UNIVERSITY OF IDAHODEPARTMENT OF MATHEMATICS
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| Multiplier inequalities have proved to
be one of the key tools of modern empirical process
theory, with applications to central limit theorems,
bootstrap theory, and weighted likelihood methods in
statistics. In this talk I will review some classical multiplier inequalities, present a new multiplier inequality, and discuss several statistical applications. The applications include new results concerning convergence rates of least squares estimators (LSE) in regression models with possibly heavy-tailed errors. Particular cases involving sparse linear regression and shape restrictions will be mentioned. [This talk is based on the University of Washington Ph.D. work of Qiyang (Roy) Han.]
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