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Recent Advances in Robust Statistics: Theory and Applications pp 1–16Cite as

Flexible Distributions as an Approach to Robustness: The Skew-t Case

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Abstract

The use of flexible distributions with adaptive tails as a route to robustness has a long tradition. Recent developments in distribution theory, especially of non-symmetric form, provide additional tools for this purpose. We discuss merits and limitations of this approach to robustness as compared with classical methodology. Operationally, we adopt the skew-t as the working family of distributions used to implement this line of thinking.

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Acknowledgments

This paper stems directly from my oral presentation with the same title delivered at the ICORS 2015 conference held in Kolkata, India. I am grateful to the conference organizers for the kind invitation to present my work in that occasion. Thanks are also due to attendees at the talk that have contributed to the discussion with useful comments, some of which have been incorporated here.

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Authors and Affiliations

  1. Department of Statistical Sciences, University of Padua, Padua, Italy

    Adelchi Azzalini

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  1. Adelchi Azzalini
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Correspondence to Adelchi Azzalini .

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Editors and Affiliations

  1. Department of Mathematics, University of Trento, Trento, Italy

    Claudio Agostinelli

  2. Interdisciplinary Statistical Research Unit, Indian Statistical Institute, Kolkata, India

    Ayanendranath Basu

  3. Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology, Vienna, Austria

    Peter Filzmoser

  4. Sampling and Official Statistics Unit, Indian Statistical Institute, Kolkata, India

    Diganta Mukherjee

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Azzalini, A. (2016). Flexible Distributions as an Approach to Robustness: The Skew-t Case. In: Agostinelli, C., Basu, A., Filzmoser, P., Mukherjee, D. (eds) Recent Advances in Robust Statistics: Theory and Applications. Springer, New Delhi. https://doi.org/10.1007/978-81-322-3643-6_1

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