On the robustness properties for maximum likelihood estimators of parameters in exponential power and generalized T distributions

ÇANKAYA M. N., ARSLAN O.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, vol.49, no.3, pp.607-630, 2020 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 49 Issue: 3
  • Publication Date: 2020
  • Doi Number: 10.1080/03610926.2018.1549243
  • Journal Name: COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.607-630
  • Keywords: Exponential power distributions, computing, modeling, robustness, PARTIALLY ADAPTIVE ESTIMATION, MULTIVARIATE LOCATION, CONVERGENCE BEHAVIOR, REGRESSION, INFERENCE, ALGORITHM, BREAKDOWN, RISK
  • Ankara University Affiliated: Yes

Abstract

Examining the robustness properties of maximum likelihood (ML) estimators of parameters in exponential power and generalized t distributions has been considered together. The well-known asymptotic properties of ML estimators of location, scale and added skewness parameters in these distributions are studied. The ML estimators for location, scale and scale variant (skewness) parameters are represented as an iterative reweighting algorithm (IRA) to compute the estimates of these parameters simultaneously. The artificial data are generated to examine performance of IRA for ML estimators of parameters simultaneously. We make a comparison between these two distributions to test the fitting performance on real data sets. The goodness of fit test and information criteria approve that robustness and fitting performance should be considered together as a key for modeling issue to have the best information from real data sets.