Journal article
Linking semi-Markov processes in time series— an approach to longitudinal data analysis
Mathematical biosciences, v 51(1), pp 141-164
1980
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
An approach to analyzing longitudinal data by linking absorbing, age-dependent, semi-Markov processes in a kind of time series is illustrated, using data from the Taichung Medical IUD Experiment. Although the immediate application of the methodology is in the field of family planning evaluation, it could be applied in other fields of research, in both the biomedical and the social sciences, when suitable longitudinal data are available. The approach to model building described in this paper is constructive-algorithmic. That is, rather than attempting to derive nice closed formulas, an approach that characterizes much model building in the biological and social sciences, attention is focused on designing algorithms and letting the computer solve the problem for you. This approach permits incorporating a greater degree of realism in the models but at the expense of a larger expenditure of effort in computer programming. From the statistical point of view, the data analysis procedures described herein would be classified as nonparametric. The paper is intended to supply an overview of the methodology along with an illustrative example and not a detailed account of the underlying technical machinery. A more detailed technical appendix is available upon request.
Metrics
Details
- Title
- Linking semi-Markov processes in time series— an approach to longitudinal data analysis
- Creators
- Charles J. Mode - Drexel UniversityMichael G. Soyka - Drexel University
- Publication Details
- Mathematical biosciences, v 51(1), pp 141-164
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- [Retired Faculty]
- Web of Science ID
- WOS:A1980KF34900009
- Scopus ID
- 2-s2.0-0019056838
- Other Identifier
- 991019174538504721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Biology
- Mathematical & Computational Biology