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Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications

Selected Contributions from SimStat 2019 and Invited Papers

  • Conference proceedings
  • © 2023

Overview

Editors:
  1. Jürgen Pilz

    1. Department of Statistics, University of Klagenfurt, Klagenfurt, Austria

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  2. Viatcheslav B. Melas

    1. Department of Stochastic Simulation, St. Petersburg State University, St. Petersburg, Russia

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    You can also search for this editor in PubMed   Google Scholar

  3. Arne Bathke

    1. Department of Artificial Intelligence and Human Interfaces, Paris Lodron University Salzburg, Salzburg, Austria

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    You can also search for this editor in PubMed   Google Scholar

  • Presents current developments in statistical modeling and simulation
  • Focuses on experimental design and machine learning applications
  • Features invited contributions

Part of the book series: Contributions to Statistics (CONTRIB.STAT.)

Included in the following conference series:

Conference proceedings info: SimStat 2019.

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Table of contents (14 papers)

  1. Front Matter

    Pages i-x
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  2. Invited Papers

    1. Front Matter

      Pages 1-1
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    2. Likelihood Ratios in Forensics: What They Are and What They Are Not

      • Daniel Gluck, Elham Tabassi, N. Balakrishnan, Liansheng Larry Tang
      Pages 3-16

    3. MANOVA for Large Number of Treatments

      • S. Ejaz Ahmed, M. Rauf Ahmad
      Pages 17-30

    4. Pollutant Dispersion Simulation by Means of a Stochastic Particle Model and a Dynamic Gaussian Plume Model

      • Maximilian Arbeiter, Albrecht Gebhardt, Gunter Spöck
      Pages 31-49

    5. On an Alternative Trigonometric Strategy for Statistical Modeling

      • Christophe Chesneau
      Pages 51-74

  3. Design of Experiments

    1. Front Matter

      Pages 75-75
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    2. Incremental Construction of Nested Designs Based on Two-Level Fractional Factorial Designs

      • Rodrigo Cabral-Farias, Luc Pronzato, Maria-João Rendas
      Pages 77-110

    3. A Study of L-Optimal Designs for the Two-Dimensional Exponential Model

      • Viatcheslav B. Melas, Alina A. Pleshkova, Petr V. Shpilev
      Pages 111-126

    4. Testing for Randomized Block Single-Case Designs by Combined Permutation Tests with Multivariate Mixed Data

      • Rosa Arboretti Giancristofaro, Riccardo Ceccato, Luca Pegoraro, Luigi Salmaso
      Pages 127-139

    5. Adaptive Design Criteria Motivated by a Plug-In Percentile Estimator

      • Rodrigo Cabral Farias, Luc Pronzato, Maria-João Rendas
      Pages 141-177

  4. Queueing and Inventory Analysis

    1. Front Matter

      Pages 179-179
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    2. On a Parametric Estimation for a Convolution of Exponential Densities

      • Alexander Andronov, Nadezda Spiridovska, Diana Santalova
      Pages 181-195

    3. Statistical Estimation with a Known Quantile and Its Application in a Modified ABC-XYZ Analysis

      • Zhanna Zenkova, Sergey Tarima, Wilson Musoni, Yuriy Dmitriev
      Pages 197-207

  5. Machine Learning and Applications

    1. Front Matter

      Pages 209-209
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    2. A Study of Design of Experiments and Machine Learning Methods to Improve Fault Detection Algorithms

      • Rosa Arboretti Giancristofaro, Riccardo Ceccato, Luca Pegoraro, Luigi Salmaso
      Pages 211-221

    3. Microstructure Image Segmentation Using Patch-Based Clustering Approach

      • Dženana Alagić, Jürgen Pilz
      Pages 223-235

    4. Clustering and Symptom Analysis in Binary Data with Application

      • N. P. Alexeyeva, F. S. Al-juboori, E. P. Skurat
      Pages 237-244

    5. Big Data for Credit Risk Analysis: Efficient Machine Learning Models Using PySpark

      • Afshin Ashofteh
      Pages 245-265
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  1. Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications

Keywords

About this book

This volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and testing problems, the design of experiments, reliability and queueing theory, inventory analysis, and the interplay between statistical inference, machine learning methods and related applications. The refereed contributions originate from the 10th International Workshop on Simulation and Statistics, SimStat 2019, which was held in Salzburg, Austria, September 2–6, 2019, and were either presented at the conference or developed afterwards, relating closely to the topics of the workshop. The book is intended for statisticians and Ph.D. students who seek current developments and applications in the field.

Editors and Affiliations

  • Department of Statistics, University of Klagenfurt, Klagenfurt, Austria

    Jürgen Pilz

  • Department of Stochastic Simulation, St. Petersburg State University, St. Petersburg, Russia

    Viatcheslav B. Melas

  • Department of Artificial Intelligence and Human Interfaces, Paris Lodron University Salzburg, Salzburg, Austria

    Arne Bathke

About the editors

Jürgen Pilz is Professor Emeritus at the Department of Statistics at the Alpen-Adria University Klagenfurt in Austria. His research areas include Bayesian statistics, spatial statistics, environmental and industrial statistics, statistical quality control and design of experiments.

Viatcheslav B. Melas is a Professor at the Department of Stochastic Simulation at the St. Petersburg State University, Russia. His research areas include experimental design, stochastic simulation and regression analysis, with a focus on functional approaches to optimal experimental design.

Arne Bathke is Full Professor of Statistics at the Paris Lodron University Salzburg, Austria. His main research interests are related to nonparametric and multivariate statistics applied in different fields, from social sciences to biomedicine and engineering.

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