Remote Access Theory of Probability and Mathematical Statistics

Theory of Probability and Mathematical Statistics

ISSN 1547-7363(online) ISSN 0094-9000(print)

 
 

 

Spectral estimation in the presence of missing data


Authors: Natalia Bahamonde and Paul Doukhan
Original publication: Teoriya Imovirnostei ta Matematichna Statistika, tom 95 (2016).
Journal: Theor. Probability and Math. Statist. 95 (2017), 59-79
DOI: https://doi.org/10.1090/tpms/1022
Published electronically: February 28, 2018
Full-text PDF

Abstract | References | Additional Information

Abstract: In this article we propose a quasi-Whittle estimator for parametric families of time series models in the presence of missing data. This estimator extends results to the incompletely observed case. This extension is valid to non-Gaussian and nonlinear models. It also allows us to bound the variance of an associated quasiperiodogram. A simulation study empirically validates the proposed estimate for mixing and nonmixing models.


References [Enhancements On Off] (What's this?)


Additional Information

Natalia Bahamonde
Affiliation: Instituto de Estadística, Pontificia Universidad Católica de Valparaíso
Address at time of publication: Avenida Errazuriz 2734, Valparaíso, Chile
Email: natalia.bahamonde@pucv.cl

Paul Doukhan
Affiliation: UMR 8088 AGM, University Cergy-Pontoise, France
Address at time of publication: Mathematics, office E 5.28, UCP site Saint-Martin, 2 Bd. Adolphe Chauvin, 95000 Cergy-Pontoise, France
Email: doukhan@u-cergy.fr

DOI: https://doi.org/10.1090/tpms/1022
Keywords: Limit theorems, time series, auto correlation, Whittle estimator, weakly dependent
Received by editor(s): September 6, 2016
Published electronically: February 28, 2018
Additional Notes: This work has been developed within the MME-DII center of excellence (ANR-11-LABEX-0023-01) and the Mathamsud 16-MATH-03 SIDRE project
Article copyright: © Copyright 2018 American Mathematical Society

American Mathematical Society