Special issue on statistical analysis of networks: Preface by the guest editors

Michael Schweinberger; Francesco C. Stingo; Maria Prosperina Vitale

doi:10.1007/s10260-021-00608-z

Special issue on statistical analysis of networks: Preface by the guest editors

Michael Schweinberger
, Francesco C. Stingo
, Maria Prosperina Vitale

Statistics

Research output: Contribution to journal › Editorial › peer-review

2 Scopus citations

Abstract

The special issue on Statistical Analysis of Networks aspires to convey the breadth and depth of statistical learning with networks, ranging from networks that are observed to networks that are unobserved and learned from data. It includes ten select papers with methodological and theoretical advances, and demonstrates the usefulness of the network paradigm by applications to current problems.

Original language	English (US)
Pages (from-to)	1285-1288
Number of pages	4
Journal	Statistical Methods and Applications
Volume	30
Issue number	5
DOIs	https://doi.org/10.1007/s10260-021-00608-z
State	Published - Dec 2021

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1007/s10260-021-00608-z

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title = "Special issue on statistical analysis of networks: Preface by the guest editors",

abstract = "The special issue on Statistical Analysis of Networks aspires to convey the breadth and depth of statistical learning with networks, ranging from networks that are observed to networks that are unobserved and learned from data. It includes ten select papers with methodological and theoretical advances, and demonstrates the usefulness of the network paradigm by applications to current problems.",

author = "Michael Schweinberger and Stingo, \{Francesco C.\} and Vitale, \{Maria Prosperina\}",

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AB - The special issue on Statistical Analysis of Networks aspires to convey the breadth and depth of statistical learning with networks, ranging from networks that are observed to networks that are unobserved and learned from data. It includes ten select papers with methodological and theoretical advances, and demonstrates the usefulness of the network paradigm by applications to current problems.

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Special issue on statistical analysis of networks: Preface by the guest editors

Abstract

All Science Journal Classification (ASJC) codes

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