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Produktbild: Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications

Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications Selected Contributions from SimStat 2019 and Invited Papers

Aus der Reihe Contributions to Statistics

195,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.10.2024

Abbildungen

X, 265 p. 85 illus., 56 illus. in color.

Herausgeber

Jürgen Pilz + weitere

Verlag

Springer

Seitenzahl

265

Maße (L/B/H)

23,5/15,5/1,6 cm

Gewicht

423 g

Sprache

Englisch

ISBN

978-3-031-40057-5

Beschreibung

Portrait


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.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

20.10.2024

Abbildungen

X, 265 p. 85 illus., 56 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

265

Maße (L/B/H)

23,5/15,5/1,6 cm

Gewicht

423 g

Sprache

Englisch

ISBN

978-3-031-40057-5

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications

  • - 
    Part I Invited Papers.
    - 1. Likelihood Ratios in Forensics: What They Are and What They Are Not. - 2. MANOVA for Large Number of Treatments. - 3. Pollutant Dispersion Simulation by Means of a Stochastic Particle Model and a Dynamic Gaussian Plume Model. - 4. On an Alternative Trigonometric Strategy for Statistical Modeling. - 
    Part II Design of Experiments.
    - 5. Incremental Construction of Nested Designs Based on Two-Level Fractional Factorial Designs. - 6. A Study of
    L
    -Optimal Designs for the Two-Dimensional Exponential Model. - 7. Testing for Randomized Block Single-Case Designs by Combined Permutation Tests with Multivariate Mixed Data. - 8. Adaptive Design Criteria Motivated by a Plug-In Percentile Estimator. - 
    Part III Queueing and Inventory Analysis.
    - 9. On a Parametric Estimation for a Convolution of Exponential Densities. - 10. Statistical Estimation with a Known Quantile and Its Application in a Modified ABC-XYZ Analysis. - 
    Part IV Machine Learning and Applications.
    - 11. A Study of Design of Experiments and Machine Learning Methods to Improve Fault Detection Algorithms. - 12. Microstructure Image Segmentation Using Patch-Based Clustering Approach. - 13. Clustering and Symptom Analysis in Binary Data with Application. - 14. Big Data for Credit Risk Analysis: Efficient Machine Learning Models Using PySpark.