Differentially private testing of symmetry equivalence hypotheses based on the characteristic function

Abstract

We investigate privacy aspects of tests for symmetry equivalence null hypotheses. Specifically we consider weighted L2--type tests as well as chi-squared type tests for multivariate symmetry based on the characteristic function, and their privacy properties are specifically quantified within the context of differential privacy. We consider both the case of known centre as well as tests for symmetry about an unknown centre.

Keywords

Differential privacy; Neighborhood--of--model validation; Symmetry testing.

Brief Biography

Simos Meintanis is Professor of Statistics & Econometrics with the National & Kapodistrian University of Athens, Greece, and Extraordinary Professor with North-West University, South Africa. He has previously held positions with the Univesrity of Patras, Greece (tenured), and the University of California Santa Barbara (visiting). Prof. Meintanis is a member of the Greek Statistical Institute, the American Statistical Association, the Institute of Mathematical Statistics, the South African Statistical Association, and an Elected Member of the International Statistical Institute. His research focuses mainly on Goodness-of-Fit and Change-Point methods

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