LAUSR

Estimation bias and bias correction in reduced rank autoregressions

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0 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… read more here.

Keywords: reduced rank; bias bias; correction reduced; bias correction ... See more keywords

Stochastic Double Deep Q-Network

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1 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… read more here.

Keywords: estimation bias; double estimator; stochastic double; double deep ... See more keywords

Magnitude of the Distance Estimation Bias in Received Signal Strength Visible Light Positioning

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1 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… read more here.

Keywords: light positioning; magnitude distance; distance; visible light ... See more keywords