Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-21T22:59:04.274Z Has data issue: false hasContentIssue false
  • English
  • Français

An autoregressive model for irregular time series of variable stars

Published online by Cambridge University Press:  30 May 2017

Susana Eyheramendy ,
Felipe Elorrieta  and
Wilfredo Palma
Susana Eyheramendy
Affiliation:
Department of Statistics, P. Universidad Católica de Chile, Santiago, Chile. email: susana@mat.uc.cl, fielorrieta@mat.uc.cl, wilfredo@mat.uc.cl
Felipe Elorrieta
Affiliation:
Department of Statistics, P. Universidad Católica de Chile, Santiago, Chile. email: susana@mat.uc.cl, fielorrieta@mat.uc.cl, wilfredo@mat.uc.cl
Wilfredo Palma
Affiliation:
Department of Statistics, P. Universidad Católica de Chile, Santiago, Chile. email: susana@mat.uc.cl, fielorrieta@mat.uc.cl, wilfredo@mat.uc.cl
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

This paper discusses an autoregressive model for the analysis of irregularly observed time series. The properties of this model are studied and a maximum likelihood estimation procedure is proposed. The finite sample performance of this estimator is assessed by Monte Carlo simulations, showing accurate estimators. We implement this model to the residuals after fitting an harmonic model to light-curves from periodic variable stars from the Optical Gravitational Lensing Experiment (OGLE) and Hipparcos surveys, showing that the model can identify time dependency structure that remains in the residuals when, for example, the period of the light-curves was not properly estimated.

Keywords

autoregressiveharmonicirregular time series
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 259 - 262
Copyright
Copyright © International Astronomical Union 2017 

References

Debosscher, J., Sarro, L. M., Aerts, C., Cuypers, J., Vandenbussche, B., Garrido, R., & Solano, E. 2007, Astronomy & Astrophysics, 475, 1159.Google Scholar
Elorrieta, F., Eyheramendy, S., Jordán, A., Dékány, I., Catelan, M., Angeloni, R., Alonso-García, J., Contreras-Ramos, R., Gran, F., Hajdu, G., Espinoza, N., Saito, R., & Minniti, D. 2016, Astronomy & Astrophysics, 595, A82.CrossRefGoogle Scholar
Jordán, A., Espinoza, N., Rabus, M., Eyheramendy, S., Sing, D. K., Désert, J. M., Bakos, G. A., Fortney, J. J., López-Morales, M., Maxted, P. F. L., Triaud, A., & Szentgyorgyi, A. 2013, The Astrophysical Journal, 778, 184.Google Scholar
Palma, W. 2016, Wiley Series in Probability and Statistics.Google Scholar
Richards, J. W., Starr, D. L., Butler, N. R., Bloom, J. S., Brewer, J. M., Crellin-Quick, A., Higgins, J., Kennedy, R., & Rischard, M. 2011, The Astrophysical Journal, 733, 10.Google Scholar
Zechmeister, M. & Kürster, M. 2009, Astronomy & Astrophysics, 496, 577.Google Scholar