Nonidentifiability of the Two-State BMAP
Joanna Rodríguez (),
Rosa E. Lillo () and
Pepa Ramírez-Cobo ()
Additional contact information
Joanna Rodríguez: Universidad Carlos III
Rosa E. Lillo: Universidad Carlos III
Pepa Ramírez-Cobo: Universidad de Cádiz
Methodology and Computing in Applied Probability, 2016, vol. 18, issue 1, 81-106
Abstract:
Abstract The capability of modeling non-exponentially distributed and dependent inter-arrival times as well as correlated batches makes the Batch Markovian Arrival Processes (BMAP) suitable in different real-life settings as teletraffic, queueing theory or actuarial contexts. An issue to be taken into account for estimation purposes is the identifiability of the process. This paper explores the identifiability of the stationary two-state BMAP noted as BMAP 2 (k), where k is the maximum batch arrival size, under the assumptions that both the interarrival times and batches sizes are observed. It is proven that for k ≥ 2 the process cannot be identified. The proof is based on the construction of an equivalent BMAP 2(k) to a given one, and on the decomposition of a BMAP 2 (k) into k BMAP 2 (2)s.
Keywords: Batch Markovian Arrival Process (BMAP); Identifiability problems; Hidden Markov models; Redundant representations; 60G55; 60J25 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://link.springer.com/10.1007/s11009-014-9401-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:18:y:2016:i:1:d:10.1007_s11009-014-9401-z
Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009
DOI: 10.1007/s11009-014-9401-z
Access Statistics for this article
Methodology and Computing in Applied Probability is currently edited by Joseph Glaz
More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().