Low-dimensional decomposition, smoothing and forecasting of sparse functional data
Alexander Dokumentov () and
Rob Hyndman ()
No 16/14, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
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 data. We also show how to calculate prediction intervals for the resulting estimates.
Keywords: Tikhonov regularisation; Smoothing; Forecasting; Ridge regression; PCA; LASSO; Maximum-margin matrix factorisation; Mortality rates; Sparse longitudinal data (search for similar items in EconPapers)
JEL-codes: C10 C14 C33 (search for similar items in EconPapers)
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