Valid post-selection and post-regularization inference: An elementary, general approach
We present an expository, general analysis of valid post-selection or post-regularization
inference about a low-dimensional target parameter in the presence of a very high …
inference about a low-dimensional target parameter in the presence of a very high …
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 …
Climate policies that achieved major emission reductions: Global evidence from two decades
Meeting the Paris Agreement's climate targets necessitates better knowledge about which
climate policies work in reducing emissions at the necessary scale. We provide a global …
climate policies work in reducing emissions at the necessary scale. We provide a global …
[PDF][PDF] The computational limits of deep learning
Deep learning's recent history has been one of achievement: from triumphing over humans
in the game of Go to world-leading performance in image classification, voice recognition …
in the game of Go to world-leading performance in image classification, voice recognition …
Empirical asset pricing via machine learning
We perform a comparative analysis of machine learning methods for the canonical problem
of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic …
of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic …
Taming the factor zoo: A test of new factors
We propose a model selection method to systematically evaluate the contribution to asset
pricing of any new factor, above and beyond what a high‐dimensional set of existing factors …
pricing of any new factor, above and beyond what a high‐dimensional set of existing factors …
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 …
On asymptotically optimal confidence regions and tests for high-dimensional models
On asymptotically optimal confidence regions and tests for high-dimensional models Page 1
The Annals of Statistics 2014, Vol. 42, No. 3, 1166–1202 DOI: 10.1214/14-AOS1221 © Institute …
The Annals of Statistics 2014, Vol. 42, No. 3, 1166–1202 DOI: 10.1214/14-AOS1221 © Institute …
Inference on treatment effects after selection among high-dimensional controls
We propose robust methods for inference about the effect of a treatment variable on a scalar
outcome in the presence of very many regressors in a model with possibly non-Gaussian …
outcome in the presence of very many regressors in a model with possibly non-Gaussian …
Regularization: A Thresholding Representation Theory and a Fast Solver
The special importance of L 1/2 regularization has been recognized in recent studies on
sparse modeling (particularly on compressed sensing). The L 1/2 regularization, however …
sparse modeling (particularly on compressed sensing). The L 1/2 regularization, however …