Multivariate Markov Chains

Wai-Ki Ching, Ximin Huang, Michael K. Ng and Tak Kuen Siu
Additional contact information
Wai-Ki Ching: The University of Hong Kong
Ximin Huang: Georgia Institute of Technology
Michael K. Ng: Hong Kong Baptist University

Chapter Chapter 7 in Markov Chains, 2013, pp 177-200 from Springer

Abstract: Abstract By making use of the transition probability matrix in, a categorical data sequence of m states can be modeled by an m-state Markov chain model. In this chapter, we extend this idea to model multiple categorical data sequences. One would expect categorical data sequences generated by similar sources or the same source to be correlated to each other. Therefore, by exploring these relationships, one can develop better models for the categorical data sequences and hence better prediction rules.

Keywords: Linear Programming Problem; Credit Rating; Markov Chain Model; Transition Probability Matrix; Rating Sequence (search for similar items in EconPapers)
Date: 2013
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:isochp:978-1-4614-6312-2_7

Ordering information: This item can be ordered from
http://www.springer.com/9781461463122

DOI: 10.1007/978-1-4614-6312-2_7

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().