A Pearson‐type goodness‐of‐fit test for stationary and time‐continuous Markov regression models
- 7 June 2002
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 21 (13), 1899-1911
- https://doi.org/10.1002/sim.1152
Abstract
Markov regression models describe the way in which a categorical response variable changes over time for subjects with different explanatory variables. Frequently it is difficult to measure the response variable on equally spaced discrete time intervals. Here we propose a Pearson‐type goodness‐of‐fit test for stationary Markov regression models fitted to panel data. A parametric bootstrap algorithm is used to study the distribution of the test statistic. The proposed technique is applied to examine the fit of a Markov regression model used to identify markers for disease progression in psoriatic arthritis. Copyright © 2002 John Wiley & Sons, Ltd.Keywords
This publication has 6 references indexed in Scilit:
- The Jackknife and BootstrapPublished by Springer Science and Business Media LLC ,1995
- Multi‐state Markov models for analysing incomplete disease history data with illustrations for hiv diseaseStatistics in Medicine, 1994
- An Introduction to the BootstrapPublished by Springer Science and Business Media LLC ,1993
- Statistical analysis of the stages of HIV infection using a Markov modelStatistics in Medicine, 1989
- Testing Departures from Time Homogeneity in Multistate Markov ProcessesJournal of the Royal Statistical Society Series C: Applied Statistics, 1988
- The Analysis of Panel Data under a Markov AssumptionJournal of the American Statistical Association, 1985