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Results 1 - 10
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5,632
Bias-Corrected Kernel Regression
"... . This paper proposes a simple and practical iterative method for bias-corrected kernel regression. The proposed approach corrects for both curvature-based and boundary-based finite-sample bias. The method is proposed as an alternative to bias-reduction through the estimation of leading terms in a ..."
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Cited by 1 (0 self)
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. This paper proposes a simple and practical iterative method for bias-corrected kernel regression. The proposed approach corrects for both curvature-based and boundary-based finite-sample bias. The method is proposed as an alternative to bias-reduction through the estimation of leading terms
Bias-Corrected Estimation for Spatial
, 2009
"... Maximum likelihood (ML) or quasi-maximum likelihood (QML) estimator of the spatial parameter in the spatial autoregressive model can be very biased. The biasness depends heavily on the magnitude of the error standard deviation and on the spatial layout. Second-order approximations to the bias and MS ..."
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and MSE are given by modifying the results of Bao and Ullah (2007a). A bootstrap procedure is introduced for the practical implementation of the bias-correction. Monte Carlo simulation shows that this bootstrap procedure works well and that the bias-corrected ML or QML estimator outperforms the regular ML
Bias Corrected ROC . . .
, 2007
"... The.632 error estimator is a bias correction of the bootstrap estimator which leads to an underestimation of the error when the apparent error is zero. As a consequence Efron and Tibshirani (1997) developed the.632+ bootstrap error as a modification that can handle this case. We demonstrate proper ..."
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The.632 error estimator is a bias correction of the bootstrap estimator which leads to an underestimation of the error when the apparent error is zero. As a consequence Efron and Tibshirani (1997) developed the.632+ bootstrap error as a modification that can handle this case. We demonstrate
Approximate Bias Correction in Econometrics
, 1997
"... this paper can still be used, but they all yield estimates that are biased at O(n ..."
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Cited by 60 (11 self)
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this paper can still be used, but they all yield estimates that are biased at O(n
Multiplicative Bias Corrected
"... The paper presents a multiplicative bias reduction estimator for nonparametric regression. The approach consists to apply a multi-plicative bias correction to an oversmooth pilot estimator. In Burr et al. [2010], this method has been tested to estimate energy spectra. For such data set, it was obser ..."
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The paper presents a multiplicative bias reduction estimator for nonparametric regression. The approach consists to apply a multi-plicative bias correction to an oversmooth pilot estimator. In Burr et al. [2010], this method has been tested to estimate energy spectra. For such data set
ENSEMBLE FORECAST BIAS CORRECTION
"... This study investigates two bias correction methods, lagged average and lagged linear regression, for individual members of ensemble forecasts. Both methods use the forecast bias from previous forecasts to predict the bias of the current forecast at every station. Also considered is the training per ..."
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This study investigates two bias correction methods, lagged average and lagged linear regression, for individual members of ensemble forecasts. Both methods use the forecast bias from previous forecasts to predict the bias of the current forecast at every station. Also considered is the training
Sample Selection Bias Correction Theory
"... Abstract. This paper presents a theoretical analysis of sample selection bias correction. The sample bias correction technique commonly used in machine learning consists of reweighting the cost of an error on each training point of a biased sample to more closely reflect the unbiased distribution. T ..."
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Cited by 27 (3 self)
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Abstract. This paper presents a theoretical analysis of sample selection bias correction. The sample bias correction technique commonly used in machine learning consists of reweighting the cost of an error on each training point of a biased sample to more closely reflect the unbiased distribution
Higher Order Efficiency of Bias Corrections
, 2004
"... The purpose of this paper is to show that the choice of bias correction method does not affect the higher order efficiency of first-order efficient estimators. We show this result by a formal expansion and verify it by calculations for the bootstrap, jackknife, and sample moment based bias correctio ..."
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Cited by 1 (1 self)
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The purpose of this paper is to show that the choice of bias correction method does not affect the higher order efficiency of first-order efficient estimators. We show this result by a formal expansion and verify it by calculations for the bootstrap, jackknife, and sample moment based bias
Bias-corrected estimation of . . .
, 2010
"... The biasedness issue arising from the maximum likelihood estimation of the spatial autoregressive model (SAR) is further investigated under a broader set-up than that in Bao and Ullah (2007a). A major difficulty in analytically evaluating the expectations of ratios of quadratic forms is overcome by ..."
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by a simple bootstrap procedure. With that, the corrections on bias and variance of the spatial estimator can easily be made up to third-order, and once this is done, the estimators of other model parameters become nearly unbiased. Compared with the analytical approach, the new approach is much simpler
Developments in Bias Correction for Reanalysis
"... A major problem with the use of observations for climate analysis is the presence of biases, and the effect on the estimation of climate signals of changes in these biases, their sampling frequencies, and details of the analysis techniques. This problem also exists in atmospheric reanalyses, which c ..."
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A major problem with the use of observations for climate analysis is the presence of biases, and the effect on the estimation of climate signals of changes in these biases, their sampling frequencies, and details of the analysis techniques. This problem also exists in atmospheric reanalyses, which
Results 1 - 10
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