Adaptive estimation for bifurcating markov chains

Abstract : In a first part, we prove Bernstein-type deviation inequalities for bifurcating Markov chains (BMC) under a geometric ergodicity assumption, completing former results of Guyon and Bitseki Penda, Djellout and Guillin. These preliminary results are the key ingredient to implement nonparametric wavelet thresholding estimation procedures: in a second part, we construct nonparametric estimators of the transition density of a BMC, of its mean transition density and of the corresponding invariant density, and show smoothness adaptation over various multivariate Besov classes under L p-loss error, for 1 ≤ p < ∞. We prove that our estimators are (nearly) optimal in a minimax sense. As an application, we obtain new results for the estimation of the splitting size-dependent rate of growth-fragmentation models and we extend the statistical study of bifurcating autoregressive processes.
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Submitted on : Wednesday, January 6, 2016 - 2:23:45 PM
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  • HAL Id : hal-01251594, version 1


Siméon Valère Bitseki Penda, Marc Hoffmann, Adélaïde Olivier. Adaptive estimation for bifurcating markov chains. Bernoulli, Bernoulli Society for Mathematical Statistics and Probability, 2016. ⟨hal-01251594⟩



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