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At the transmitter, the use of several antennas lead us to modify our SISO channel estimator to use orthogonal pilots that constitute matrices called Orthogonal Space-Time PilOSTPMtrices (OSTPM).
Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open.
The choice of number of bins, bin boundaries and which estimator to use are all the subject of intensive research.
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As a robust estimator, we propose to use a minimum density power divergence estimator (MDPDE) designed by Basu et al.
This would be a great time to use estimators such as a DebtBlaster Calculator to come up with a game plan to pay off remaining debts before you retire.
We therefore believe that it is important to use estimators that depend on as few assumptions as possible, such as the ML estimator we have studied here.
Instead it is desirable to use estimators of the mean with minimal bias.
Explain why this estimator would be very difficult to use in practice.
Here, it was necessary to use robust estimators for means and standard deviations (Minimum volume ellipsoid, [14]).
Options include a weighted estimator (preferably a partitioned estimator, to make use of all the data efficiently) or a phase-based approach.
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