Results 21 to 30 of about 292,252 (254)

Linking Pareto-Tail Kernel Goodness-Offit Statistics with Tail Index at Optimal Threshold and Second Order Estimation

open access: yesRevstat Statistical Journal, 2008
In this paper the relation between goodness-of-fit testing and the optimal selection of the sample fraction for tail estimation, for instance using Hill’s estimator, is examined.
Yuri Goegebeur   +2 more
doaj   +1 more source

Optimal $δ$-Correct Best-Arm Selection for Heavy-Tailed Distributions

open access: yes, 2019
Given a finite set of unknown distributions or arms that can be sampled, we consider the problem of identifying the one with the maximum mean using a $δ$-correct algorithm (an adaptive, sequential algorithm that restricts the probability of error to a specified $δ$) that has minimum sample complexity.
Agrawal, Shubhada   +2 more
openaire   +3 more sources

A deep neural network based model for the prediction of hybrid electric vehicles carbon dioxide emissions

open access: yesEnergy and AI, 2021
Hybrid electric vehicles (HEV) are nowadays proving to be one of the most promising technologies for the improvement of the fuel economy of several transportation segments.
Claudio Maino   +3 more
doaj   +1 more source

Problem-driven scenario generation: an analytical approach for stochastic programs with tail risk measure [PDF]

open access: yes, 2019
Scenario generation is the construction of a discrete random vector to represent parameters of uncertain values in a stochastic program. Most approaches to scenario generation are distribution-driven, that is, they attempt to construct a random vector ...
Fairbrother, Jamie   +2 more
core   +2 more sources

A sequential Monte Carlo approach to computing tail probabilities in stochastic models [PDF]

open access: yes, 2011
Sequential Monte Carlo methods which involve sequential importance sampling and resampling are shown to provide a versatile approach to computing probabilities of rare events.
Chan, Hock Peng, Lai, Tze Leung
core   +1 more source

Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees

open access: yes, 2020
Algorithms typically come with tunable parameters that have a considerable impact on the computational resources they consume. Too often, practitioners must hand-tune the parameters, a tedious and error-prone task.
Balcan, Maria-Florina   +2 more
core   +1 more source

Oligometastatic head and neck cancer: Which patients benefit from radical local treatment of all tumour sites?

open access: yesRadiation Oncology, 2021
Background There is a large lack of evidence for optimal treatment in oligometastatic head and neck cancer and it is especially unclear which patients benefit from radical local treatment of all tumour sites.
Thomas Weissmann   +12 more
doaj   +1 more source

Validation studies on migraine diagnostic tools for use in nonclinical settings: a systematic review

open access: yesArquivos de Neuro-Psiquiatria, 2023
Background Migraine underdiagnosis and undertreatment are so widespread, that hence is essential to diagnose migraine sufferers in nonclinical settings.
Du Wei   +6 more
doaj   +1 more source

Evolutionary Computation in High Energy Physics [PDF]

open access: yes, 2008
Evolutionary Computation is a branch of computer science with which, traditionally, High Energy Physics has fewer connections. Its methods were investigated in this field, mainly for data analysis tasks. These methods and studies are, however, less known
Teodorescu, Liliana
core   +2 more sources

Optimal diet selection by white-tailed deer: Balancing reproduction with starvation risk [PDF]

open access: yesEvolutionary Ecology, 1992
Summary Energy intake rates of wintering deer vary over time because of variation in the abundance and quality of their natural foods. Accordingly, there is a chance that energy requirements will not be satisfied in a feeding period. This is especially critical because deer are reproductive during winter; hence selecting diets to minimize the risk of ...
openaire   +2 more sources

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