Results 71 to 80 of about 2,555,341 (310)
Variance estimation for sensitivity analysis of poverty and inequality measures
Survey Research Methods, 2017 Estimates of poverty and inequality are often based on application of a single equivalence scale, despite the fact that a large number of different equivalence scales can be found in the literature.
Christian Dudel
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FEBS Letters, EarlyView.Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho +3 more
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Robust Estimation Using Influence Function as Error [PDF]
, 1979 1 online resource (PDF, 18 pages)Wang, Hai-Li; Hinkley, David. (1979). Robust Estimation Using Influence Function as Error.
Hinkley, David, Wang, Hai-Li
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Applied SciencesThis paper presents a hybrid methodology for predicting rock mass deformation and roadway loads induced by longwall mining. The approach combines the classical Budryk–Knothe influence function model with numerical simulations in the FLAC3D finite ...
Roman Ścigała, Marek Jendryś
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Variance estimation for complex indicators of poverty and inequality using linearization techniques
Survey Research Methods, 2009 The paper presents the Eurostat experience in calculating measures of precision, including standard errors, confidence intervals and design effect coefficients - the ratio of the variance of a statistic with the actual sample design to the variance of ...
Guillaume Osier
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Phosphatidylinositol 4‐kinase as a target of pathogens—friend or foe?
FEBS Letters, EarlyView.This graphical summary illustrates the roles of phosphatidylinositol 4‐kinases (PI4Ks). PI4Ks regulate key cellular processes and can be hijacked by pathogens, such as viruses, bacteria and parasites, to support their intracellular replication. Their dual role as essential host enzymes and pathogen cofactors makes them promising drug targets.
Ana C. Mendes +3 more
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Debugging and Explaining Metric Learning Approaches: An Influence Function Based Perspective
, 2022 Deep metric learning (DML) learns a generalizable embedding space where the representations of semantically similar samples are closer. Despite achieving good performance, the state-of-the-art models still suffer from the generalization errors such as ...
Dong, JS, Liu, R, Yang, X, Lin, Y
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Foreign Workers and the Wage Distribution: What Does the Influence Function Reveal?
Econometrics, 2018 This paper draws upon influence function regression methods to determine where foreign workers stand in the distribution of private sector wages in Luxembourg, and assess whether and how much their wages contribute to wage inequality.
Chung Choe, Philippe Van Kerm
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Protein pyrophosphorylation by inositol pyrophosphates — detection, function, and regulation
FEBS Letters, EarlyView.Protein pyrophosphorylation is an unusual signaling mechanism that was discovered two decades ago. It can be driven by inositol pyrophosphate messengers and influences various cellular processes. Herein, we summarize the research progress and challenges of this field, covering pathways found to be regulated by this posttranslational modification as ...
Sarah Lampe +3 more
wiley +1 more source
Influence function of projection-pursuit principal components for functional data [PDF]
, 2015 In the finite-dimensional setting, Li and Chen (1985) proposed a method for principal components analysis using projection-pursuit techniques. This procedure was generalized to the functional setting by Bali et al.
Bali, Juan Lucas +1 more
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