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ORDERING RESULTS ON EXTREMES OF EXPONENTIATED LOCATION-SCALE MODELS

Part of: Special processes Distribution theory - Probability Nonparametric inference

Published online by Cambridge University Press:  18 October 2019

Sangita Das ,
Suchandan Kayal Open the ORCID record for Suchandan Kayal [Opens in a new window]  and
Debajyoti Choudhuri
Sangita Das
Affiliation:
Department of Mathematics, National Institute of Technology Rourkela, Rourkela 769008, India E-mail: [email protected], [email protected], [email protected], [email protected]
Suchandan Kayal
Affiliation:
Department of Mathematics, National Institute of Technology Rourkela, Rourkela 769008, India E-mail: [email protected], [email protected], [email protected], [email protected]
Debajyoti Choudhuri
Affiliation:
Department of Mathematics, National Institute of Technology Rourkela, Rourkela 769008, India E-mail: [email protected], [email protected], [email protected], [email protected]
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Abstract

In this paper, we consider exponentiated location-scale model and obtain several ordering results between extreme order statistics in various senses. Under majorization type partial order-based conditions, the comparisons are established according to the usual stochastic order, hazard rate order and reversed hazard rate order. Multiple-outlier models are considered. When the number of components are equal, the results are obtained based on the ageing faster order in terms of the hazard rate and likelihood ratio orders. For unequal number of components, we develop comparisons according to the usual stochastic order, hazard rate order, and likelihood ratio order. Numerical examples are considered to illustrate the results.

Keywords

exponentiated location-scale familymajorizationmultiple-outlier modelorder statisticsstochastic orders

MSC classification

Primary: 60E15: Inequalities; stochastic orderings 62G30: Order statistics; empirical distribution functions 60K10: Applications (reliability, demand theory, etc.)
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 35 , Issue 2 , April 2021 , pp. 331 - 354
Copyright
Copyright © Cambridge University Press 2019

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