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New Statistical Robust Estimators: Open Problems
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.ORCID iD: 0000-0001-8473-3663
2018 (English)In: Open Problems in Optimization and Data Analysis / [ed] Pardalos, Panos M., Springer Publishing Company, 2018, p. 23-47Chapter in book (Refereed)
Abstract [en]

Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book.  Each contribution provides the fundamentals  needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline.

Place, publisher, year, edition, pages
Springer Publishing Company, 2018. p. 23-47
Series
Springer Optimization and Its Application, ISSN 1931-6836 ; 141
Keywords [en]
detecting outliers, robust estimators, regression, covariance, mathematical programming
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:ltu:diva-73555ISBN: 978-3-319-99141-2 (print)OAI: oai:DiVA.org:ltu-73555DiVA, id: diva2:1303737
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-07-24

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https://www.springer.com/us/book/9783319991412

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Migdalas, Athanasios

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