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Bias-Reduction in Variational Regularization [PDF]
Journal of Mathematical Imaging and Vision, 2016 The aim of this paper is to introduce and study a two-step debiasing method for variational regularization. After solving the standard variational problem, the key idea is to add a consecutive debiasing step minimizing the data fidelity on an appropriate
Brinkmann, Eva-Maria +3 more
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Bias Reduction News Recommendation System
Digital, 2023 News recommender systems (NRS) are crucial for helping users navigate the vast amount of content available online. However, traditional NRS often suffer from biases that lead to a narrow and unfair distribution of exposure across news items.
Shaina Raza
doaj +2 more sources
A note on bias reduction [PDF]
Comptes Rendus. Mathématique, 2020 Let $\widehat{w}$ be an unbiased estimate of an unknown $w\in R$. Given a function $t(w)$, we show how to choose a function $f_n(w)$ such that for $w^*=\widehat{w} + f_n(w)$, $E\ t\left(w^*\right) =t(w)$.
Withers, Christopher S. +1 more
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Reduction of Nonresponse Bias through Case Prioritization
Survey Research Methods, 2010 How response rates are increased can determine the remaining nonresponse bias in estimates. Studies often target sample members that are most likely to be interviewed to maximize response rates.
Andy Peytchev +4 more
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Measurement Error Bias Reduction in Unemployment Durations [PDF]
Working Paper Series, 2006 The impact of duration response measurement error is investigated by using small variance approximations. The inconsistency of GMM estimators that ignore measurement error is studied for both single spell models with right censoring, and for a two spell ...
Montezuma Dumangane
core +4 more sources
Bias Reduction in Sample-Based Optimization [PDF]
SIAM Journal on Optimization, 2022 We consider stochastic optimization problems which use observed data to estimate essential characteristics of the random quantities involved. Sample average approximation (SAA) or empirical (plug-in) estimation are very popular ways to use data in optimization. It is well known that sample average optimization suffers from downward bias.
Darinka Dentcheva, Yang Lin
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Catalytic bias in oxidation–reduction catalysis
Chemical Communications, 2021 Under steady state conditions, the differential stability of reaction intermediates can alter the rate and the direction of a catalytic process regardless the overall underlying thermodynamic driving force.
David W. Mulder +2 more
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Initializing For Bias Reduction: Some Analytical Considerations [PDF]
1988 Winter Simulation Conference Proceedings, 1988 We are concerned with simulation studies where the simple method of independent replications and classical statistical techniques are used to construct estimates (point and interval) for a steady-state parameter of interest. This paper describes the results of an analytical study on the effectiveness of stochastic initialization, where the initial ...
Joseph R. Murray, W. David Kelton
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Median bias reduction in cumulative link models [PDF]
Communications in Statistics - Simulation and Computation, 2021 This paper presents a novel estimation approach for cumulative link models, based on median bias reduction as developed in Kenne Pagui et al. (2017). The median bias reduced estimator is obtained as solution of an estimating equation based on an adjustment of the score.
V. Gioia, E. C. Kenne Pagui, A. Salvan
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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
openaire +4 more sources