Results 11 to 20 of about 286,227 (342)
Bias in estimators of archaic admixture [PDF]
Theoretical Population Biology, 2015 This article evaluates bias in one class of methods used to estimate archaic admixture in modern humans. These methods study the pattern of allele sharing among modern and archaic genomes. They are sensitive to "ghost" admixture, which occurs when a population receives archaic DNA from sources not acknowledged by the statistical model.
Alan R. Rogers, Ryan J. Bohlender
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Bias in Zipf’s law estimators [PDF]
Scientific Reports, 2021 AbstractThe prevailing maximum likelihood estimators for inferring power law models from rank-frequency data are biased. The source of this bias is an inappropriate likelihood function. The correct likelihood function is derived and shown to be computationally intractable. A more computationally efficient method of approximate Bayesian computation (ABC)
Charlie Pilgrim +2 more
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A comparison of bias approximations for the 2SLS estimator [PDF]
SSRN Electronic Journal, 2010 We consider the bias of the 2SLS estimator in the linear instrumental variables regression with one endogenous regressor only. By using asymptotic expansion techniques we approximate 2SLS coefficient estimation bias under various scenarios regarding the number and strength of instruments.
Maurice J. G. Bun, Frank Windmeijer
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Bias Estimation in Sensor Networks [PDF]
IEEE Transactions on Control of Network Systems, 2020 12 pages, 8 ...
Mingming Shi +3 more
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Prewhitening Bias in HAC Estimation* [PDF]
Oxford Bulletin of Economics and Statistics, 2005 AbstractHeteroskedasticity and autocorrelation consistent (HAC) estimation commonly involves the use of prewhitening filters based on simple autoregressive models. In such applications, small sample bias in the estimation of autoregressive coefficients is transmitted to the recolouring filter, leading to HAC variance estimates that can be badly biased.
Donggyu Sul +2 more
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Bias Reduction for Sum Estimation [PDF]
, 2022 In classical statistics and distribution testing, it is often assumed that elements can be sampled from some distribution $P$, and that when an element $x$ is sampled, the probability $P$ of sampling $x$ is also known. Recent work in distribution testing has shown that many algorithms are robust in the sense that they still produce correct output if ...
Eden, Talya +4 more
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Direct Reduction of Bias of the Classical Hill Estimator
Revstat Statistical Journal, 2005 In this paper we are interested in an adequate estimation of the dominant component of the bias of Hill’s estimator of a positive tail index γ, in order to remove it from the classical Hill estimator in different asymptotically equivalent ways.
Frederico Caeiro +2 more
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Bias Robust Estimation of Scale [PDF]
The Annals of Statistics, 1991 In this paper we consider the problem of robust estimation of the scale of the location residuals when the underlying distribution of the data belongs to a contamination neighborhood of a parametric location-scale family. We define the class of $M$-estimates of scale with general location, and show that under certain regularity assumptions, these scale
Martin, R. D., Zamar, Ruben H.
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Revstat Statistical Journal, 2011 Sousa and Michailidis (2004) developed the sum plot based on the Hill (1975) estimator as a diagnostic tool for selecting the optimal k when the distribution is heavy tailed.
J. Beirlant , E. Boniphace , G. Dierckx
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A new method of joint nonparametric estimation of probability density and its support
, 2017 In this paper we propose a new method of joint nonparametric estimation of probability density and its support. As is well known, nonparametric kernel density estimator has "boundary bias problem" when the support of the population density is not the ...
Brown, André EX (5398142) +11 more
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