Statistics & Data Science Seminar

  • Remote Access - Zoom
  • Virtual Presentation - ET
  • Professor
  • Department of Biostatistics
  • University of Washington

Inference on function-valued parameters using a restricted score test

Function-valued parameters that can be defined as the minimizer of a population risk arise naturally in many applications. Examples include the conditional mean function and the density function. Although there is an extensive literature on constructing consistent estimators for function-valued risk minimizers, such estimands can typically only be estimated at a slower-than-parametric rate in nonparametric and semiparametric models, and performing calibrated inference can be challenging. In this talk, we present a general inferential framework for function-valued risk minimizers as a nonparametric extension of the classical score test. We demonstrate that our framework is applicable in a wide variety of problems and describe how the approach can be used for inference on a mean regression function under (i) nonparametric and (ii) partially additive models. 

About the Speaker

Zoom Participation. See announcement.

Seminars will consist of a 40-minute talk, followed by a 5-10 minute 'discussion' by the speaker's host, and then followed by Q&A.

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