso: Impact and Correction
Øystein Sørensen, Arnoldo Frigessi, Magne Thoresen
Regression with the lasso penalty is a popular tool for performing dimension reduction when the number of covariates is large. In many applications of the lasso, like in genomics,…