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Descripción
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| In many practical scenarios, including those dealing with large data sets, calculating global estimators of unknown variables of interest becomes unfeasible. A common solution is obtaining partial estimators and combining them to approximate the global one. In this paper, we focus on minimum mean squared error (MMSE) estimators, introducing two efficient linear schemes for the fusion of partial estimators. The proposed approaches are valid for any type of partial estimators, although in the simulated scenarios we concentrate on the combination of Monte Carlo estimators due to the nature of the problem addressed. Numerical results show the good performance of the novel fusion methods with only a fraction of the cost of the asymptotically optimal solution. | |
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Internacional
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Si |
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Nombre congreso
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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
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Tipo de participación
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960 |
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Lugar del congreso
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Brisbane (Australia) |
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Revisores
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Si |
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ISBN o ISSN
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978-1-4673-6997-8 |
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DOI
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Fecha inicio congreso
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19/04/2015 |
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Fecha fin congreso
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24/04/2015 |
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Desde la página
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4100 |
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Hasta la página
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4104 |
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Título de las actas
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Proceedings of the 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |