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Seminars

A non-smoothing framework for inference on functional means

  • 2023-03-20 (Mon.), 10:30 AM
  • Auditorium, B1F, Institute of Statistical Science;The tea reception will be held at 10:10.
  • Online live streaming through Cisco Webex will be available.
  • Dr. Hsin-wen Chang
  • Institute of Statistical Science, Academia Sinica

Abstract

This talk introduces a nonparametric inference framework that is applicable to occupation time curves derived from wearable device data. Motivated by the right-continuity of these curves, we develop a non-smoothing approach that involves weaker conditions than existing conditions imposed when using smoothing to estimate functional means under a fixed dense design. Notably, our procedure allows discontinuities in the functional covariances while accommodating discretization of the observed trajectories. Under this non-smoothing framework, we devise an empirical likelihood method to construct confidence bands for the functional means. Our method utilizes the known optimality of empirical likelihood. It also respects range and monotonicity constraints on occupation time curves.  A simulation study shows that the proposed procedures outperform competing functional data procedures. We illustrate the proposed methods using wearable device data from an NHANES study.

Please click here for participating the talk online

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1120320 Dr. Hsin-wen Chang.pdf
Update:2023-03-09 15:42
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