On joint weak reversed hazard rate order under symmetric copulas

Barmalzan, Ghobad; Kosari, Sajad

doi:10.3103/s1066530717040056

Cited by 3 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reinterpretation of elasticity in statistical terms and its better comprehension Pavía 2012, 2014) has indirectly expanded the use of (proportional) reserve hazard rates and has directly opened up new areas for their application. In addition to fostering their classical usefulness in survival analysis and stochastic ordering (see, e.g., Navarro et al 2014;Oliveira and Torrado 2015;Balakrishnan et al 2017;Kundu and Ghosh 2017;Hazra et al 2017;Arab and Oliveira 2019;Esna-Ashari et al 2020), they are being applied to new areas, such as epidemiology (Veres-Ferrer and Pavía 2021), risk management in business (Pavía et al 2012;Torrado and Oliviera 2013;Pavía and Veres-Ferrer 2016;Torrado and Navarro 2021), and trading and bidding in markets (Yang et al 2021;Auster and Kellner 2022). Furthermore, these tools are proving their usefulness in the field of medicine (Popović et al 2021), for the characterisation of stochastic distributions Pavía 2017a, 2017b) and as a means for proposing new probability models (Szymkowiak 2019;Kotelo 2019;Anis and De 2020).…”

Section: Introductionmentioning

confidence: 99%

The Elasticity of a Random Variable as a Tool for Measuring and Assessing Risks

Veres-Ferrer

Pavía

2022

Risks

View full text Add to dashboard Cite

Elasticity is a very popular concept in economics and physics, recently exported and reinterpreted in the statistical field, where it has given form to the so-called elasticity function. This function has proved to be a very useful tool for quantifying and evaluating risks, with applications in disciplines as varied as public health and financial risk management. In this study, we consider the elasticity function in random terms, defining its probability distribution, which allows us to measure for each stochastic process the probability of finding elastic or inelastic situations (i.e., with elasticities greater or less than 1). This new tool, together with new results on the most notable points of the elasticity function covered in this research, offers a new approach to risk assessment, facilitating proactive risk management. The paper also includes other contributions of interest, such as new results that relate elasticity and inverse hazard functions, the derivation of the functional form of the cumulative distribution function of a probability model with constant elasticity and how the elasticities of functionally dependent variables are related. The interested reader can also find in the paper examples of how elasticity cumulative distribution functions are calculated, and an extensive list of probability models with their associated elasticity functions and distributions.

show abstract

Section: Introductionmentioning

confidence: 99%

The Elasticity of a Random Variable as a Tool for Measuring and Assessing Risks

Veres-Ferrer

Pavía

2022

Risks

View full text Add to dashboard Cite

show abstract

“…For this purpose, many authors have introduced stochastic orders which take care of the dependence structure and are commonly known as joint stochastic orders. They also demonstrated applications of these orders in many areas, more specifically, analysis of random utility models, allocation of redundant components, and portfolio selection (see, for example, [1,5,7,8,10,21,22,34,37]). We recall the following definitions of some of the joint stochastic orders.…”

Section: Introductionmentioning

confidence: 99%

“…iv. joint weak reversed hazard rate order (written as [5]; and v. joint stochastic order (written as [37].…”

Section: Introductionmentioning

confidence: 99%

Residual Stochastic Precedence Order

Misra

Gupta

Misra

2020

Prob. Eng. Inf. Sci.

View full text Add to dashboard Cite

The aim of this paper is to introduce a new stochastic order based on the residual lifetimes of two nonnegative dependent random variables and the stochastic precedence order. We develop some characterizations and preservation properties of this stochastic order. In addition, we study some of its reliability properties and its relation with other existing stochastic orders. One of the possible applications in reliability theory has also been discussed.

show abstract