Weak convergence in the functional autoregressive model

André Mas (1)

1 IMAG - Institut Montpelliérain Alexander Grothendieck

The functional autoregressive model is a Markov model taylored for data of functional nature. It revealed fruitful when attempting to model samples of dependent random curves and has been widely studied along the past few years. This article aims at completing the theoretical study of the model by adressing the crucial issue of weak convergence for estimates from the model. The main difficulties stem from an underlying inverse problem as well as from dependence between the data. Traditional facts about weak convergence in non parametric models appear : the normalizing sequence is an o(√n), a bias terms appears. Several original features of the functional framework are pointed out.

Mots clés: linear inverse problem; martingale difference arrays.; Hilbert space; autoregressive model; Functional data; perturbation theory; random operator; weak convergence; martingale difference arrays

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

André Mas. Weak convergence in the functional autoregressive model. Journal of Multivariate Analysis, 2007, 98 (6), pp.1231-1261. ⟨10.1016/j.jmva.2006.05.010⟩. ⟨hal-00008621⟩