Publication | Open Access
Non‐Parametric Estimation of the Conditional Distribution of the Interjumping Times for Piecewise‐Deterministic Markov Processes
21
Citations
16
References
2014
Year
EngineeringConditional DensityPiecewise‐deterministic Markov ProcessesStochastic AnalysisStochastic PhenomenonMarkov Chain Monte CarloStochastic SimulationHidden Markov ModelStochastic ProcessesNon‐parametric EstimationStatisticsJump DiffusionsDensity EstimationInterjumping TimesProbability TheoryMarkov Decision ProcessStochastic ModelingNon‐parametric MethodMarkov KernelStatistical InferenceJump Rate
ABSTRACT This paper presents a non‐parametric method for estimating the conditional density associated to the jump rate of a piecewise‐deterministic Markov process. In our framework, the estimation needs only one observation of the process within a long time interval. Our method relies on a generalization of Aalen's multiplicative intensity model. We prove the uniform consistency of our estimator, under some reasonable assumptions related to the primitive characteristics of the process. A simulation study illustrates the behaviour of our estimator.
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