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Linear operator‐based statistical analysis: A useful paradigm for big data

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Publication:4960909
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DOI10.1002/cjs.11329zbMath1466.62363OpenAlexW2765401959MaRDI QIDQ4960909

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Publication date: 24 April 2020

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.11329


zbMATH Keywords

functional data analysissufficient dimension reductionkernel learningcollective smoothnessnonparametric graphical models


Mathematics Subject Classification ID

Multivariate analysis (62H99) Functional data analysis (62R10) Nonparametric inference (62G99) Statistical aspects of big data and data science (62R07)


Related Items (6)

Dimension reduction for functional data based on weak conditional moments ⋮ Copula Gaussian Graphical Models for Functional Data ⋮ Sliced Inverse Regression in Metric Spaces ⋮ Functional sufficient dimension reduction through average Fréchet derivatives ⋮ B-scaling: a novel nonparametric data fusion method ⋮ Nonparametric Functional Graphical Modeling Through Functional Additive Regression Operator







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