In this work, we propose a novel procedure for deriving a discrete counterpart to a continuous probability distribution. This procedure or, better, this class of procedures, is based on an appropriate distance between cumulative distribution functions. A discrete random distribution, supported on the set of integer values, is obtained by minimizing its distance to the assigned continuous probability distribution. An application is provided with reference to the negative exponential distribution, along with a comparison with an existing discretization technique.

A new method for building a discrete analogue to a continuous random variable based on minimization of a distance between distribution functions

Hitaj A.
2021-01-01

Abstract

In this work, we propose a novel procedure for deriving a discrete counterpart to a continuous probability distribution. This procedure or, better, this class of procedures, is based on an appropriate distance between cumulative distribution functions. A discrete random distribution, supported on the set of integer values, is obtained by minimizing its distance to the assigned continuous probability distribution. An application is provided with reference to the negative exponential distribution, along with a comparison with an existing discretization technique.
2021
2022
2021
2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021
338
341
4
ELETTRONICO
Institute of Electrical and Electronics Engineers Inc.
United States
9781665416566
Inglese
Approximation; count distribution; Cramér-von Mises distance; discretization; exponential distribution
no
268
info:eu-repo/semantics/bookPart
Barbiero, A.; Hitaj, A.
reserved
Contributo specifico in volume::Articolo in Volume
2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2132424
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