A Pearson‐type goodness‐of‐fit test for stationary and time‐continuous Markov regression models | Zendy

AguirreHernández R. | Zendy; Farewell V. T. | Zendy

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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.

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