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Published in 2019 at "Econometric Reviews"
DOI: 10.1080/07474938.2017.1308065
Abstract: ABSTRACT This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias…
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Keywords:
reduced rank;
bias bias;
correction reduced;
bias correction ... See more keywords
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Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2922706
Abstract: Estimation bias seriously affects the performance of reinforcement learning algorithms. The maximum operation may result in overestimation, while the double estimator operation often leads to underestimation. To eliminate the estimation bias, these two operations are…
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Keywords:
estimation bias;
double estimator;
stochastic double;
double deep ... See more keywords
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Published in 2018 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2018.2867026
Abstract: In several works, statistical precision of distance estimators in visible light positioning was studied by evaluating the Cramér–Rao lower bound, assuming that the estimator is unbiased. Here, it is demonstrated that the maximum likelihood estimator…
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Keywords:
light positioning;
magnitude distance;
distance;
visible light ... See more keywords