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A Pearson‐type goodness‐of‐fit test for stationary and time‐continuous Markov regression models
Author(s) -
AguirreHernández R.,
Farewell V. T.
Publication year - 2002
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1152
Subject(s) - statistics , goodness of fit , categorical variable , mathematics , markov chain , markov model , pearson's chi squared test , regression analysis , marginal model , test statistic , econometrics , statistical hypothesis testing
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.