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.

Keywords

Editors and Affiliations

La Trobe University, Bundoora, Australia

Hien Nguyen

Bibliographic Information

Book Title: Statistics and Data Science
Book Subtitle: Research School on Statistics and Data Science, RSSDS 2019, Melbourne, VIC, Australia, July 24–26, 2019, Proceedings
Editors: Hien Nguyen
Series Title: Communications in Computer and Information Science
DOI: https://doi.org/10.1007/978-981-15-1960-4
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2019
Softcover ISBN: 978-981-15-1959-8Published: 04 January 2020
eBook ISBN: 978-981-15-1960-4Published: 03 January 2020
Series ISSN: 1865-0929
Series E-ISSN: 1865-0937
Edition Number: 1
Number of Pages: X, 263
Number of Illustrations: 86 b/w illustrations, 66 illustrations in colour
Topics: Probability and Statistics in Computer Science, Machine Learning, Database Management, Operating Systems

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Table of contents (18 papers)

Front Matter

Invited Papers

Front Matter

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 to Fit Mixtures of Multiple Scale Distributions

An Introduction to Approximate Bayesian Computation

Contributing Papers

Front Matter

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

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