Results 111 to 120 of about 16,825 (201)

On nonnegative unbiased estimators

, 2015
We study the existence of algorithms generating almost surely nonnegative unbiased estimators. We show that given a nonconstant real-valued function $f$ and a sequence of unbiased estimators of $\lambda\in\mathbb{R}$, there is no algorithm yielding ...
Jacob, Pierre E., Thiery, Alexandre H.
core   +1 more source

“Within‐Trial” Prognostic Score Adjustment Is Targeted Maximum Likelihood Estimation

Pharmaceutical Statistics, Volume 25, Issue 2, March/April 2026.
ABSTRACT Adjustment for “super” or “prognostic” composite covariates has become more popular in randomized trials recently. These prognostic covariates are often constructed from historical data obtained from previous clinical trials or registries by fitting a predictive model of the outcome on the raw covariates.
Emilie Højbjerre‐Frandsen, Alejandro Schuler   +1 more
wiley   +1 more source

Debiased LASSO under Poisson–Gauss model

Information and Inference: A Journal of the IMA
Abstract Quantifying uncertainty in high-dimensional sparse linear regression is a fundamental task in statistics that arises in various applications. One of the most successful methods for quantifying uncertainty is the debiased LASSO, which has a solid theoretical foundation but is restricted to settings where the noise is purely ...
Abdalla, Pedro, Kur, Gil
openaire   +3 more sources

Conservatives, Progressives and Transformers: The Influence of Marketers' Biases on Sustainable Innovation

Creativity and Innovation Management, Volume 35, Issue 1, Page 251-276, March 2026.
ABSTRACT The inherent uncertainty of the innovation process, amplified by the complexity of the Anthropocene, means that marketers are likely to be subject to decision‐making biases that can affect sustainable product innovation. In parallel, new approaches to sustainability and innovation management are emerging, aiming to mitigate such biases and ...
Sophie Richit, Aurélie Hemonnet‐Goujot   +1 more
wiley   +1 more source

Parameter Estimation in Comparative Judgment Under Random and Adaptive Scheduling Schemes

Journal of Educational Measurement, Volume 63, Issue 1, Spring 2026.
Abstract Comparative judgment is an assessment method where item ratings are estimated based on rankings of subsets of the items. These rankings are typically pairwise, with ratings taken to be the estimated parameters from fitting a Bradley‐Terry model. Likelihood penalization is often employed to ensure finiteness of estimates. Adaptive scheduling of
Ian Hamilton, Nick Tawn
wiley   +1 more source

Debiasing Education Algorithms

International Journal of Artificial Intelligence in Education
AbstractThis systematic literature review investigates the fairness of machine learning algorithms in educational settings, focusing on recent studies and their proposed solutions to address biases. Applications analyzed include student dropout prediction, performance prediction, forum post classification, and recommender systems.
openaire   +1 more source

Artificial intelligence and judicial decision-making: Evaluating the role of AI in debiasing

TATuP – Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis
As arbiters of law and fact, judges are supposed to decide cases impartially, basing their decisions on authoritative legal sources and not being influenced by irrelevant factors.
Giovana Lopes
doaj   +1 more source

Shrouded Attributes, Consumer Myopia, and Information Suppression in Competitive Markets [PDF]

Bayesian consumers infer that hidden add-on prices (e.g. the cost of ink for a printer) are likely to be high prices. If consumers are Bayesian, firms will not shroud information in equilibrium. However, shrouding may occur in an economy with some myopic
David Laibson, Xavier Gabaix
core  

No Free Lunch in Language Model Bias Mitigation? Targeted Bias Reduction Can Exacerbate Unmitigated LLM Biases

AI
Large Language Models (LLMs) inherit societal biases from their training data, potentially leading to harmful outputs. While various techniques aim to mitigate these biases, their effects are typically evaluated only along the targeted dimension, leaving
Shireen Chand, Faith Baca, Emilio Ferrara   +2 more
doaj   +1 more source

A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics

Journal of Advances in Modeling Earth Systems
Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully‐resolved climate simulations remain computationally intractable, policy makers must rely on coarse‐models to ...
Benedikt Barthel Sorensen, A. Charalampopoulos, S. Zhang, B. E. Harrop, L. R. Leung, T. P. Sapsis   +5 more
doaj   +1 more source