Applied Statistics and Data Science: Proceedings of Statistics 2021 Canada, Selected Contributions: 375 (Springer Proceedings in Mathematics & Statistics, 375)

Chaubey, Yogendra P. (Editor) / Lahmiri, Salim (Editor) / Nebebe, Fassil (Editor) / Sen, Arusharka (Editor)

ISBN 10: 303086135X / ISBN 13: 9783030861353

Editore: Springer, 2022

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168 pages. 9.25x6.10x0.51 inches. In Stock.

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Dati bibliografici

• Titolo: Applied Statistics and Data Science: Proceedings of Statistics 2021 Canada, Selected Contributions: 375 (Springer Proceedings in Mathematics & Statistics, 375)
• Editore: Springer
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 303086135X
• ISBN 13: 9783030861353
• Rilegatura: Paperback
• Condizione: Brand New

Descrizione articolo

Riassunto

This proceedings volume features top contributions in modern statistical methods from Statistics 2021 Canada, the 6th Annual Canadian Conference in Applied Statistics, held virtually on July 15-18, 2021. Papers are contributed from established and emerging scholars, covering cutting-edge and contemporary innovative techniques in statistics and data science. Major areas of contribution include Bayesian statistics; computational statistics; data science; semi-parametric regression; and stochastic methods in biology, crop science, ecology and engineering. It will be a valuable edited collection for graduate students, researchers, and practitioners in a wide array of applied statistical and data science methods.

Informazioni sull?autore

?Dr. Yogendra P. Chaubey is a Professor of Mathematics and Statistics at Concordia University. His research focus is in statistical methodology, mostly concentrated in the area of nonparametric smoothing.

Dr. Fassil Nebebe is a Professor of Supply Chain and Business Technology Management at Concordia University. His research focuses on statistical methodology using resampling techniques, SEM, and predictive analytics.

Dr. Arusharka Sen is an Associate Professor of Mathematics and Statistics at Concordia University. His research focuses on nonparametric function estimation and the analysis of censored data.

Dr. Salim Lahmiri is an Assistant Professor of Supply Chain and Business Technology Management at Concordia University. He serves as associate editor for Expert Systems with Applications; Machine Learning with Applications; Chaos, Solitons & Fractals; Entropy; and Machine Learning & Knowledge Extraction. Dr. Lahmiri's research focuses on artificial intelligence, intelligent systems, data science, predictive analytics, and pattern recognition.