Modelling and estimating heavy-tailed non-homogeneous correlated queues: Pareto-inverse gamma HGLM with covariates

DOI10.1080/02664760500449311zbMath1118.62394OpenAlexW2011810396MaRDI QIDQ3592575

So Young Sohn, Sungcheol Yun, Youngjo Lee

Publication date: 13 September 2007

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664760500449311

zbMATH Keywords

hierarchical generalized linear modelinternet trafficrandom effects linear modelbetween-queue variabilitywithin-queue correlation

Mathematics Subject Classification ID

Communication networks in operations research (90B18) Applications of statistics (62P99) Inference from stochastic processes (62M99) Traffic problems in operations research (90B20)

Related Items (2)

A family of generalized linear models for repeated measures with normal and conjugate random effects ⋮ GLM-methods for volatility models

Cites Work

• Hierarchical generalised linear models: A synthesis of generalised linear models, random-effect models and structured dispersions
• Waiting-time tail probabilities in queues with long-tail service-time distributions
• Heavy tail modeling and teletraffic data. (With discussions and rejoinder)
• Empirical Bayesian analysis for traffic intensity: \(M/M/1\) queues with covariates
• Long-range Dependence: Revisiting Aggregation with Wavelets
• Modeling service–time distributions with non–exponential tails:beta mixtures of exponentials

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