LOCAL BANDWIDTH SELECTION FOR KERNEL DENSITY ESTIMATION IN BIFURCATING MARKOV CHAIN MODEL
Résumé
We propose an adaptive estimator for the stationary distribution of a bifurcating Markov Chain on R d. Bifurcating Markov chains (BMC for short) are a class of stochastic processes indexed by regular binary trees. A kernel estimator is proposed whose bandwidth is selected by a method inspired by the works of Goldenshluger and Lepski [18]. Drawing inspiration from dimension jump methods for model selection, we also provide an algorithm to select the best constant in the penalty.
| Origine | Fichiers produits par l'(les) auteur(s) |
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