Multivariate Statistics: Classical Foundations and Modern Machine Learning: Hemant Ishwaran, Boca Raton, FL: Chapman & Hall/CRC Press, 2025, xix + 465 pp., $180.00(H), ISBN: 978-1-032-75879-4.
The book *Multivariate Statistics: Classical Foundations and Modern Machine Learning* offers a comprehensive introduction to multivariate statistics, bridging classical methods such as multivariate normality, MANOVA, and principal component analysis with modern machine learning techniques including...
| Published in: | American Statistician Vol. 80; no. 2; pp. 327 - 329 |
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| Format: | Book Review |
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Taylor & Francis Ltd
May2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=193251896&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 193251896 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: May2026 vid: 80 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 193251896 10.1080/00031305.2026.2620697 ppf: 327 ppct: 2 formats: tig: atl: Multivariate Statistics: Classical Foundations and Modern Machine Learning: Hemant Ishwaran, Boca Raton, FL: Chapman & Hall/CRC Press, 2025, xix + 465 pp., $180.00(H), ISBN: 978-1-032-75879-4. aug: au: Böhning, Dankmar affil: University of Southampton, Southampton, UK su: Multivariate analysis Statistical learning Boosting algorithms Mathematical statistics Biometry Random forest algorithms Principal components analysis Causal inference sug: subj: Multivariate analysis Statistical learning Boosting algorithms Mathematical statistics Biometry Random forest algorithms Principal components analysis Causal inference ab: The book *Multivariate Statistics: Classical Foundations and Modern Machine Learning* offers a comprehensive introduction to multivariate statistics, bridging classical methods such as multivariate normality, MANOVA, and principal component analysis with modern machine learning techniques including gradient boosting, random forests, and causal inference. It is structured into 20 chapters, each with exercises and bibliographies, and is designed primarily for advanced biostatistics Ph.D. students over a semester or two. The text emphasizes practical applications with case studies and discusses theoretical foundations, though it does not provide exercise solutions or direct dataset access, expecting readers to locate data independently. This work serves as a detailed resource for researchers and students seeking an integrated understanding of classical and contemporary multivariate statistical methods. pubtype: Review doctype: Book Review src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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