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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

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Hien Nguyen

    1. La Trobe University, Bundoora, Australia

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Part of the book series: Communications in Computer and Information Science (CCIS, volume 1150)

Included in the following conference series:

Conference proceedings info: RSSDS 2019.

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Table of contents (18 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Invited Papers

    1. Front Matter

      Pages 1-1
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    2. Symbolic Formulae for Linear Mixed Models

      • Emi Tanaka, Francis K. C. Hui
      Pages 3-21

    3. code::proof: Prepare for Most Weather Conditions

      • Charles T. Gray
      Pages 22-41

    4. Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models

      • Faïcel Chamroukhi, Florian Lecocq, Hien D. Nguyen
      Pages 42-56

    5. Flexible Modelling via Multivariate Skew Distributions

      • Geoffrey J. McLachlan, Sharon X. Lee
      Pages 57-67

    6. Estimating Occupancy and Fitting Models with the Two-Stage Approach

      • Natalie Karavarsamis
      Pages 68-80

    7. Component Elimination Strategies to Fit Mixtures of Multiple Scale Distributions

      • Florence Forbes, Alexis Arnaud, Benjamin Lemasson, Emmanuel Barbier
      Pages 81-95

    8. An Introduction to Approximate Bayesian Computation

      • Hien D. Nguyen
      Pages 96-108

  3. Contributing Papers

    1. Front Matter

      Pages 109-109
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    2. Truth, Proof, and Reproducibility: There’s No Counter-Attack for the Codeless

      • Charles T. Gray, Ben Marwick
      Pages 111-129

    3. On Adaptive Gauss-Hermite Quadrature for Estimation in GLMM’s

      • Paul Kabaila, Nishika Ranathunga
      Pages 130-139

    4. Deep Learning with Periodic Features and Applications in Particle Physics

      • Steffen Maeland, Inga Strümke
      Pages 140-147

    5. Copula Modelling of Nurses’ Agitation-Sedation Rating of ICU Patients

      • Ainura Tursunalieva, Irene Hudson, Geoff Chase
      Pages 148-161

    6. Predicting the Whole Distribution with Methods for Depth Data Analysis Demonstrated on a Colorectal Cancer Treatment Study

      • D. Vicendese, L. Te Marvelde, P. D. McNair, K. Whitfield, D. R. English, S. Ben Taieb et al.
      Pages 162-182

    7. Resilient and Deep Network for Internet of Things (IoT) Malware Detection

      • Nazanin Bakhshinejad, Ali Hamzeh
      Pages 183-197

    8. Prediction of Neurological Deterioration of Patients with Mild Traumatic Brain Injury Using Machine Learning

      • Gem Ralph Caracol, Jin-gyu Choi, Jae-Sung Park, Byung-chul Son, Sin-soo Jeon, Kwan-Sung Lee et al.
      Pages 198-210

    9. Spherical Data Handling and Analysis with R package rcosmo

      • Daniel Fryer, Andriy Olenko
      Pages 211-225

    10. On the Parameter Estimation in the Schwartz-Smith’s Two-Factor Model

      • Karol Binkowski, Peilun He, Nino Kordzakhia, Pavel Shevchenko
      Pages 226-237

    11. Interval Estimators for Inequality Measures Using Grouped Data

      • Dilanka S. Dedduwakumara, Luke A. Prendergast
      Pages 238-252
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  1. Statistics and Data Science

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About this book

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. 

Editors and Affiliations

  • La Trobe University, Bundoora, Australia

    Hien Nguyen

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