Modeling correlated count data through some bivariate (or multivariate) discrete distribution is essential in many real-world applications in a wide range of fields, such as industrial quality control, healthcare, marketing, management science, and many others. Moreover, finding a discrete analogue to a continuous distribution can be useful in those problems where dealing with a continuous model is computationally cumbersome and substituting it with an appropriate discrete model can produce an approximate but still acceptable solution with a much smaller computational effort. In this work, two methods for deriving a bivariate discrete distribution from a bivariate continuous distribution are discussed, which retain the expression of either the joint density function or the joint survival function. These methods can be regarded as the bivariate extension of two popular methods used for deriving a discrete counterpart of a continuous distribution; they can be used as viable alternatives to existent techniques of construction of bivariate discrete random variables. Examples of application to several continuous distributions are presented in order to illustrate how the procedures work. A real dataset taken from the literature is eventually analyzed and fitted using two discrete analogues of a bivariate exponential distribution.

Discrete analogues of bivariate continuous distributions / A. Barbiero. ((Intervento presentato al 29. convegno European Conference on Operational Research tenutosi a Valencia nel 2018.

Discrete analogues of bivariate continuous distributions

A. Barbiero
Primo
2018

Abstract

Modeling correlated count data through some bivariate (or multivariate) discrete distribution is essential in many real-world applications in a wide range of fields, such as industrial quality control, healthcare, marketing, management science, and many others. Moreover, finding a discrete analogue to a continuous distribution can be useful in those problems where dealing with a continuous model is computationally cumbersome and substituting it with an appropriate discrete model can produce an approximate but still acceptable solution with a much smaller computational effort. In this work, two methods for deriving a bivariate discrete distribution from a bivariate continuous distribution are discussed, which retain the expression of either the joint density function or the joint survival function. These methods can be regarded as the bivariate extension of two popular methods used for deriving a discrete counterpart of a continuous distribution; they can be used as viable alternatives to existent techniques of construction of bivariate discrete random variables. Examples of application to several continuous distributions are presented in order to illustrate how the procedures work. A real dataset taken from the literature is eventually analyzed and fitted using two discrete analogues of a bivariate exponential distribution.
11-lug-2018
Settore SECS-S/01 - Statistica
Universitat Politècnica de València
International Federation of Operational Research Societies (IFORS)
Discrete analogues of bivariate continuous distributions / A. Barbiero. ((Intervento presentato al 29. convegno European Conference on Operational Research tenutosi a Valencia nel 2018.
Conference Object
File in questo prodotto:
File Dimensione Formato  
program-euro29.pdf

accesso aperto

Descrizione: programma con abstract
Tipologia: Post-print, accepted manuscript ecc. (versione accettata dall'editore)
Dimensione 3.07 MB
Formato Adobe PDF
3.07 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/581864
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact