Applied Statistics and Data Science

Proceedings of Statistics 2021 Canada, Selected Contributions
1. Auflage 2021
ISBN: 978-3-030-86135-3
Verlag: Springer International Publishing

Buch, Englisch, 159 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 277 g

Reihe: Springer Proceedings in Mathematics & Statistics

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.

Zielgruppe

Research

Autoren/Hrsg.

Chaubey, Yogendra P.

Sen, Arusharka

Lahmiri, Salim

Nebebe, Fassil

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle

Inhaltsverzeichnis

1. Minimum Profile Hellinger Distance Estimation for Semiparametric Simple Linear Regression Model.- 2. A Spatiotemporal Investigation of the Cod Stock in the Northern Gulf of St-Lawrence.- 3. Modeling Obesity Rate with Spatial Auto-correlation: A Case Study.- 4. Bayesian Inference for Inverse Gaussian Data with Emphasis on the Coefficient of Variation.- 5. Estimation and Testing of a Common Coefficient of Variation from Inverse Gaussian Distributions.- 6. A Markov Model of Polygenic Inheritance.- 7. Bayes Linear Emulation of Simulated Crop Yield.

Über Autor(innen)

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.