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

Keyphrases

• Approximation Results 66%
• Bahadur Representation 33%
• Berry-Esseen Inequality 33%
• Concentration Results 33%
• Deviation Inequalities 33%
• False Discovery Proportion 33%
• Finite-sample Theory 100%
• Heavy-tailed Data 33%
• Heavy-tailed Errors 33%
• Huber Estimator 100%
• Least Square Method 33%
• Least Squares Estimate 33%
• Letting 33%
• Moderate Deviations 33%
• Moment Conditions 33%
• Multiple Testing 100%
• Multiple Testing Procedure 33%
• Noise Variables 33%
• Nominal Level 33%
• Non-asymptotic 100%
• Normal Approximation 66%
• Numerical Results 33%
• Outlying Observation 33%
• Robust M-estimation 100%
• Robust Normal 33%
• Sample Application 100%
• Sampling Distribution 33%
• Second Moment 33%
• Simultaneous Inference 33%
• Statist 33%
• Sub-Gaussian 33%
• Tuning Parameter 66%

Mathematics

• Esseen Inequality 50%
• Gaussian Distribution 50%
• Least-Squares Estimate 50%
• M-Estimation 100%
• Method of Least Square 50%
• Moment Condition 50%
• Multiple Testing 100%
• Nominal Level 50%
• Normal Approximation 100%
• Sampling Distribution 50%
• Testing Procedure 50%
• Variance 50%