Low-dimensional decomposition , smoothing and forecasting of sparse functional data

@inproceedings{Dokumentov2014LowdimensionalD,
  title={Low-dimensional decomposition , smoothing and forecasting of sparse functional data},
  author={Alexander Dokumentov and Rob J. Hyndman},
  year={2014}
}
We propose a new generic method ROPES (Regularized Optimization for Prediction and Estimation with Sparse data) for decomposing, smoothing and forecasting two-dimensional sparse data. In some ways, ROPES is similar to Ridge Regression, the LASSO, Principal Component Analysis (PCA) and Maximum-Margin Matrix Factorisation (MMMF). Using this new approach, we propose a practical method of forecasting mortality rates, as well as a new method for interpolating and extrapolating sparse longitudinal… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 11 references

demography: Forecasting mortality, fertility, migration and population data

  • R. J. Hyndman
  • R package version 1.16. With contributions from…
  • 2012
1 Excerpt

Sparseness and functional data analysis

  • G. James
  • Ferraty, F. and Romain, Y., editors, The Oxford…
  • 2010
1 Excerpt

The BigChaos solution to the Netflix grand prize

  • A. Töscher, M. Jahrer, R. M. Bell
  • Technical report, Commendo Research & Consulting…
  • 2009
2 Excerpts

University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany)

  • Human Mortality Database
  • Data downloaded on 20 Feb 2008. http: //www…
  • 2008
2 Excerpts

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