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.