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Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
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On Selection Criteria for the Tuning Parameter in Robust Divergence.

Shonosuke Sugasawa1,2, Shouto Yonekura2,3

  • 1Center for Spatial Information Science, The University of Tokyo, Chiba 277-8568, Japan.

Entropy (Basel, Switzerland)
|September 28, 2021
PubMed
Summary

This study introduces a new method for selecting tuning parameters in robust divergence methods, improving statistical inference efficiency. The approach uses an asymptotic Hyvarinen score approximation for better outlier handling.

Keywords:
Hyvarinen scoreefficiencyoutlierunnormalized model

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Area of Science:

  • Statistical Inference
  • Robust Statistics

Background:

  • Robust divergence methods, like density power and γ-divergence, aid statistical inference with outliers.
  • Current tuning parameter selection relies on rules-of-thumb, potentially causing inefficient inference.

Purpose of the Study:

  • To propose a novel selection criterion for tuning parameters in robust divergence models.
  • To enhance the efficiency of statistical inference in the presence of outliers.

Main Methods:

  • Utilized an asymptotic approximation of the Hyvarinen score applied to an unnormalized model.
  • The criterion requires only first and second-order partial derivatives of the density function.

Main Results:

  • The proposed criterion offers an efficient way to select tuning parameters for robust divergence.
  • The method's computational requirements are minimal, independent of the number of parameters.

Conclusions:

  • The new selection criterion improves robust statistical inference by optimizing tuning parameter choice.
  • Demonstrated effectiveness through numerical studies on normal distributions and regularized linear regression.