Applied Statistics and Data Science. Proceedings of Statistics 2021 Canada, Selected Contributions.

Chaubey, Yogendra P. et al. (Eds.)

ISBN 10: 3030861325 / ISBN 13: 9783030861322

Editore: Cham, Springer International Publishing : Imprint: Springer., 2021

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1st ed. 2021. IX, 159 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Springer Proceedings in Mathematics & Statistics, 375. Sprache: Englisch. Codice articolo 7574BB

Dati bibliografici

Titolo: Applied Statistics and Data Science. ...

Casa editrice: Cham, Springer International Publishing : Imprint: Springer.

Data di pubblicazione: 2021

Legatura: Rilegato

Edizione: prima edizione

Descrizione articolo

Riassunto:

<p>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.</p>

Informazioni sull?autore:

<b>?</b><b>Dr. Yogendra P. Chaubey</b>&nbsp;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.<p><b>Dr. Fassil Nebebe</b>&nbsp;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.</p><p><b>Dr. Arusharka Sen</b>&nbsp;is an Associate Professor of Mathematics and Statistics at Concordia University. His research focuses on nonparametric function estimation and the analysis of censored data.</p><p><br></p><b>Dr. Salim Lahmiri</b>&nbsp;is an Assistant Professor of Supply Chain and Business Technology Management at Concordia University. He serves as associate editor for&nbsp;<i>Expert Systems with Applications</i>;&nbsp;<i>Machine Learning with Applications</i>;&nbsp;<i>Chaos, Solitons & Fractals</i>;&nbsp;<i>Entropy</i>; and&nbsp;<i>Machine Learning & Knowledge Extraction</i>. Dr. Lahmiri's research focuses on artificial intelligence, intelligent systems, data science, predictive analytics, and pattern recognition.