64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

On Robustness of Statistical Inference based on the Logarithmic Super Divergence Family

Author

AM
Dr AVIJIT MAJI

Co-author

  • A
    Abhik Ghosh
  • A
    Ayanendranath Basu

Conference

64th ISI World Statistics Congress - Ottawa, Canada

Format: CPS Abstract

Keywords: inference

Session: CPS 17 - Statistical inference

Monday 17 July 4 p.m. - 5:25 p.m. (Canada/Eastern)

Abstract

This paper discusses a new superfamily of divergences that is similar in spirit to the S-divergence family introduced by Ghosh et al. (2017). This new family serves as an umbrella that contains the logarithmic power divergence family (Renyi, 1961; Maji et al., 2017) and the logarithmic density power divergence family (Jones et al., 2001) as special cases. Various properties of this new family and the corresponding minimum distance procedures are discussed with particular emphasis on the robustness issue; these properties are demonstrated both theoretically as well as through simulation studies. In particular the
method demonstrates the limitation of the first order influence function in assessing the robustness of the corresponding minimum distance procedures. In this respect, for the first time, we examine the necessity and usefulness of the third order influence functions for the divergence based test statistics.