Bias of an estimator - Open Access .click

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Journal of the American Statistical Association, 1971
Abstract A general procedure for reducing the bias of point estimators is introduced. The technique includes the “jackknife” as a special case. The existing notion of reapplication is shown to lack a desirable bias removal property for which it was originally designed.
W. R. Schucany, H. L. Gray, D. B. Owen
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Bias in Engineering Estimation

Journal of Petroleum Technology, 1982
Summary This paper presents 5-year-operation results of successful pressure maintenance by formation water dumping into a partially depleted oil reservoir of limestone rock. Water is being dumped into light 33 deg. API(0.86-g/c3)oil in the reservoir periphery.
Randal M. Brush, Sullivan S. Marsden
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Bias in seroprevalence estimates

2023
Systematic review of bias in estimates of COVID-19 seroprevalence in the medical literature, due to not accounting for sensitivity and specificity of the diagnostic test.
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Double ratio estimation within a design-based nonresponse bias mitigation strategy

Forestry: An International Journal of Forest Research
In the national forest inventory of the USA, there is an ongoing issue with sample plots that are either completely or partially unmeasured due to access issues such as hazardous conditions or lack of permission. The nonrandom nature of the nonresponse
James A Westfall, Paul L Patterson
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On a Method of Removing Bias of Estimates

Theory of Probability & Its Applications, 1988
Let \(t_ n\) be the estimator of the parameter \(\theta\) such that the following asymptotic expansion takes place \[ E_{\theta}t_ n=\theta +a_ 1(\theta)/b(n)+a_ 2(\theta)/b^ 2(n)+... \] Here \(a_ i(.)\), \(i=1,2,..\). are unknown, and b(n)\(\to \infty\) as \(n\to \infty\).
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Bias of Nearest Neighbor Error Estimates

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987
The bias of the finite-sample nearest neighbor (NN) error from its asymptotic value is examined. Expressions are obtained which relate the bias of the NN and 2-NN errors to sample size, dimensionality, metric, and distributions. These expressions isolate the effect of sample size from that of the distributions, giving an explicit relation showing how ...
Fukunaga, Keinosuke, Hummels, Donald M.
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'Bias reduction of maximum likelihood estimates'

Biometrika, 1993
Summary: It is shown how, in regular parametric problems, the first-order term is removed from the asymptotic bias of maximum likelihood estimates by a suitable modification of the score function. In exponential families with canonical parameterization the effect is to penalize the likelihood by the Jeffreys invariant prior. In binomial logistic models,
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A note on the bias of L-estimators and a bias reduction procedure

Statistics & Probability Letters, 1995
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Random effects estimation in a fractional diffusion model based on continuous observations

Statistical Inference for Stochastic Processes : An International Journal devoted to Time Series Analysis and the Statistics of Continuous Time Processes and Dynamical Systems
The purpose of the present work is to construct estimators for the random effects in a fractional diffusion model using a hybrid estimation method where we combine parametric and nonparametric techniques.
Nesrine Chebli, Hamdi Fathallah, Yousri Slaoui +2 more
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Euclid preparation. LXV. Determining the weak lensing mass accuracy and precision for galaxy clusters

Astronomy & Astrophysics
The ability to measure unbiased weak-lensing (WL) masses is a key ingredient to exploit galaxy clusters as a competitive cosmological probe with the ESA survey or future missions.
Euclid Collaboration L. Ingoglia +499 more
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