Title

Simulation input data modeling

Keywords

lambda distribution; least squares methods; maximum likelihood estimation; method of L-moments; S distribution B; TES (Test-Expand-Sample) distribution

Abstract

Input data modeling is a critical component of a successful simulation application. A perspective of the area is given with an emphasis on available probability distributions as models, estimation methods, model selection and discrimination, and goodness of fit. Three specific distribution classes (lambda, SB, TES processes) are discussed in some detail to illustrate characteristics that favor input models. Regarding estimation, we argue for maximum likelihood estimation over method of moments and other matching schemes due to intrinsic superior properties (presuming a specific model) and the capability of accommodating messy data types. We conclude with a list of specific research problems and areas warranting additional attention. © 1994 J.C. Baltzer AG, Science Publishers.

Publication Date

12-1-1994

Publication Title

Annals of Operations Research

Volume

53

Issue

1

Number of Pages

47-75

Document Type

Article

Identifier

scopus

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/BF02136826

Socpus ID

24044522589 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/24044522589

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