Statistical modeling and simulation for experimental design and machine learning applications : selected contributions from SimStat 2019 and invited papers

Statistical modeling and simulation for experimental design and machine learning applications : selected contributions from SimStat 2019 and invited papers / Jürgen Pilz, Viatcheslav B. Melas, Arne Bathke, editors.

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, mach...

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Bibliographic Details
Corporate Author: International Workshop on Simulation and Statistics Salzburg, Austria
Other Authors: Pilz, Jürgen, 1951- (Editor), Melas, V. B. (Vi︠a︡cheslav Borisovich) (Editor), Bathke, Arne (Editor)
Format: eBook
Language:English
Published: Cham, Switzerland : Springer, [2023]
Series:Contributions to statistics.
Subjects:
Experimental design > Statistical methods > Congresses.
Machine learning > Statistical methods > Congresses.
Experimental design > Statistical methods
Machine learning > Statistical methods
Disseny d'experiments.
Aprenentatge automàtic.
Estadística matemàtica.
proceedings (reports)
Conference papers and proceedings
Conference papers and proceedings.
Actes de congrès.
Llibres electrònics.
Online Access:Click for online access
Table of Contents:
  • Intro
  • Preface
  • Contents
  • Part I Invited Papers
  • 1 Likelihood Ratios in Forensics: What They Are and What They Are Not
  • 1.1 Introduction
  • 1.2 Lindley's Likelihood Ratio (LLR)
  • 1.2.1 Notations
  • 1.2.2 A Frequentist Framework for Lindley's Likelihood Ratio (LLR)
  • 1.3 Score-Based Likelihood Ratio (SLR)
  • 1.3.1 The Expression of the SLR
  • 1.3.2 The Glass Example
  • 1.4 Discussion
  • References
  • 2 MANOVA for Large Number of Treatments
  • 2.1 Introduction
  • 2.2 Notations and Model Setup
  • 2.3 Simulations
  • 2.3.1 MANOVA Tests for Large g
  • 2.3.2 Special Case: ANOVA for Large g
  • 2.4 Discussion and Outlook
  • References
  • 3 Pollutant Dispersion Simulation by Means of a Stochastic Particle Model and a Dynamic Gaussian Plume Model
  • 3.1 Introduction
  • 3.2 Meteorological Monitoring Network
  • 3.3 Wind Field Modeling
  • 3.3.1 Mass Correction of the Wind Field
  • 3.3.2 Plume Rise
  • 3.4 Stochastic Particle Model
  • 3.4.1 Deposition
  • 3.4.2 Implementation
  • 3.5 Dynamic Gaussian Plume Model
  • 3.6 Implementation on the Server
  • 3.7 A Real-World Example with Application to an Alpine Valley
  • 3.8 Conclusions and Outlook
  • References
  • 4 On an Alternative Trigonometric Strategy for StatisticalModeling
  • 4.1 Introduction
  • 4.2 The Alternative Sine Distribution
  • 4.2.1 Presentation
  • 4.2.2 Moment Properties
  • 4.2.3 Parametric Extensions
  • 4.3 AS Generated Family
  • 4.3.1 Definition
  • 4.3.2 Series Expansions
  • 4.3.3 Example: The ASE Exponential Distribution
  • 4.3.4 Moment Properties
  • 4.4 Application to a Famous Cancer Data
  • 4.5 Conclusion
  • References
  • Part II Design of Experiments
  • 5 Incremental Construction of Nested Designs Basedon Two-Level Fractional Factorial Designs
  • 5.1 Introduction
  • 5.6 Covering Properties of Two-Level Factorial Designs
  • 5.6.1 Bounds on CRH(Xn)
  • 5.6.2 Calculation of CRH(Xn)
  • 5.6.2.1 Algorithmic Construction of a Lower Bound on CRH(Xn)
  • 5.7 Greedy Constructions Based on Fractional Factorial Designs
  • 5.7.1 Base Designs
  • 5.7.2 Rescaled Designs
  • 5.7.3 Projection Properties
  • 5.8 Summary and Future Work
  • Appendix
  • References
  • 6 A Study of L-Optimal Designs for the Two-Dimensional Exponential Model
  • 6.1 Introduction
  • 6.2 Equivalence Theorem for L-Optimal Designs
  • 6.3 General Case
  • 6.4 Excess and Saturated Designs
  • References

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