Concordance and value information criteria for optimal treatment decision

DOI10.1214/19-AOS1908zbMath1461.62029OpenAlexW3015894046MaRDI QIDQ2656587

Rui Song, Chengchun Shi, Wen-Bin Lu

Publication date: 11 March 2021

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aos/1611889218

zbMATH Keywords

variable selectionoptimal treatment regimetuning parameter selectionconcordance and value information criteria

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Statistical aspects of information-theoretic topics (62B10) Compound decision problems in statistical decision theory (62C25)

Related Items

Significance test for semiparametric conditional average treatment effects and other structural functions, A reluctant additive model framework for interpretable nonlinear individualized treatment rules, Flexible inference of optimal individualized treatment strategy in covariate adjusted randomization with multiple covariates

Uses Software

• rgenoud

Cites Work

• Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy
• Performance guarantees for individualized treatment rules
• U-processes: Rates of convergence
• Cube root asymptotics
• Estimating the dimension of a model
• Maximal inequalities for degenerate \(U\)-processes with applications to optimization estimators
• A Bernstein-type inequality for \(U\)-statistics and \(U\)-processes
• High-dimensional \(A\)-learning for optimal dynamic treatment regimes
• \({\mathcal Q}\)-learning
• Weak convergence and empirical processes. With applications to statistics
• Ranking and empirical minimization of \(U\)-statistics
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes
• Estimating Individualized Treatment Rules Using Outcome Weighted Learning
• Optimal Dynamic Treatment Regimes
• A Robust Method for Estimating Optimal Treatment Regimes
• Model selection consistency of Dantzig selector
• Inference for non-regular parameters in optimal dynamic treatment regimes
• Nonconcave Penalized Likelihood With NP-Dimensionality
• The Limiting Distribution of the Maximum Rank Correlation Estimator
• New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes
• U-Processes and Preference Learning
• Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions
• Tuning Parameter Selection in High Dimensional Penalized Likelihood
• Unnamed Item
• Unnamed Item
• Unnamed Item