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＜電子ブック＞
Applied Statistics and Data Science : Proceedings of Statistics 2021 Canada, Selected Contributions / edited by Yogendra P. Chaubey, Salim Lahmiri, Fassil Nebebe, Arusharka Sen
(Springer Proceedings in Mathematics & Statistics. ISSN:21941017 ; 375)

版	1st ed. 2021.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2021
大きさ	IX, 159 p. 1 illus : online resource
著者標目	Chaubey, Yogendra P editor Lahmiri, Salim editor Nebebe, Fassil editor Sen, Arusharka editor SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Quantitative research LCSH:Mathematical statistics—Data processing LCSH:Actuarial science FREE:Applied Statistics FREE:Data Analysis and Big Data FREE:Statistics and Computing FREE:Statistical Theory and Methods FREE:Actuarial Mathematics
一般注記	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 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 HTTP:URL=https://doi.org/10.1007/978-3-030-86133-9

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