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This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.

This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a full chapter for this book in order to disseminate the findings and promote further research collaborations in this area. This timely book offers new methods that impact advanced statistical model development in big-data sciences.

In the present study, a numerically based methodology for determining the load bearing capacity of particulate reinforced metal matrix composites (PRMMCs) under both monotonic and cyclic loadings is presented. A multi scale approach which combines the shakedown analysis with homogenization was used to study the material. To take into account the randomness of the material, statistical methods have been applied to interpret the results. To prepare a sufficient number of representative volume element (RVE) models for the statistical study, a computational tool was developed to automate the generation of RVE samples. The general work flow of the numerical approach can be summarized as follows: first, a large number of RVE was constructed as finite element models from either real or artificial material microstructures using the aforementioned in-house code. Next, limit and shakedown analyses were carried out by means of the interior-point method. Finally, results were converted to their corresponding macro quantities and evaluated statistically. With this approach a representative PRMMC material, WC/Co, was studied. Based on the established numerical work flow, ultimate strength and endurance limit of the material were predicted. The relationship between them and other material parameters was examined. The study investigated how the predicted strength is influenced by the RVE size and the size of reinforcement particles. The study also exposed the change of the feasible load domain, when the kinematic hardening of the binder phase is considered or when multiple independently varied loads are applied simultaneously. In addition to that, the study built predictive models to explain what are the decisive factors that determine the endurance limit of the material.