Results 71 to 80 of about 652,410 (265)
Extreme Values and Financial Risk [PDF]
, 2018 Since the 2008 financial crisis, modeling of the extreme values of financial risk has become important. Postgraduate programs and PhD research programs in mathematical finance are cropping up in nearly every university. Additionally, many conferences are being held annually on the topic of extreme financial risk.
Chan, Stephen, Nadarajah, Saralees
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Reconstructing enzyme evolution by protein engineering
FEBS Letters, EarlyView.Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler +2 more
wiley +1 more source
MathematicsThis paper proposes a computationally efficient framework for estimating first-passage probabilities of nonlinear structures under stochastic seismic excitations. The methodology integrates Optimal Latinized Partially Stratified Sampling (OLPSS) with the
Bao-Hua Liu, Yan Cao, Long-Wen Zhang
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Inference for Clusters of Extreme Values
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2003 SummaryInference for clusters of extreme values of a time series typically requires the identification of independent clusters of exceedances over a high threshold. The choice of declustering scheme often has a significant effect on estimates of cluster characteristics.
Ferro, C.A.T., Segers, J.J.J.
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Molecular Oncology, EarlyView.Circulating microRNAs as biomarkers of cachexia and sex‐specific cancer in senior dogs. In 25 client‐owned dogs, four circulating miRNAs (miR‐15a, miR‐15b, miR‐16, miR‐140) were downregulated in cachexia, with miR‐16 the strongest individual biomarker (AUC = 0.899).
Soon‐Seok Park +6 more
wiley +1 more source
Advanced Techniques for Financial Distress Prediction
ForecastingThis study compares Logit, Probit, Extreme Value, and Artificial Neural Network (ANN) models using data from 2012 to 2024 in the Taiwan electronics industry. ANN outperforms traditional models, achieving 98% accuracy in predicting financial distress. Two
Lee-Wen Yang +2 more
doaj +1 more source
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Molecular Oncology, EarlyView.Interferon type 1 (IFN‐1) production and signaling is associated with the acquisition of therapy resistance, following chronic DNA damage, via Interferon‐related DNA damage resistance signature (IRDS) gene expression. An alternative, DNA damage‐independent role of sustained IFN‐1 mediated resistance was identified and characterized by the emergence of ...
Ashlyn Conant +11 more
wiley +1 more source
Improving extreme value statistics
Physical Review E, 2014 The rate of uniform convergence in extreme value statistics is non-universal and can be arbitrarily slow. Further, the relative error can be unbounded in the tail of the approximation, leading to difficulty in extrapolating the extreme value fit beyond the available data.
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Spatial biology in cancer epigenetics
Molecular Oncology, EarlyView.Spatial epigenomics combines molecular profiling with tissue architecture to reveal how gene regulation is organized within intact tissues. In cancer, these technologies uncover the mechanisms driving tumor heterogeneity and microenvironmental interactions, opening new opportunities for biomarker discovery and precision medicine.
Eva Crespo‐García, Manel Esteller
wiley +1 more source