Although the shrinkage estimators are biased, they have smaller variances than do the community-specific estimators, thus providing more stable estimates of risk over time.
Consequently, it is of interest to consider approximation methods that may lead to more stable covariance matrix estimators.
The AIPW estimator is more efficient and more stable than the IPW estimator.
Nevertheless, unlike the Schwarz-like regularization, the group Lasso one is continuous which renders the resulting estimator more stable when applied to real data; see also [10, 22].
Through Monte Carlo simulation we show that both variance estimators are approximately unbiased, but that the new estimator is more stable (i.e., has lower empirical variance) and provides empirical confidence interval coverage rates that coincide more closely with the nominal coverage rates.
From this analysis, we can conclude that a transformation can be useful when the shape of the tail of the distribution of permutation values is transformed from light-tailed (k>0) to heavy-tailed (k<0), where our estimator has more stable and accurate performance.
We observe that the trajectories of RL-estimator are much more stable than those of standard random walk based estimation procedures, and its error performance is comparable to that of respondent-driven sampling (RDS) which has a smaller asymptotic variance than many other estimators.
The results showed the precision of estimators tended to be more stable in the proposed method than the Pocock Simon method.
We prove that the linear shrinkage estimator is consistent and asymptotically optimal under the expected quadratic loss, and will have more stable numerical performance than the sample covariance matrix.
