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