Missing data recovery via a nonparametric iterative adaptive approach

We introduce a missing data recovery methodology based on a weighted least squares iterative adaptive approach (IAA). The proposed method is referred to as the missing-data IAA (MIAA) and it can be used for uniform or non-uniform sampling as well as for arbitrary data missing patterns. MIAA uses the IAA spectrum estimates to retrieve the missing data, based on a spectral least squares criterion similar to that used by IAA. Numerical examples are presented to show the effectiveness of MIAA for missing data recovery. We also show that MIAA can outperform an existing competitive approach, and this at a much lower computational cost.