Applied Statistics and Data Science
Springer International Publishing (Verlag)
978-3-030-86132-2 (ISBN)
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.
Dr. Yogendra P. Chaubey is professor of mathematics and statistics at Concordia University. His research focus is in statistical methodology. Dr. Fassil Nebebe is professor of supply chain and business technology management at Concordia University. He teaches undergraduate and graduate courses in applied linear statistical models, research methods, managerial statistics, and business statistics. His research focuses on Bayesian methods, Gibbs sampling, data modeling, and statistical computing. Dr. Arusharka Sen is 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 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.
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.
| Erscheinungsdatum | 10.12.2021 |
|---|---|
| Reihe/Serie | Springer Proceedings in Mathematics & Statistics |
| Zusatzinfo | IX, 159 p. 1 illus. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 384 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra | |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Schlagworte | actuarial mathematics • Computational Statistics • data analytics • Data Mining • Data Science • environmental statistics • Probability and Statistics in Computer Science • Small area estimation • Statistical Methods • Supply Chain • Survival Analysis |
| ISBN-10 | 3-030-86132-5 / 3030861325 |
| ISBN-13 | 978-3-030-86132-2 / 9783030861322 |
| Zustand | Neuware |
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