This book provides an account of weak convergence theory, empirical processes, and their application to a wide variety of problems in statistics. The first part of the book presents a thorough treatment of stochastic convergence in its various forms. Part 2 brings together the theory of empirical processes in a form accessible to statisticians and probabilists. In Part 3, the authors cover a range of applications in statistics including rates of convergence of estimators; limit theorems for M− and Z−estimators; the bootstrap; the functional delta-method and semiparametric estimation. Most of the chapters conclude with “problems and complements.� Some of these are exercises to help the reader’s understanding of the material, whereas others are intended to supplement the text.  This second edition includes many of the new developments in the field since publication of the first edition in 1996: Glivenko-Cantelli preservation theorems; new bounds on expectations of suprema of empirical processes; new bounds on covering numbers for various function classes; generic chaining; definitive versions of concentration bounds; and new applications in statistics including penalized M-estimation, the lasso, classification, and support vector machines. The approximately 200 additional pages also round out classical subjects, including chapters on weak convergence in Skorokhod space, on stable convergence, and on processes based on pseudo-observations.

This book shares the theoretical advancements that have been made regarding psychological ownership since the development of the construct and specifically the practical applications within multi-cultural and cross-cultural environments. Enriched by empirical data and case studies by subject specialists in the field, this book serves as a cutting-edge benchmark for human resource management specialists, industrial psychologists, as well as students in positive organizational psychology and professionals in other fields. This book follows an in-depth view of the most recent research trends in psychological ownership. Offering practical tools of how the psychological ownership of employees could be developed in the workplace to not only enhance the performance of organisations, but to increase the commitment of employees and influence the intentions of skilled employees to remain with their organisations.

This book offers a range of approaches and specific examples of how a sample of internationally leading research-intensive universities, from a variety of regions around the world, work to improve teaching and learning. It describes and analyzes broad university initiatives and approaches that have the potential of driving institution-wide change processes in teaching and learning, thus providing a link between strategic ambitions and cultural transformation in the universities. Globally, research-intensive universities are increasingly pressured to increase their performance in both research and education. However, while much focus internationally has been devoted to how universities are working to boost their research performance, less is known about how internationally leading universities are working to improve teaching and learning. Through comparative cases drawn from universities in Europe, Asia and the US, key practices and lessons are identified and showcased providing a unique insight into the ways internationally leading research universities work to support and enhance staff engagement in teaching and learning. It will be essential reading for researchers and advanced students working in Higher Education and Sociology, particularly those with an interest in comparative studies.

Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics.

This book explores weak convergence theory and empirical processes and their applications to many applications in statistics. Part one reviews stochastic convergence in its various forms. Part two offers the theory of empirical processes in a form accessible to statisticians and probabilists. Part three covers a range of topics demonstrating the applicability of the theory to key questions such as measures of goodness of fit and the bootstrap.

A new turn in mixed methods research is here: merged methods. This provocative book offers a novel analysis of current mixed methods research, complicating traditional approaches and challenging existing techniques. Moving beyond the binary quantitative-qualitative distinction, the book presents methodologically grounded ways to merge methods in social research and integrate interpretive and structural approaches in one instrument or procedure. The book: Considers the importance of merging both epistemologies and methodologies. Showcases eight merged methods research approaches, from the Delphi method to multimodal content analysis. Explores the opportunities for merging methods using computational techniques, such as text mining. This innovative book is a must-read for any postgraduate student or researcher across the social sciences wanting to develop their understanding of mixed methods research.

This new volume of the long-established St. Flour Summer School of Probability includes the notes of the three major lecture courses by Erwin Bolthausen on "Large Deviations and Iterating Random Walks", by Edwin Perkins on "Dawson-Watanabe Superprocesses and Measure-Valued Diffusions", and by Aad van der Vaart on "Semiparametric Statistics".

Here is a practical and mathematically rigorous introduction to the field of asymptotic statistics. In addition to most of the standard topics of an asymptotics course--likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures--the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, one of the book's unifying themes that mainly entails the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation.

A new turn in mixed methods research is here: merged methods. This provocative book offers a novel analysis of current mixed methods research, complicating traditional approaches and challenging existing techniques. Moving beyond the binary quantitative-qualitative distinction, the book presents methodologically grounded ways to merge methods in social research and integrate interpretive and structural approaches in one instrument or procedure. The book: Considers the importance of merging both epistemologies and methodologies. Showcases eight merged methods research approaches, from the Delphi method to multimodal content analysis. Explores the opportunities for merging methods using computational techniques, such as text mining. This innovative book is a must-read for any postgraduate student or researcher across the social sciences wanting to develop their understanding of mixed methods research.

Statistics is the science that focuses on drawing conclusions from data, by modeling and analyzing the data using probabilistic models. In An Introduction to Mathematical Statistics the authors describe key concepts from statistics and give a mathematical basis for important statistical methods. Much attention is paid to the sound application of those methods to data. The three main topics in statistics are estimators, tests, and confidence regions. The authors illustrate these in many examples, with a separate chapter on regression models, including linear regression and analysis of variance. They also discuss the optimality of estimators and tests, as well as the selection of the best-fitting model. Each chapter ends with a case study in which the described statistical methods are applied. This book assumes a basic knowledge of probability theory, calculus, and linear algebra. Several annexes are available for Mathematical Statistics on this page.

This volume provides a multi-disciplinary perspective on grit, its measurement, manifestation and development. Specifically, it provides a comprehensive and balanced response to critiques associated with the construct within the contemporary positive psychological literature. These critiques revolve around the lack of consensus in the conceptualisation, measurement, and management of grit, as well as consensus on its difference from other psychological constructs such as conscientiousness, diligence or determination. Therefore, this volume thoroughly reappraises and consolidates the nature, function, measurement and implications of grit in order to effectively advance the science of achievement. It looks at grit scales developed in various countries and evaluates the concept in various aspects of life, from work performance to sports. Written by a team of multi-disciplinary experts in fields ranging from neuroscience, sociology, and education to human resource management and psychology, this volume firmly positions grit within the discipline of positive psychology's nomological lexicon.