Estimation in autoregressive model with measurement error - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Journal Articles ESAIM: Probability and Statistics Year : 2014

Estimation in autoregressive model with measurement error


Consider an autoregressive model with measurement error: we observe Z(i) = X-i + epsilon(i), where the unobserved X-i is a stationary solution of the autoregressive equation X-i = g(theta 0) (Xi- 1) + xi(i). The regression function g(theta 0) is known up to a finite dimensional parameter theta(0) to be estimated. The distributions of xi(1) and X-0 are unknown and g(theta) belongs to a large class of parametric regression functions. The distribution of epsilon(0) is completely known. We propose an estimation procedure with a new criterion computed as the Fourier transform of a weighted least square contrast. This procedure provides an asymptotically normal estimator (theta) over cap of theta(0), for a large class of regression functions and various noise distributions.
Fichier principal
Vignette du fichier
Dedecker_2014_ESAIM Prob Stat_1.pdf (870.66 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

hal-02633693 , version 1 (27-05-2020)





Jérôme Dedecker, Adeline Samson, Marie-Luce Taupin. Estimation in autoregressive model with measurement error. ESAIM: Probability and Statistics, 2014, 18, pp.277-307. ⟨10.1051/ps/2013037⟩. ⟨hal-02633693⟩
26 View
41 Download



Gmail Facebook X LinkedIn More