final_program_abstracts[1]

11 IMSC Session Program
Variance-preserving, data-adaptive regularization schemes in
RegEM. application to ENSO reconstructions
Tuesday - Parallel Session 3
Julien Emile-Geay 1 , Tapio Schneider 2 , Diana Sima 3 and Kim Cobb 4
1
University of Southern California, USA
2 California Institute of Technology, USA
3 Katholieke Universiteit Leuven, Belgium
4 Georgia Institute of Technology, USA
The Regularized Expectation-Maximization algorithm (RegEM) has been abundantly
used in recent years to estimate past climate variability from proxy data (e.g. Mann et
al [2005,2007a, 2008, 2009], Rutherford et al., [2005, 2007]; Emile-Geay et al,
2010). Paleoclimate problems offer three important challenges to this particular
imputation method:
(a) the amount of missing values is very large, typically > 50%
(b) proxy networks are noisy, so only few modes (