Bayesian semi-parametric estimation of the long-memory parameter under FEXP-priors

Willem Kruijer (1) , Judith Rousseau (2, 3)

1 Biometris
2 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision
3 CREST - Centre de Recherche en Économie et Statistique

For a Gaussian time series with long-memory behavior, we use the FEXP-model for semi-parametric estimation of the long-memory parameter $d$. The true spectral density $f_o$ is assumed to have long-memory parameter $d_o$ and a FEXP-expansion of Sobolev-regularity $\be > 1$. We prove that when $k$ follows a Poisson or geometric prior, or a sieve prior increasing at rate $n^{\frac{1}{1+2\be}}$, $d$ converges to $d_o$ at a suboptimal rate. When the sieve prior increases at rate $n^{\frac{1}{2\be}}$ however, the minimax rate is almost obtained. Our results can be seen as a Bayesian equivalent of the result which Moulines and Soulier obtained for some frequentist estimators.

Mots clés: Bayesian semi-parametric; posterior concentration; bernstein von Mises; long memory models

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Willem Kruijer, Judith Rousseau. Bayesian semi-parametric estimation of the long-memory parameter under FEXP-priors. 2012. ⟨hal-00672772⟩