1. Introduction 2. Decision theory 3. Bayesian methods 4. Hypothesis testing 5. Special models 6. Sufficiency and completeness 7. Two-sided tests and conditional inference 8. Likelihood theory 9. Higher-order theory 10. Predictive inference 11. Bootstrap methods.

Advances in scientific computing have made modelling and simulation an important part of the decision-making process in engineering, science, and public policy. This book provides a comprehensive and systematic development of the basic concepts, principles, and procedures for verification and validation of models and simulations. The emphasis is placed on models that are described by partial differential and integral equations and the simulations that result from their numerical solution. The methods described can be applied to a wide range of technical fields, from the physical sciences, engineering and technology and industry, through to environmental regulations and safety, product and plant safety, financial investing, and governmental regulations. This book will be genuinely welcomed by researchers, practitioners, and decision makers in a broad range of fields, who seek to improve the credibility and reliability of simulation results. It will also be appropriate either for university courses or for independent study.

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Essentials of statistical inference

Verification and Validation in Scientific Computing

Preface 1. Preliminaries 2. Some concepts and simple applications 3. Significance tests 4. More complicated situations 5. Some interpretational issues 6. Asymptotic theory 7. Further aspects of maximum likelihood 8. Additional objectives 9. Randomization-based analysis Appendix A. A brief history Appendix B. A personal view References Author index Index.

/pdf/principles-of-statistical-inference-1nzki2o1oo.pdf

Principles of Statistical Inference

Tree Boosting With XGBoost - Why Does XGBoost Win "Every" Machine Learning Competition?

With few exceptions, statistical analysis of social networks is currently focused on cross-sectional or panel data On the other hand, automated collection of network-data often produces event data, ie, data encoding the exact time of interaction between social actors In this paper we propose models and methods to analyze such networks of dyadic events and to determine the factors that influence the frequency and quality of interaction We apply our methods to empirical datasets about political conflicts and test several hypotheses concerning reciprocity and structural balance theory

Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data

Data, which lie in the space $\mathcal {P}_{m\,}$ , of $m \times m$ symmetric positive definite matrices, (sometimes called tensor data ), play a fundamental role in applications, including medical imaging, computer vision, and radar signal processing. An open challenge, for these applications, is to find a class of probability distributions, which is able to capture the statistical properties of data in $\mathcal {P}_{m\,}$ , as they arise in real-world situations. The present paper meets this challenge by introducing Riemannian Gaussian distributions on $\mathcal {P}_{m\,}$ . Distributions of this kind were first considered by Pennec in 2006. However, the present paper gives an exact expression of their probability density function for the first time in existing literature. This leads to two original contributions. First, a detailed study of statistical inference for Riemannian Gaussian distributions, uncovering the connection between the maximum likelihood estimation and the concept of Riemannian centre of mass, widely used in applications. Second, the derivation and the implementation of an expectation-maximisation algorithm, for the estimation of mixtures of Riemannian Gaussian distributions. The paper applies this new algorithm, to the classification of data in $\mathcal {P}_{m\,}$ , (concretely, to the problem of texture classification, in computer vision), showing that it yields significantly better performance, in comparison to recent approaches.

https://hal.archives-ouvertes.fr/hal-01248573/document

Riemannian Gaussian Distributions on the Space of Symmetric Positive Definite Matrices

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Essentials of statistical inference

Citations

Verification and Validation in Scientific Computing

Principles of Statistical Inference

Tree Boosting With XGBoost - Why Does XGBoost Win "Every" Machine Learning Competition?

Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data

Riemannian Gaussian Distributions on the Space of Symmetric Positive Definite Matrices

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