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Record Nr. |
UNINA9910520068603321 |
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Titolo |
Applied Statistics and Data Science : Proceedings of Statistics 2021 Canada, Selected Contributions / / edited by Yogendra P. Chaubey, Salim Lahmiri, Fassil Nebebe, Arusharka Sen |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (163 pages) |
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Collana |
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Springer Proceedings in Mathematics & Statistics, , 2194-1017 ; ; 375 |
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Disciplina |
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Soggetti |
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Statistics |
Quantitative research |
Mathematical statistics - Data processing |
Actuarial science |
Applied Statistics |
Data Analysis and Big Data |
Statistics and Computing |
Statistical Theory and Methods |
Actuarial Mathematics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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, |
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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. |
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