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Catalogue complet

Gelman Andrew: Bayesian Workflow

Bayesian Workflow

This book provides the first comprehensive comparative and empirical analysis of the state of harmonisation in EU copyright law in the 27 Member States, and the UK, at the level of national courts. For 3 decades, the EU has harmonised many aspects of copyright law via EU legislation and the case law of the Court of Justice of the EU (CJEU). However, it is not known whether national courts actually comply with it, as a comprehensive analysis of the national case law has not yet been done. This book addresses this major gap in the literature. In the book, a lawyer and a statistician analyse some of the most recent decisions on EU copyright law issued by the 27 Member States and the UK (pre- and post-Brexit), using doctrinal and quantitative methodologies. The main research question addressed is whether there is disharmony in the national case law and whether this is owed to Members States' misimplementing EU copyright legislation, lack of clarity of EU legislation and/or case law, or national courts being unaware, misinterpreting or resisting CJEU case law. The book provides detailed legal analyses and descriptive statistics per topic, per type of work, per country, across countries and over time supported by statistical analysis. Its findings and in-depth reflections on the law and how to improve it are of crucial relevance for policymakers and the judiciary at EU and national level and will interest scholarly audiences in the UK, EU, EEA and beyond.

An Empirical Analysis of National Courts Case Law

EU Copyright Law Harmonisation

In today's data-driven business world, organizations struggle to extract actionable insights from vast amounts of information. This textbook bridges theory and practice, offering structured approaches to leverage data for enhanced decision-making through machine learning, AI, and optimization modeling-transforming raw data into competitive business intelligence.Decision Making and Analytics: Data-Driven Business Intelligence and Optimization features advanced analytical methodologies paired with real-world implementation studies, creating a progressive learning path from customer behavior analysis through hybrid decision models. By integrating fuzzy logic, neural networks, text mining, and reinforcement learning, it provides a comprehensive toolkit applicable across business scenarios, turning abstract concepts into practical solutions.Serving multiple audiences, this resource bridges the gap between classroom theory and industry practice for students in industrial engineering, business analytics, and management information systems. Practitioners gain applicable methodologies to improve organizational decision-making. At the same time, researchers benefit from cutting-edge approaches across various domains, and policymakers can develop data-informed strategies-addressing the growing demand for professionals who translate data into strategic decisions.The textbook employs open-ended questions that develop analytical reasoning skills without predetermined solutions, better preparing students for real-world complexity. Supporting materials include high-quality figure slides, as well as PowerPoint slides, for qualified adopters, enabling engaging learning environments that effectively communicate complex concepts while building practical, data-driven decision-making skills.

Data-Driven Business Intelligence and Optimization

Decision-Making and Analytics

This concise, practical guide demystifies experimental methodology for students embarking on consumer research for the first time. The book follows the natural progression of conducting experimental research-from formulating research questions and building theoretical frameworks to designing experiments, analyzing results, and writing up findings. Each chapter combines scientific background with practical examples and hands-on advice, including helpful checklists for each stage of the research process. Unlike traditional textbooks that can overwhelm novice researchers with excessive detail, this guide addresses common challenges faced by students and provides clear explanations of complex concepts such as mediation, moderation, and experimental design in accessible language, without sacrificing academic rigor. This book is essential reading for undergraduate and graduate students undertaking experimental research projects or theses in consumer behavior, marketing, and other social sciences. It serves as an "academic companion" for students who need practical guidance through the experimental process. Faculty members supervising experimental research will also find it valuable as a reference guide, particularly those less familiar with experimental methodology.

A Beginner's Guide to Experiments in Consumer Research

Chapman & Hall/CRC The R Series

Work Automation with R

Chapman & Hall/CRC Texts in Statistical Science

The field of time series analysis has undergone a remarkable transformation since the publication of the seventh edition of this book. While classical statistical models such as ARIMA, state-space models, and spectral methods remain essential, the rise of artificial intelligence (AI) has introduced groundbreaking approaches to modeling, forecasting, and generating time-dependent data. This eighth edition reflects these advancements with the addition of two new chapters: Predictive AI for Time Series and Generative AI for Time Series. These chapters bridge the gap between traditional time series methods and cutting-edge AI techniques, offering readers a comprehensive and integrated perspective on the field.Features:· Comprehensive coverage of classical time series models, including ARIMA, state-space models, and spectral methods· Two new chapters on predictive and generative AI, introducing cutting-edge methods like transformers, variational autoencoders, and diffusion models· Practical examples and illustrations using R, demonstrating the application of both classical and AI-based approaches to real-world time series data· Emphasis on the integration of classical statistical rigor with the flexibility and scalability of AI methods· Clear explanations and intuitive insights, making advanced concepts accessible to a broad audience· Updated content reflecting the latest developments in time series analysis, with a focus on modern, high-dimensional, and nonlinear data challengesThis eighth edition is designed for students, researchers, and practitioners in statistics, as well as in finance, economics, climate science, health, and engineering. It serves as both a foundational text for those new to time series analysis and a valuable resource for experienced analysts seeking to engage with the rapidly evolving landscape of predictive and generative AI. With its balance of theory, practical implementation, and real-world examples, the book is ideal for use in academic courses, professional training, and self-study.

An Introduction with R

The Analysis of Time Series

Statistical skills are essential for analysing data and ensuring optimal business operation and efficiency. In this clear and concise textbook, Les Oakshott and Kathy Kotiadis guide students through key quantitative techniques, from basic maths and statistics through to advanced topics such as hypothesis testing and linear programming. Grounded in the everyday realities of business, this textbook introduces each chapter with an engaging case study to highlight how organisations operationalise these techniques, ranging from the growth of China's economy and a Covid-19 infection survey to goal scoring at Euro 2024 and the effect of global warming on the occurrence of salmonella in Singapore. New to this edition: -Links the UN Sustainable Development Goals to quantitative techniques -Greater emphasis on Excel to enable seamless transition to blended learning approaches -Walk-through videos of key processes hosted on the companion website This is the essential textbook for delivering quantitative methods and business statistics modules to students of mixed maths abilities across undergraduate and postgraduate levels. Accompanying online resources for this title can be found at bloomsburypub/essential-quantitative-methods-8e. These resources are designed to support teaching and learning at no extra cost.

for Business, Management and Finance

Essential Quantitative Methods

International Series on Actuarial Science

This comprehensive yet accessible guide to enterprise risk management for financial institutions contains all the tools needed to build and maintain an ERM framework. It discusses the internal and external contexts within which risk management must be carried out, and it covers a range of qualitative and quantitative techniques that can be used to identify, model and measure risks. This third edition has been thoroughly revised and updated to reflect new regulations and legislation. It includes additional detail on machine learning, a new section on vine copulas, and significantly expanded information on sustainability. A range of new case studies include Theranos and FTX. Suitable as a course book or for self-study, this book forms part of the core reading for the Institute and Faculty of Actuaries' examination in enterprise risk management.

Financial Enterprise Risk Management

Now in its 6th edition, this textbook for undergraduate, postgraduate and doctoral students offers a succinct and user-friendly guide to the research process, from identifying a research topic to writing up and disseminating the research. The step-by-step guidance, examples and clear writing style help students embark on and successfully complete their research projects. With a balance of practical advice, methodological approach and sound academic underpinning, the book provides a comprehensive grounding in research methods. Chapters are interspersed with key definitions and Vox Pops provide examples of students' perspectives at different stages of their research journeys. A key feature is the troubleshooting chapter, which gives easy access to advice on the common issues and dilemmas faced by students. New to this edition: - Guidance on using online search methods and Artificial Intelligence-assisted tools, together with the associated ethical implications. - Extended coverage of qualitative data analysis, including how to use data analysis software and how to display categories/codes and models when writing up the research. - Fully updated and revised statistical analysis chapters using SPSS (with duplicate versions using jamovi hosted on the companion website), together with additional advice on how to describe the sample and present the statistical results. Accompanying online resources for this title can be found at bloomsbury.pub/business_research_6e. These resources are designed to support teaching and learning and are available at no extra cost.

A Practical Guide for Students

Collis and Hussey's Business Research

This publication addresses copula approaches to competing risks models for dependent latent failure times. It will establish the fundamentals on dependent competing risks models, as well as a range of recent developments to identifiability and estimation using copulas. Readers will learn from the presentation of theory, in relation to its applications in different subject areas in economics, engineering, and medicine. The comprehensive knowledge provided in this book gives readers an overview of existing models for dependent competing risks, which are not found in other books.The book covers basic statistical ideas, such as "parametric latent failure time models", theoretical issues, such as "identifiability of the competing risks model", and model comparisons, such as comparative studies for "different copulas and different models for hazard functions". Aside from the introductory materials, the authors cover new and recently developed methodologies. The new solutions address the identifiability problems in competing risks models that rely on copula modelling. Copula models and other competing risks models are presented in unified theoretical framework and illustrated with data from various disciplines (such as economics, engineering, and medicine). The book also includes applications with real data and the code (written by R and/or Stata) will be made available for easy practice purposes.A selection of exercises and their solutions will be available online. This book serves as a good reading for senior undergraduates and postgraduate students studying in such courses as "Multivariate Survival Analysis", "Survival Data Analysis" or "Advanced Econometrics". This book would also serve as a useful reference for empirical researchers in a wide range of disciplines.

Theory and Applications in Economics, Engineering, Medicine

Copula Models for Dependent Competing Risks

Business Statistics of the United States is a comprehensive and practical collection of data from as early as 1913 that reflects the nation's economic performance. It provides several years of annual, quarterly, and monthly data in industrial and demographic detail, including key indicators such as: gross domestic product, personal income, spending, saving, employment, unemployment, the capital stock, and more. Business Statistics of the United States is the best place to find historical perspectives on the U.S. economy. Of equal importance to the data are the introductory highlights, extensive notes, and figures for each chapter that help users to understand the data, use them appropriately, and, if desired, seek additional information from the source agencies. Business Statistics of the United States provides a rich and deep picture of the American economy and contains approximately 3,500 time series. The data are predominantly from federal government sources, including: ·Board of Governors of the Federal Reserve System ·Bureau of Economic Analysis ·Bureau of Labor Statistics ·Census Bureau ·Employment and Training Administration ·Energy Information Administration ·Federal Housing Finance Agency ·Department of the Treasury

Patterns of Economic Change

Business Statistics of the United States 2026

A History of Risk, Reward, and Delusion on Wall Street

The Myth of the Rational Market

Bayesian Workflow

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