The Logarithmic Super Divergence and Statistical Inference : Asymptotic Properties

Abstract

Statistical inference based on divergence measures have a long history. Recently, Maji, Ghosh and Basu (2014) have introduced a general family of divergences called the logarithmic super divergence (LSD) family. This family acts as a superfamily for both of the logarithmic power divergence (LPD) family (eg. Renyi, 1961) and the logarithmic density power divergence (LDPD)family introduced by Jones et al. (2001). In this paper we describe the asymptotic properties of the inference procedures resulting from this divergence in discrete models. The properties are well supported by real data examples.

26 pages; Pre-print, Under Review

References in corpus (3)

• Several Applications of Divergence Criteria in Continuous Families
• Asymptotic Properties of Minimum S-Divergence Estimator for Discrete Models
• The Logarithmic Super Divergence and its use in Statistical Inference

Cited by in corpus (4)

• A Scale-invariant Generalization of the Rényi Entropy, Associated Divergences and their Optimizations under Tsallis' Nonextensive Framework
• The Logarithmic Super Divergence and its use in Statistical Inference
• Generalized Fisher-Darmois-Koopman-Pitman Theorem and Rao-Blackwell Type Estimators for Power-Law Distributions
• Projection Theorems of Divergences and Likelihood Maximization Methods