Weak convergence in the functional autoregressive model

2005/09/12 by André Mas, Mas, André · 2 citations
Mathematics · #FOS: Mathematics #Statistics Theory (math.ST) #math.ST #stat.TH

paper · pdf · doi:10.48550/arxiv.math/0509256

28 pages

arxiv created 2005/09/12 · arxiv updated 2016/08/16

Abstract

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(sqrtn), a bias terms appears. Several original features of the functional framework are pointed out.

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• Functional Periodic ARMA Processes
• Analysis of Distributional Dynamics for Repeated Cross-Sectional and Intra-Period Observations

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