Central limit theorem for bifurcating Markov chains
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Bifurcating Markov chains (BMC) are Markov chains indexed by a full binary tree representing the evolution of a trait along a population where each individual has two children. We first provide a central limit theorem for general additive functionals of BMC, and prove the existence of three regimes. This corresponds to a competition between the reproducing rate (each individual has two children) and the ergodicity rate for the evolution of the trait. This is in contrast with the work of Guyon (2007), where the considered additive functionals are sums of martingale increments, and only one regime appears. Our first result can be seen as a discrete time version, but with general trait evolution, of results in the time continuous setting of branching particle system from Adamczak and Miłoś (2015), where the evolution of the trait is given by an Ornstein-Uhlenbeck process. Secondly, motivated by the functional estimation of the density of the invariant probability measure which appears as the asymptotic distribution of the trait, we prove the consistence and the Gaussian fluctuations for a kernel estimator of this density. In this setting, it is interesting to note that the distinction of the three regimes disappears. This second result is a first step to go beyond the threshold condition on the ergodic rate given in previous statistical papers on functional estimation, see e.g. Doumic, Hoffmann, Krell and Roberts (2015).
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