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Optimal Recursive Estimation of Raw Data

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Abstract

We present a new approach to the optimal estimation of random vectors. The approach is based on a combination of a specific iterative procedure and the solution of a best approximation problem with a polynomial approximant. We show that the combination of these new techniques allow us to build a computationally effective and flexible estimator. The strict justification of the proposed technique is provided.

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Author information

Authors and Affiliations

  1. Centre for Industrial and Applicable Mathematics, University of South Australia, SA, 5095, Australia

    Anatoli Torokhti & Phil Howlett

  2. Applied Mathematics Department, University of Adelaide, SA, 5001, Australia

    Charles Pearce

Authors
  1. Anatoli Torokhti
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  2. Phil Howlett
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  3. Charles Pearce
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Corresponding author

Correspondence to Anatoli Torokhti.

Additional information

This work was supported by the Australian Research Council under the ARC Large Grant Scheme.

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Torokhti, A., Howlett, P. & Pearce, C. Optimal Recursive Estimation of Raw Data. Ann Oper Res 133, 285–302 (2005). https://doi.org/10.1007/s10479-004-5039-5

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  • DOI: https://doi.org/10.1007/s10479-004-5039-5

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