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Transformations and the Jackknife
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1981 SUMMARY Whether to jackknife a parameter estimate or some function of it has in the past relied on recommendations based on folklore rather than statistical analyses. This paper puts the jackknifed estimator into the context of finding a least squares estimator from a model involving a location parameter with additive “pseudo” errors ...
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Scientific African, 2022 The parameters in the Poisson regression model are usually estimated using the maximum likelihood estimator (MLE). MLE suffers a breakdown when there is either multicollinearity or outliers in the Poisson regression model.
Kingsley C Arum +2 more
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Comparing the Thiessen’s Method against simpler alternatives using Monte Carlo Simulation
Revista Cartográfica, 2018 Estimating the expected value of a function over geographic areas is problem with a long history. In the beginning of the XX-th century the most common method was just the arithmetic mean of the field measurements ignoring data location.
Marcelo Guelfi, Carlos López-Vazquez
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Estimation of confidence intervals for quantiles in a finite population
Mathematical Modelling and Analysis, 2008 Confidence intervals provide a way of reporting an estimate of a population quantile along with some information about the precision of estimates. Some procedures that may be used to obtain estimates of confidence intervals for quantiles in a finite ...
Viktoras Chadyšas
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MathematicsHigh-dimensional parameter testing is commonly used in bioinformatics to analyze complex relationships in gene expression and brain connectivity studies, involving parameters like means, covariances, and correlations.
Mingjuan Zhang, Libin Jin
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Bootstrap, jackknife and Edgeworth approximations for finite population L-statistics
Lietuvos Matematikos Rinkinys, 2010 In this paper we give exact bootstrap estimators for the parameters defining one-term Edgeworth expansion of distribution function of finite population L-statistic and compare these estimators with corresponding jackknife estimators. We also compare `````
Andrius Čiginas
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The Annals of Statistics, 1974 It is proved that the jackknife estimate $\tilde{\theta} = n\hat{\theta} - (n - 1)(\sum \hat{\theta}_{-i}/n)$ of a function $\theta = f(\beta)$ of the regression parameters in a general linear model $\mathbf{Y} = \mathbf{X\beta} + \mathbf{e}$ is asymptotically normally distributed under conditions that do not require $\mathbf{e}$ to be normally ...
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BarekengPath analysis tests the relationship between variables through cause and effect. The assumption of linearity must be met before conducting further tests on path analysis. If the shape of the relationship is nonlinear and the shape of the curve is unknown,
Devi Veda Amanda +3 more
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The consistency of bootstrap and jackknife variance estimators for finite population L-statistics
Lietuvos Matematikos Rinkinys, 2012 For the linear combinations of order statistics (L-statistics), we present conditions sufficient for the consistency of their finite-population bootstrap variance estimator and the classical jackknife variance estimator.
Andrius Čiginas
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The Effect of Sideslip on Jackknife Limits during Low Speed Trailer Operation
Robotics, 2022 Jackknifing refers to the serious situation where a vehicle-trailer system enters a jackknife state and the vehicle and trailer eventually collide if trailer operation is not corrected.
Zhe Leng +3 more
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