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Optimal Subsampling Algorithms for Big Data Regressions.
TLDR
This paper studies the Optimal Subsampling Method under the A-optimality Criterion (OSMAC) for generalized linear models. Expand
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Infection of human pegivirus 2 (HPgV-2) is associated with hepatitis C virus but not hepatitis B virus infection in people who inject drugs.
We evaluated the association between human pegivirus-2 (HPgV-2) infection and hepatitis C virus (HCV)/hepatitis B virus (HBV) co-infection in 745 plasma samples collected from HCV-positive but humanExpand
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Concrete Compression Test Data Estimation Based on a Wavelet Neural Network Model
Firstly, a genetic algorithm (GA) and simulated annealing (SA) optimized fuzzy c-means clustering algorithm (FCM) was proposed in this paper, which was developed to allow for a clustering analysis ofExpand
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A Review on Optimal Subsampling Methods for 1 Massive Datasets
5 Subsampling is an effective way to deal with big data problems and many subsampling 6 approaches have been proposed for different models, such as leverage sampling for lin7 ear regression modelsExpand
Statistica Sinica OPTIMAL SUBSAMPLING ALGORITHMS FOR BIG DATA REGRESSIONS
To fast approximate maximum likelihood estimators with massive data, this paper studies the optimal subsampling method under the A-optimality criterion (OSMAC) for generalized linear models. TheExpand
Financial contagion and contagion channels in the forex market: A new approach via the dynamic mixture copula-extreme value theory
We propose a new approach to the study of financial contagion and contagion channels in the forex market by using a dynamic mixture copula-extreme value theory (DMC-EVT) model. This method allowsExpand