A Pearson-type goodness-of-fit test for stationary and time-continuous Markov regression models

被引:53
作者
Aguirre-Hernández, R
Farewell, VT
机构
[1] Univ Nacl Autonoma Mexico, IIMAS, Dept Probabil & Estadist, Mexico City 04510, DF, Mexico
[2] MRC, Biostat Unit, Cambridge CB2 2BW, England
关键词
panel data; stationary transition probabilities; Markov regression; goodness of fit; parametric bootstrap;
D O I
10.1002/sim.1152
中图分类号
Q [生物科学];
学科分类号
07 [理学]; 0710 [生物学]; 09 [农学];
摘要
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 (C) 2002 John Wiley Sons, Ltd.
引用
收藏
页码:1899 / 1911
页数:13
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