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Concentration study of M-estimators using the influence function

https://doi.org/10.1214/22-ejs2030

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Robust Statistics

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Robust Estimation of a Location Parameter

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von Mises Calculus For Statistical Functionals

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Weak Convergence and Empirical Processes

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Robust statistical learning with Lipschitz and convex loss functions

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Mean Estimation and Regression Under Heavy-Tailed Distributions: A Survey

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Weiszfeld’s Method: Old and New Results

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Uniform bounds for robust mean estimators

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Robust inference by influence functions

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Robust High-Dimensional Statistics

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The Influence Curve and Its Role in Robust Estimation

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Adaptive Huber Regression

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Concentration Inequalities

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Robust estimation via generalized quasi-gradients

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Agnostic Estimation of Mean and Covariance

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Robust Statistics, 2nd edn

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Challenging the empirical mean and empirical variance: A deviation study

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Sub-Gaussian mean estimators

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A new perspective on robust $M$-estimation: Finite sample theory and applications to dependence-adjusted multiple testing

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Robust covariance and scatter matrix estimation under Huber’s contamination model

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Sub-Gaussian estimators of the mean of a random vector

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Mean estimation with sub-Gaussian rates in polynomial time

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Robust sub-Gaussian estimation of a mean vector in nearly linear time

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A General Qualitative Definition of Robustness

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A Bound on Tail Probabilities for Quadratic Forms in Independent Random Variables

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A Note on Quantiles in Large Samples

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Robust Estimation of a Location Parameter

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A general Bahadur representation of M-estimators and its application to linear regression with nonstochastic designs

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A tail inequality for suprema of unbounded empirical processes with applications to Markov chains

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Robust Statistics: The Approach Based on Influence Functions

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Weak Convergence and Empirical Processes

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Geometric median and robust estimation in Banach spaces

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Understanding Black-box Predictions via Influence Functions

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Dimension-free PAC-Bayesian bounds for matrices, vectors, and linear least squares regression

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Fast Mean Estimation with Sub-Gaussian Rates

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How Hard Is Robust Mean Estimation?

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Lecture Notes: Selected topics on robust statistical learning theory

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Excess risk bounds in robust empirical risk minimization

inferredcites →https://doi.org/10.48550/arxiv.1911.05911openalex-snapshot

Recent Advances in Algorithmic High-Dimensional Robust Statistics

inferredcites →https://doi.org/10.48550/arxiv.2007.15618openalex-snapshot

Outlier Robust Mean Estimation with Subgaussian Rates via Stability

inferredcites →https://doi.org/10.48550/arxiv.2007.15839openalex-snapshot

Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization

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Decoupling: from dependence to independence

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Robust estimations from distribution structures: I. Mean

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Asymptotic normality and confidence region for Catoni's Z estimator

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Fixed-Parameter Prototype Regression Models

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Comparison and equality of generalized $$\psi $$-estimators

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Existence and uniqueness of weighted generalized ψ-estimators

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A two-stage optimized robust kernel density estimation for Bayesian classification with outliers

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Accelerated convergence of error quantiles using robust randomized quasi Monte Carlo methods

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On weighted multivariate sign functions

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Nonasymptotic heavy-tailed mean estimation in smooth Banach spaces

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Topics in robust statistical learning

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Determining classes for generalized ψ -estimators

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Robust Estimations From Distribution Structures: III- Invariant Moments

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Robust estimations from distribution structures: I. Mean

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Robust estimations from distribution structures: III. Invariant Moment

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Robust estimations from distribution structures: I. Mean

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Gaussian differentially private robust mean estimation and inference

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Robust algorithms and other contributions to machine learning

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