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Are analysts' loss functions asymmetric? [PDF]
Journal of Forecasting, 2011 Despite displaying a statistically significant optimism bias, analysts' earnings forecasts are an important input to investors’ valuation models. Understanding the possible reasons for any bias is important if information is to be extracted from earnings
Clatworthy, Mark A +10 more
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Loss Functions for Finite Sets
Computational Optimization and Applications, 2022 This paper studies loss functions for finite sets. For a given finite set $S$, we give sum-of-square type loss functions of minimum degree. When $S$ is the vertex set of a standard simplex, we show such loss functions have no spurious minimizers (i.e ...
Zhong, Suhan, Nie, Jiawang
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Are analysts' loss functions asymmetric? [PDF]
, 2005 Recent research by Gu and Wu (2003) and Basu and Markov (2004) suggests that the well-known optimism bias in analysts’ earnings forecasts is attributable to analysts minimizing symmetric, linear loss functions when the distribution of forecast errors is ...
Mark A. Clatworthy +5 more
core +4 more sources
A Case for Soft Loss Functions [PDF]
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2020 Recently, Peterson et al. provided evidence of the benefits of using probabilistic soft labels generated from crowd annotations for training a computer vision model, showing that using such labels maximizes performance of the models over unseen data. In this paper, we generalize these results by showing that training with soft labels is an effective ...
Uma, Alexandra +5 more
openaire +3 more sources
Neurologic Clinics, 2010 Neurologists frequently evaluate patients complaining of vision loss, especially when the patient has been examined by an ophthalmologist who has found no ocular disease. A significant proportion of patients presenting to the neurologist with visual complaints have nonorganic or functional visual loss. Although there are examination techniques that can
Beau B, Bruce, Nancy J, Newman
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Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Training deep neural networks is inherently subject to the predefined and fixed loss functions during optimizing. To improve learning efficiency, we develop Stochastic Loss Function (SLF) to dynamically and automatically generating appropriate gradients to train deep networks in the same round of back-propagation, while maintaining the completeness and
Qingliang Liu 0002, Jinmei Lai
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Frontiers in Water, 2022 Environmental changes are predicted to exacerbate changes in flood events, resulting in consequences for exposed systems. While the availability and quality of flood risk analyses are generally increasing, very little attention has been paid to flood ...
Jeremy R. Porter +8 more
doaj +1 more source
When loss-of-function is loss of function: assessing mutational signatures and impact of loss-of-function genetic variants [PDF]
Bioinformatics, 2017 Abstract Motivation Loss-of-function genetic variants are frequently associated with severe clinical phenotypes, yet many are present in the genomes of healthy individuals. The available methods to assess the impact of these variants rely primarily upon evolutionary conservation with little to no ...
Kymberleigh A. Pagel +9 more
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Mathematics, 2022 This article aims to consider estimating the unknown parameters, survival, and hazard functions of the beta inverted exponential distribution. Two methods of estimation were used based on type-II censored samples: maximum likelihood and Bayes estimators.
Maha A. Aldahlan +2 more
doaj +1 more source
Are Loss Functions All the Same? [PDF]
Neural Computation, 2004 In this letter, we investigate the impact of choosing different loss functions from the viewpoint of statistical learning theory. We introduce a convexity assumption, which is met by all loss functions commonly used in the literature, and study how the bound on the estimation error changes with the loss.
ROSASCO, LORENZO +4 more
openaire +4 more sources