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Regarding variance estimation, Osier (2006, 2009) proposed a variance estimator based on linearization techniques.
Variance estimation for complex statistics and estimators: linearization and residual techniques.
Standard errors were estimated using robust cluster variance estimation.
Regression parameters were then estimated using GEE with robust variance estimation.
Variances and confidence intervals on estimates were produced using the ultimate cluster variance estimation technique [ 19].
The classical sandwich estimator (Diggle et al., 2002b) in variance estimation for fixed-effects was applied.
A bootstrap variance estimation procedure was used for statistical analysis of the weighted estimates.
The short-term window is used for variance estimation.
Figure 7 Example of variance estimation on a synthetic signal.
Therefore, the bound for the variance estimation cannot be found.
EM for data detection and noise variance estimation: In TnDFnD channels, an EM-based joint data detection and noise variance estimation algorithm is proposed in [20].
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