Abstract / Description of output
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.
Original language | English |
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Title of host publication | 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 4100-4104 |
Number of pages | 5 |
Volume | 2015-August |
ISBN (Electronic) | 9781467369978 |
DOIs | |
Publication status | Published - 1 Jan 2015 |
Event | 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia Duration: 19 Apr 2014 → 24 Apr 2014 |
Conference
Conference | 40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 |
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Country/Territory | Australia |
City | Brisbane |
Period | 19/04/14 → 24/04/14 |
Keywords / Materials (for Non-textual outputs)
- fusion
- Global estimator
- linear combination
- Monte Carlo estimation
- partial estimator