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Bias reduction with Variable Percent Bias Reducing matching
Statistics & Probability Letters, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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'Bias reduction of maximum likelihood estimates'
Biometrika, 1993Summary: 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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On Bias Reduction in Estimation
Journal of the American Statistical Association, 1971Abstract 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 reduction for high quantiles
Journal of Statistical Planning and Inference, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Deyuan, Peng, Liang, Yang, Jingping
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Bias reduction for jackknife skewness
Communications in Statistics - Theory and Methods, 1994It has been found in simulation studies(Beran, 1984; Tu and Zhang, 1992a) that the jackknife skewness estimators have large downward biases. In this paper a method to reduce the biases of negative jackknife skewness estimators is suggested. The performance of the bias reduced estimators is assessed by Monte Carlo simulations for both small and moderate
D. Tu, A.J. Gross
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Dataset Reduction via Bias-Variance Minimization
2021 5th Scientific School Dynamics of Complex Networks and their Applications (DCNA), 2021The amount of generated, collected and labelled data rapidly increases nowadays, which raises the question of creating methods for extracting dataset subsets, learning on which a model can achieve the same generalization level as when learning on the whole available set of data.
Georgii Novikov +2 more
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Bias reduction in parameter estimation
Automatica, 1988The upper bound of the bias in the estimated parameters is derived in terms of the spectral norm. It is then established that the bias is related to the condition of the problem as well as the noise existing in the data. Conventional methods such as the generalized least squares (GLDS), the instrumental variables (IV) and the extended matrix (EM ...
Le, Loc X., Wilson, W. J.
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Array Interpolation and Bias Reduction
IEEE Transactions on Signal Processing, 2004Interpolation (mapping) of data from a given antenna array onto the output of a virtual array of more suitable configuration is well known in array signal processing. This operation allows arrays of any geometry to be used with fast direction-of-arrival (DOA) estimators designed for linear arrays. Conditions for preserving DOA error variance under such
P. Hyberg, M. Jansson, B. Ottersten
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JACKKNIFE BIAS REDUCTION FOR POLYCHOTOMOUS LOGISTIC REGRESSION
Statistics in Medicine, 1997Despite theoretical and empirical evidence that the usual MLEs can be misleading in finite samples and some evidence that bias reduced estimates are less biased and more efficient, they have not seen a wide application in practice. One can obtain bias reduced estimates by jackknife methods, with or without full iteration, or by use of higher order ...
S B, Bull, C M, Greenwood, W W, Hauck
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Bias reduction in autoregressive models
Economics Letters, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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