On Implementation of the Markov Chain Monte Carlo Stochastic Approximation Algorithm

Yihua Jiang (), Peter Karcher and Yuedong Wang
Additional contact information
Yihua Jiang: Capital One Financial Corp., 15000 Capital One Dr.

Chapter Chapter 7 in Advances in Directional and Linear Statistics, 2011, pp 97-111 from Springer

Abstract: Abstract The Markov Chain Monte Carlo Stochastic Approximation Algorithm (MCMCSAA) was developed to compute estimates of parameters with incomplete data. In theory this algorithm guarantees convergence to the expected fixed points. However, due to its flexibility and complexity, care needs to be taken for implementation in practice. In this paper we show that the performance of MCMCSAA depends on many factors such as the Markov chain Monte Carlo sample size, the step-size of the parameter update, the initial values and the choice of an approximation to the Hessian matrix. Good choices of these factors are crucial to the practical performance and our results provide practical guidelines for these choices. We propose a new adaptive and hybrid procedure which is stable and faster while maintaining the same theoretical properties.

Keywords: Markov Chain Monte Carlo; Monte Carlo; Generalize Linear Mixed Model; Hybrid Algorithm; Stat Assoc (search for similar items in EconPapers)
Date: 2011
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:978-3-7908-2628-9_7

Ordering information: This item can be ordered from
http://www.springer.com/9783790826289

DOI: 10.1007/978-3-7908-2628-9_7

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 ().