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Nonparametric tests for transition probabilities in nonhomogeneous Markov processes

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  • Giorgos Bakoyannis

Abstract

This paper proposes nonparametric two-sample tests for the direct comparison of the probabilities of a particular transition between states of a continuous time nonhomogeneous Markov process with a finite state space. The proposed tests are a linear nonparametric test, an $ L^2 $L2-norm-based test and a Kolmogorov–Smirnov-type test. Significance level assessment is based on rigorous procedures, which are justified through the use of modern empirical process theory. Moreover, the $ L^2 $L2-norm and the Kolmogorov–Smirnov-type tests are shown to be consistent for every fixed alternative hypothesis. The proposed tests are also extended to more complex situations such as cases with incompletely observed absorbing states and non-Markov processes. Simulation studies show that the test statistics perform well even with small sample sizes. Finally, the proposed tests are applied to data on the treatment of early breast cancer from the European Organization for Research and Treatment of Cancer (EORTC) trial 10854, under an illness-death model.

Suggested Citation

  • Giorgos Bakoyannis, 2020. "Nonparametric tests for transition probabilities in nonhomogeneous Markov processes," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(1), pages 131-156, January.
  • Handle: RePEc:taf:gnstxx:v:32:y:2020:i:1:p:131-156
    DOI: 10.1080/10485252.2019.1705298
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    Cited by:

    1. Giorgos Bakoyannis, 2021. "Nonparametric analysis of nonhomogeneous multistate processes with clustered observations," Biometrics, The International Biometric Society, vol. 77(2), pages 533-546, June.
    2. Giorgos Bakoyannis & Dipankar Bandyopadhyay, 2022. "Nonparametric tests for multistate processes with clustered data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(5), pages 837-867, October.

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