Articles | Volume 1, issue 1
https://doi.org/10.5194/ascmo-1-15-2015
https://doi.org/10.5194/ascmo-1-15-2015
25 Mar 2015
 | 25 Mar 2015

Joint inference of misaligned irregular time series with application to Greenland ice core data

T. K. Doan, J. Haslett, and A. C. Parnell

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Cited articles

Chiles, J.-P. and Delfiner, P.: Geostatistics: modeling spatial uncertainty, Vol. 497, John Wiley & Sons, 2012.
Cismondi, F., Fialho, A., Vieira, S., Sousa, J., Reti, S., Howell, M., and Finkelstein, S.: Computational intelligence methods for processing misaligned, unevenly sampled time series containing missing data, in: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 224–231, https://doi.org/10.1109/CIDM.2011.5949447, 2011.
Cismondi, F., Fialho, A. S., Vieira, S. M., Reti, S. R., Sousa, J., and Finkelstein, S. N.: Missing data in medical databases: Impute, delete or classify?, Artif. Intell. Med., 58, 63–72, 2013.
Cressie, N. and Wikle, C. K.: Statistics for spatio-temporal data, John Wiley & Sons, 2011.
Eckner, A.: A framework for the analysis of unevenly spaced time series data, Preprint, available at: http://eckner.com/papers/unevenly_spaced_time_series_analysis.pdf (last access: 20 March 2015), 2012.