A Pearson-type goodness-of-fit test for stationary and time-continuous Markov regression models.
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.
Aguirre-Hernández
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Depto. de Probabilidad y Estadística, IIMAS - UNAM, México, D.F. rebeca@sigma.iimas.unam.mx
Farewell
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VT
D015535:Arthritis, Psoriatic / Q000188:drug therapy / Q000473:pathology; D001799:Blood Sedimentation / Q000187:drug effects; D018450:Disease Progression; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D012044:Regression Analysis