scientific article; zbMATH DE number 7626769
From MaRDI portal
Publication:5053279
Jinbo Chen, Prabrisha Rakshit, Daniel S. Herman, Zi-Jian Guo
Publication date: 6 December 2022
Full work available at URL: https://arxiv.org/abs/2012.07133
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Doubly Robust Estimation in Missing Data and Causal Inference Models
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
- Robust inference on average treatment effects with possibly more covariates than observations
- A general theory of hypothesis tests and confidence regions for sparse high dimensional models
- Statistics for high-dimensional data. Methods, theory and applications.
- Oracle inequalities in empirical risk minimization and sparse recovery problems. École d'Été de Probabilités de Saint-Flour XXXVIII-2008.
- Spectral norm of products of random and deterministic matrices
- High-dimensional inference: confidence intervals, \(p\)-values and R-software \texttt{hdi}
- Honest variable selection in linear and logistic regression models via \(\ell _{1}\) and \(\ell _{1}+\ell _{2}\) penalization
- Self-concordant analysis for logistic regression
- Least squares after model selection in high-dimensional sparse models
- Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity
- The likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled Chi-square
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- Linear Hypothesis Testing in Dense High-Dimensional Linear Models
- The Group Lasso for Logistic Regression
- The central role of the propensity score in observational studies for causal effects
- One-Step Huber Estimates in the Linear Model
- Inference on Treatment Effects after Selection among High-Dimensional Controls
- Note on “Comparison of Model Selection for Regression” by Vladimir Cherkassky and Yunqian Ma
- Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions
- Global and Simultaneous Hypothesis Testing for High-Dimensional Logistic Regression Models
- Optimal Statistical Inference for Individualized Treatment Effects in High-Dimensional Models
- Double/debiased machine learning for treatment and structural parameters
- A modern maximum-likelihood theory for high-dimensional logistic regression
- Estimation And Selection Via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
- A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers
This page was built for publication: