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Full Description
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.
Contents
- 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.
