Optimal observation times for multistate Markov models—applications to pneumococcal colonization studies

Juha Mehtälä, Kari Auranen and Sangita Kulathinal

Journal of the Royal Statistical Society Series C, 2015, vol. 64, issue 3, 451-468

Abstract: type="main" xml:id="rssc12084-abs-0001">

Applications of finite state Markov transition models are numerous and the problem of estimating transition rates of such processes has been considered in many fields of science. Because these processes cannot always be followed in continuous time, the investigators often confront the question of when to measure the state of the process. The estimation of transition rates then needs to be based on a sequence of discrete time data, and the variance and estimability of the estimators greatly depend on the time spacings between consecutive observations. We study optimal time spacings for a sequence of discrete time observations to estimate the transition rates of a time homogeneous multistate Markov process. For comparative studies, optimal time spacings to estimate rate ratios are considered. Optimality criteria are formulated through the minimization of the variances of the parameter estimators of interest and are investigated assuming a stationary initial distribution. For practical purposes, we propose a simple approximation for the optimal time spacing and study the limits for its applicability. The work is motivated by studies of colonization with Streptococcus pneumoniae.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1111/rssc.2015.64.issue-3 (text/html)
Access to full text is restricted to subscribers.

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:bla:jorssc:v:64:y:2015:i:3:p:451-468

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().