Double-estimation-friendly inference for high-dimensional misspecified models
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Publication:2684689
DOI10.1214/22-STS850MaRDI QIDQ2684689
Publication date: 16 February 2023
Published in: Statistical Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1909.10828
conditional independencedouble robustnesshigh-dimensional inferencegeneralised linear modelsdebiased Lasso
Uses Software
Cites Work
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- Doubly Robust Estimation in Missing Data and Causal Inference Models
- On asymptotically optimal confidence regions and tests for high-dimensional models
- The Hardness of Conditional Independence Testing and the Generalised Covariance Measure
- Asymptotic normality and optimalities in estimation of large Gaussian graphical models
- Estimation and testing under sparsity. École d'Été de Probabilités de Saint-Flour XLV -- 2015
- Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
- High-dimensional inference in misspecified linear models
- A general theory of hypothesis tests and confidence regions for sparse high dimensional models
- Statistics for high-dimensional data. Methods, theory and applications.
- Slicing regression: A link-free regression method
- Significance testing in non-sparse high-dimensional linear models
- High-dimensional inference: confidence intervals, \(p\)-values and R-software \texttt{hdi}
- Regression analysis under link violation
- Least angle regression. (With discussion)
- On the conditions used to prove oracle results for the Lasso
- Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity
- Models as approximations. I. Consequences illustrated with linear regression
- Models as approximations. II. A model-free theory of parametric regression
- Improved double-robust estimation in missing data and causal inference models
- Sparse Matrix Inversion with Scaled Lasso
- Square-root lasso: pivotal recovery of sparse signals via conic programming
- Linear Hypothesis Testing in Dense High-Dimensional Linear Models
- Scaled sparse linear regression
- Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data
- The central role of the propensity score in observational studies for causal effects
- Science and Statistics
- Asymptotic Statistics
- Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
- Goodness-of-Fit Tests for High Dimensional Linear Models
- High-Dimensional Statistics
- Estimating Exposure Effects by Modelling the Expectation of Exposure Conditional on Confounders
- Semiparametric Efficiency in Multivariate Regression Models with Missing Data
- Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection
- Goodness-of-fit Testing in High Dimensional Generalized Linear Models
- Double/debiased machine learning for treatment and structural parameters
- Asymptotic Properties of Non-Linear Least Squares Estimators
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
- Maximum Likelihood Estimation of Misspecified Models
- Doubly robust tests of exposure effects under high‐dimensional confounding
