A critical review of LASSO and its derivatives for variable selection under dependence among covariates
L Freijeiro‐González, M Febrero‐Bande… - International …, 2022 - Wiley Online Library
The limitations of the well‐known LASSO regression as a variable selector are tested when
there exists dependence structures among covariates. We analyse both the classic situation …
there exists dependence structures among covariates. We analyse both the classic situation …
Panning for gold:'model-X'knockoffs for high dimensional controlled variable selection
Many contemporary large-scale applications involve building interpretable models linking a
large set of potential covariates to a response in a non-linear fashion, such as when the …
large set of potential covariates to a response in a non-linear fashion, such as when the …
A differential equation for modeling Nesterov's accelerated gradient method: Theory and insights
We derive a second-order ordinary differential equation (ODE) which is the limit of
Nesterov's accelerated gradient method. This ODE exhibits approximate equivalence to …
Nesterov's accelerated gradient method. This ODE exhibits approximate equivalence to …
Approximate residual balancing: debiased inference of average treatment effects in high dimensions
There are many settings where researchers are interested in estimating average treatment
effects and are willing to rely on the unconfoundedness assumption, which requires that the …
effects and are willing to rely on the unconfoundedness assumption, which requires that the …
A unifying tutorial on approximate message passing
OY Feng, R Venkataramanan, C Rush… - … and Trends® in …, 2022 - nowpublishers.com
Over the last decade or so, Approximate Message Passing (AMP) algorithms have become
extremely popular in various structured high-dimensional statistical problems. Although the …
extremely popular in various structured high-dimensional statistical problems. Although the …
Adaptive huber regression
Big data can easily be contaminated by outliers or contain variables with heavy-tailed
distributions, which makes many conventional methods inadequate. To address this …
distributions, which makes many conventional methods inadequate. To address this …
[كتاب][B] Statistical foundations of data science
Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …
statistical models, contemporary statistical machine learning techniques and algorithms …
[كتاب][B] Introduction to high-dimensional statistics
C Giraud - 2021 - taylorfrancis.com
Praise for the first edition:"[This book] succeeds singularly at providing a structured
introduction to this active field of research.… it is arguably the most accessible overview yet …
introduction to this active field of research.… it is arguably the most accessible overview yet …
Precise Error Analysis of Regularized -Estimators in High Dimensions
C Thrampoulidis, E Abbasi… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
A popular approach for estimating an unknown signal x 0∈ ℝ n from noisy, linear
measurements y= Ax 0+ z∈ ℝ m is via solving a so called regularized M-estimator: x̂:= arg …
measurements y= Ax 0+ z∈ ℝ m is via solving a so called regularized M-estimator: x̂:= arg …
High-dimensional inference: confidence intervals, p-values and R-software hdi
R Dezeure, P Bühlmann, L Meier, N Meinshausen - Statistical science, 2015 - JSTOR
We present a (selective) review of recent frequentist high-dimensional inference methods for
constructing p-values and confidence intervals in linear and generalized linear models. We …
constructing p-values and confidence intervals in linear and generalized linear models. We …