.png){kind=icon}
.png){kind=icon}
Return
Dependence patterns for modeling simultaneous events
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ress.2016.05.008.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper we examine in detail some of the modeling capabilities of the stationary m-state BMAP, with simultaneous events up to size k, noted BMAP(m)(k). Specifically, we study the forms of the auto correlation functions of the inter-event times and event sizes. We provide a novel characterization of the functions which is suitable for analyzing the dependence patterns. In particular, this allows one to prove a geometrically decrease to zero of the functions and to identify four correlation patterns, when m=2. The case m >= 3 is illustrated via an extensive simulation study, from which it can be deduced that, as expected, richer structures can be obtained as m increases. In addition, the influence of the dependence patterns for both auto-correlation functions for the BMAP(2) (2) in the counting process has been explored through an empirical analysis. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Batch Markovian arrival process (BMAP)
Dependent inter-event times
Dependent event arrivals size
Autocorrelation function
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
R
IF:
11
Papers:
9.0K
Citations:
4.2W