NOTES ON BIAS IN ESTIMATION

By M. H. Quenouille. 1956

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Citations in the corpus

DeltaGrad: Rapid retraining of machine learning models (2020, methodology): “Constructing models with some datapoints removed can also be used for constructing bias-corrected models, such as in jackknife resampling (Quenouille, 1956) which requires retraining the model on all leave-one-out datasets.”
Explosive Behavior in the 1990s Nasdaq: When Did Exuberance Escalate Asset Values? (2011, methodology): “Several statistical procedures are available for doing so, including the use of asymptotic expansion formulae (Kendall, 1954), jackknifing (Quenouille, 1956; and Efron, 1982), median unbiased estimation (Andrews, 1993) and indirect inference (Gouriéroux et al.”
Precision of Warm’s Weighted Likelihood Estimates for a Polytomous Model in Computerized Adaptive Testing (2001, methodology): “The first includes computationally intensive procedures, such as the jackknife and bootstrap methods (Quenouille, 1949, 1956).”
Estimation of finite population mean using known correlation coefficient between auxiliary characters (2005, methodology): “An almost unbiased ratio-cum-product estimator has also been obtained by using Jackknife technique envisaged by Quenouille (1956).”
Jackknifing Bond Option Prices (2005, unknown): “Quenouille (1956) proposed the jackknife as a solution to finite sample bias in parametric estimation problems.”
Jackknife estimation of stationary autoregressive models (2013, unknown): “The jackknife method of bias reduction was originally proposed by Quenouille (1956) with Tukey (1958) subsequently demonstrating how the method could also be used to construct a nonparameteric estimator of variance.”
Characterization of uncertainty in probabilistic model using bootstrap method and its application to reliability of piles (2015, methodology): “In statistics, the resampling techniques such as jackknifing [24] and bootstrapping [25] are often used to derive the sampling distributions of statistics based on the measured data.”
A Toolbox for Nonlinear Regression in R: The Package nlstools (2015, methodology): “By using a leave-one-out procedure, it produces jackknife parameter estimates together with conﬁdence intervals [Quenouille, 1956, Fox et al., 1980, Seber and Wild, 1989].”
ON THE LIU AND ALMOST UNBIASED LIU ESTIMATORS IN THE PRESENCE OF MULTICOLLINEARITY WITH HETEROSCEDASTIC OR CORRELATED ERRORS (2009, methodology): “Finally, a simulation study has been conducted which indicated that under certain conditions on d, the proposed estimator performed well compared to GLS, ORR and LE estimators.”
Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance (2007, methodology): “Based on the CLT ( 64 ), 2 can be estimated in the first stage by running a (nonlinear) least squares regression of the standardized realized volatility”
TropFishR: an R package for fisheries analysis with length‐frequency data (2017, methodology): “…that an R-based implementation affords, the confidence intervals for the growth parameters can be easily calculated with the jack knife technique – a resamplingmethod with replacement (Quenouille 1956; Tukey , 1958, 1986), – by incorporating ELEFAN_SA in a for-loop (supplementary onlinematerial).”