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Statistics and Data Science: Research School on Statistics and Data Science, RSSDS 2019, Melbourne, VIC, Australia, July 24–26, 2019, Proceedings de Hien Nguyen

Statistics and Data Science: Research School on Statistics and Data Science, RSSDS 2019, Melbourne, VIC, Australia, July 24–26, 2019, Proceedings: Communications in Computer and Information Science, cartea 1150

Editat de Hien Nguyen
en Limba Engleză Paperback – 4 ian 2020
This book constitutes the proceedings of the Research School on Statistics and Data Science, RSSDS 2019, held in Melbourne, VIC, Australia, in July 2019.
The 11 papers presented in this book were carefully reviewed and selected from 23 submissions. The volume also contains 7 invited talks. The workshop brought together academics, researchers, and industry practitioners of statistics and data science, to discuss numerous advances in the disciplines and their impact on the sciences and society. The topics covered are data analysis, data science, data mining, data visualization, bioinformatics, machine learning, neural networks, statistics, and probability. 
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Specificații

ISBN-13: 9789811519598
ISBN-10: 9811519595
Pagini: 263
Ilustrații: X, 263 p. 152 illus., 66 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.39 kg
Ediția:1st ed. 2019
Editura: Springer Nature Singapore
Colecția Springer
Seria Communications in Computer and Information Science

Locul publicării:Singapore, Singapore

Cuprins

Invited Papers.- Symbolic Formulae for Linear Mixed Models.- code::proof: Prepare for most weather conditions.- Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models.- Flexible Modelling via Multivariate Skew Distributions.- Estimating occupancy and fitting models with the two-stage approach.- Component elimination strategies for mixtures of multiple scale distributions.- An introduction to approximate Bayesian computation.- Contributing Papers.- Truth, Proof, and Reproducibility: There's no counter-attack for the codeless.- On Adaptive Gauss-Hermite Quadrature for Estimation in GLMM's.- Deep learning with periodic features and applications in particle physics.- Copula Modelling of Nurses' Agitation-Sedation Rating of ICU Patients.- Predicting the whole distribution with methods for depth data analysis demonstrated on a colorectal cancer treatment study.- Resilient and Deep Network for Internet of Things (IoT) Malware Detection.- Prediction of Neurological Deterioration of Patients with Mild Traumatic Brain Injury using Machine Learning.- Spherical data handling and analysis with R package rcosmo.- On the Parameter Estimation in the Schwartz-Smith's Two-Factor Model.- Interval estimators for inequality measures using grouped data.- Exact model averaged tail area confidence intervals.