Sequential Estimation Functions in Stochastic Population Processes
Helmut Pruscha
Additional contact information
Helmut Pruscha: University of Munich, Institute of Mathematics
A chapter in Mathematical Statistics and Probability Theory, 1987, pp 189-203 from Springer
Abstract:
Abstract Sequential, i.e., randomly stopped estimation functions in a class of continuous time stochastic population models are considered. The class includes finite, irreducible Markov processes. Three types of efficient sequential estimation functions are discussed and their asymptotic behaviour is investigated. The main tools of analysis are taken from point process theory.
Keywords: Population processes; Markov branching process with immigration; birth-and-death-process; sequential estimation; Cramér-Rao inequality; asymptotic behaviour (search for similar items in EconPapers)
Date: 1987
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
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:sprchp:978-94-009-3965-3_18
Ordering information: This item can be ordered from
http://www.springer.com/9789400939653
DOI: 10.1007/978-94-009-3965-3_18
Access Statistics for this chapter
More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().