5 citations · 8 across the 8 of their papers we have counts for
9 papers
An Introduction to Double/Debiased Machine Learning
Achim Ahrens, Victor Chernozhukov, Christian Hansen +3
This paper provides an introduction to Double/Debiased Machine Learning (DML). DML is a general approach to performing inference about a target parameter in the presence of nuisanc…
Hedging Forecast Combinations With an Application to the Random Forest
Elliot Beck, Damian Kozbur, Michael Wolf
This papers proposes a generic, high-level methodology for generating forecast combinations that would deliver the optimal linearly combined forecast in terms of the mean-squared f…
Dimension-Free Anticoncentration Bounds for Gaussian Order Statistics with Discussion of Applications to Multiple Testing
Damian Kozbur
The following anticoncentration property is proved. The probability that the -order statistic of an arbitrarily correlated jointly Gaussian random vector with unit variance…
Inference for Dependent Data with Learned Clusters
Jianfei Cao, Christian Hansen, Damian Kozbur +1
This paper presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependen…
Targeted Undersmoothing
Christian Hansen, Damian Kozbur, Sanjog Misra
This paper proposes a post-model selection inference procedure, called targeted undersmoothing, designed to construct uniformly valid confidence sets for a broad class of functiona…
Sharp Convergence Rates for Forward Regression in High-Dimensional Sparse Linear Models
Damian Kozbur
Forward regression is a statistical model selection and estimation procedure which inductively selects covariates that add predictive power into a working statistical regression mo…
Analysis of Testing-Based Forward Model Selection
Damian Kozbur
This paper introduces and analyzes a procedure called Testing-based forward model selection (TBFMS) in linear regression problems. This procedure inductively selects covariates tha…
Inference in Additively Separable Models With a High-Dimensional Set of Conditioning Variables
Damian Kozbur
This paper studies nonparametric series estimation and inference for the effect of a single variable of interest x on an outcome y in the presence of potentially high-dimensional c…
Inference in High Dimensional Panel Models with an Application to Gun Control
Alexandre Belloni, Victor Chernozhukov, Christian Hansen +1
We consider estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high dimensional setting. The setting allows the number of…