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Tests for semiparametric model based on non-homogeneous Markov process

Author

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  • Marzec, Leszek
  • Marzec, Pawel

Abstract

In the paper the semiparametric Markov process model is considered. This model describes the effect of an observable covariate process on the transition intensity. A multivariate process based on the observed jumps between the states of Markov process is defined. Its weak convergence to a continuous Gaussian martingale is established which leads to the formal construction of the Cramer-von Mises, Kolmogorov-Smirnov and [chi]2-type goodness-of-fit tests. The optimality problem in the class of tests under a sequence of local alternatives is also discussed.

Suggested Citation

  • Marzec, Leszek & Marzec, Pawel, 1996. "Tests for semiparametric model based on non-homogeneous Markov process," Statistics & Probability Letters, Elsevier, vol. 27(2), pages 137-143, April.
    • Handle: RePEc:eee:stapro:v:27:y:1996:i:2:p:137-143
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    Cited by:

    1. Marzec, Leszek & Marzec, Pawel, 1998. "Testing based on sampled data for proportional hazards model," Statistics & Probability Letters, Elsevier, vol. 37(3), pages 303-313, March.

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