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Catalogue complet

Starnes D: The Practice of Statistics
for the AP® Course

The Practice of Statistics for the AP® Course

The Instant New York Times Bestseller | With a New Preface from Nate Silver for 2025 New York Times Book Review Paperback Row selection “Engaging and entertaining . . . a glimpse of the economy of the future.” —Tim Wu, New York Times Book Review From the New York Times bestselling author of The Signal and the Noise, the definitive guide to our era of risk—and the players raising the stakes In a world wired for chaos, these players are rewriting the rules. High-stakes, high-IQ, and often high on their own mythologies, they are driving the next era of finance, tech, and politics. But what happens when their bets go too far? Nate Silver’s On The Edge reveals the hidden world of the River. It is the domain of gamblers and like-minded folks who move markets and change the fabric of society: poker legends, hedge fund titans, crypto speculators, and even those willing to bet the world’s future on AI. They are obsessives with a deep hunger for volatility and an unrelenting desire to exploit every edge over the rest of us. Silver embeds with them, competing in the World Series of Poker, visiting Sam Bankman-Fried’s FTX compound, and attending wild Miami yacht parties at the height of the crypto bubble. On the Edge is a front-row seat to a new world order built on risk, math, and ambition—a gripping ride through the minds shaping your future, whether you like it or not.

The Instant New York Times Bestseller | With a New Preface from Nate Silver for 2025 New York Times Book Review Paperback Row selection “Engaging and entertaining . . . a glimpse of the economy of the future.” —Tim Wu, New York Times Book Review From the New York Times bestselling author of The Signal and the Noise, the definitive guide to our era of risk—and the players raising the stakes In a world wired for chaos, these players are rewriting the rules. High-stakes, high-IQ, and often high on their own mythologies, they are driving the next era of finance, tech, and politics. But what happens when their bets go too far? Nate Silver’s On The Edge reveals the hidden world of the River. It is the domain of gamblers and like-minded folks who move markets and change the fabric of society: poker legends, hedge fund titans, crypto speculators, and even those willing to bet the world’s future on AI. They are obsessives with a deep hunger for volatility and an unrelenting desire to exploit every edge over the rest of us. Silver embeds with them, competing in the World Series of Poker, visiting Sam Bankman-Fried’s FTX compound, and attending wild Miami yacht parties at the height of the crypto bubble. On the Edge is a front-row seat to a new world order built on risk, math, and ambition—a gripping ride through the minds shaping your future, whether you like it or not.

The Art of Risking Everything

On the Edge

Chapman and Hall/CRC Financial Mathematics Series

Sustainable Financial Structured Products: Managing Risk Appetite across Modelling, Structuring, and Hedging gives an overview of all relevant aspects of Financial Structured Products (i.e. long-term savings products) from the risk perspective of both issuers (banks, insurers) and customers. It contains technical details associated with both practice and theory, specifically related to modelling, product design, investments, and risk management challenges and solutions, tailored to both savings institutions and customer perspectives.<ul> <li>Offers detailed theoretical background alongside concrete, cutting-edge "quick wins" across strategic and operational business axes</li> <li>Practical tools for professionals in the banking industry, as well as great teaching/learning resources for students</li> <li>Highlights operational challenges across modelling, product designs and hedging.</li> </ul>This book is aimed at practicing risk managers and associated professionals but can also be a useful teaching resource for courses in risk management, financial structured products, and other related topics.

Managing Risk Appetites across Modelling, Structuring, and Hedging

Sustainable Financial Structured Products

Prescriptions for Quant Traders Using R is a practical, hands-on guide for turning data into trading decisions. This book is written for quantitative traders, financial analysts, and data scientists who want more than theory. It delivers step-by-step "prescriptions" using R-script-driven tasks that solve real-world trading problems.

Videos & Scripts

Prescriptions for Quant Traders Using R

A foundational treatment of discrete choice models, with a focus on random utility models Discrete choice models are essential tools for understanding decision-making when individuals must choose among alternatives. They have applications across the social sciences, notably in economics, marketing, and political science. This book offers a foundational treatment of discrete choice models, introducing the logit model and its generalizations, logistic and Poisson regressions, and generalized linear models, and demonstrates their use in analyzing important econometric models. These include international trade gravity, demand estimation, matching with and without transfers, hedonic markets, and dynamic discrete choice. Bridging theoretical clarity and practical applicability, the book is suitable for use in graduate-level coursework and will be an essential resource for researchers and practitioners. • Extensive coverage of computational issues, focusing on optimization and the reformulation as generalized linear models • Emphasis on econometric questions, including simulation, estimation, and inference methods, with estimation techniques based on both simulated and actual datasets • A substantial set of exercises and problems at the end of each chapter • Two appendixes, with one covering the mathematical tools needed to understand the material, and the other the Python code examples

Mathematical Methods, Econometrics, and Data Science

Discrete Choice Models

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

Security, Audit and Leadership Series

This book explores the deep impact of AI and digital transformation on finance, its practices and decision intelligence processes. The book and its practical, analytical chapters present further insights regarding ways to effectively utilize AI, potentially even beyond the existing theoretical and practical knowledge. Regulatory, ethical, security and moral challenges and concerns associated with the use of AI in finance are thoroughly analyzed.Readers will take away a conceptual and practical understanding of AI and digitalization as well as real-world practical case studies, so that they can be applied appropriately in a workplace setting. It aims to differentiate itself by focusing more specifically on the practical realities of AI and digital transformation in decision-making processes within finance, offering actionable guidance and real-world case study or implications, which are less emphasized in most other competitors.The book is appropriate for experts and professionals interested in deployment of AI, digitalization and their implementation in finance. No doubt, academics, professionals, students and finance experts will find this book an informative practice resource. The book would suit anyone interested in AI and its broader applications in finance or its uses in decision-making processes.

AI-Driven Decision-Making in Finance

Chapman and Hall/CRC Series on Statistics in Business and Economics

Introduction to Bayesian Econometrics: A GUIded Toolkit Using R offers a practical, conceptually clear, and computationally accessible pathway into Bayesian data analysis. Designed for readers who wish to apply Bayesian methods without necessarily investing years in programming, the book combines rigorous treatment of foundational ideas with a graphical user interface (GUI) that allows users to run Bayesian regression models in a user-friendly environment.The first part develops the mathematical foundations of Bayesian inference by presenting all derivations step-by-step. This transparent treatment of conjugate models, including posterior analysis, marginal likelihoods, and posterior predictive distributions, provides readers with a strong theoretical base for the more advanced material that follows.The second part focuses on implementation. It introduces the custom GUI for readers with little or no programming experience, demonstrates how to fit Bayesian models using established R packages, and guides more advanced users through programming key components of Bayesian samplers from scratch. This integrated approach enables readers with different backgrounds to engage with Bayesian methods at their preferred level of computational depth.The third part extends the framework to modern Bayesian econometrics. It covers Bayesian machine learning, causal inference, and approximate methods, illustrating how Bayesian ideas can be applied to contemporary empirical challenges. By combining theory, software, and hands-on computation, the book provides a comprehensive entry point into both classical and modern Bayesian analysis.Across all parts, the book is designed to support a wide range of users -beginners, intermediate programmers, and advanced learners-. To the best of the author's knowledge, no existing text combines mathematical transparency, software accessibility, and modern Bayesian topics in a single, integrated resource.

A GUIded Toolkit using R

Introduction to Bayesian Econometrics

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

Derivatives: Mathematical Foundations for Finance Students is written for students of finance and actuarial science who want to understand derivatives not just as formulas but as ideas that make sense. The goal is to build the mathematical foundations up step by step - starting from the familiar territory of first-year undergraduate probability and statistics and moving all the way to stochastic calculus and the principles of risk-neutral pricing, which form the heart of modern financial mathematics.This text will enable the reader to:¿ understand models of asset price behavior and apply stochastic calculus ¿ use stochastic simulations in R to investigate processes and price options ¿ price options with the binomial model ¿ derive solutions to the Black-Scholes equation using risk-neutral pricing ¿ price interest-rate derivatives using Black's model, short-rate models and the Heath-Jarrow-Morton (HJM) framework ¿ tackle quantitative finance exam questions.It incorporates a range of learning features to aid student understanding, including boxed examples, end-of-chapter summaries, selected questions from Society of Actuaries (US) Quantitative Finance examinations and further reading suggestions. The book is also supported by a suite of digital learning resources, including PowerPoint slides, multiple choice questions, instructor manual/advice document for lecturers and a test bank.This book will appeal to both undergraduate and postgraduate students who wish to understand the principles of stochastic calculus and option pricing.

Mathematical Foundations for Finance Students

Derivatives

Integrating Statistical and Management Methods of Quality, Fourth Edition

A First Course in Quality Engineering

The book presents real-life applications of data science, artificial intelligence, and big data for the Sustainable Development Goals. It includes a list of case studies from different states across India. The case studies in the book use both structured and unstructured data like numeric data, textual data, and video/image data across the various chapters for analysis. The book chapters delve into various aspects of data science starting from on ground data collection to dashboard based reporting, unsupervised methods like clustering for grouping data points, use of artificial intelligence, machine learning and deep learning, search or information retrieval, time series forecasting, and optimization.¿ It showcases data science decision-making processes, driving innovation, and solving complex problems in real-life scenarios across sectors like finance, healthcare, and e-commerce.¿ The SDGs provide a framework for societal development and well-being for all; the data science and big data interventions in this book are aligned towards mapping the various SDGs.¿ Most of the data science use cases and initiative projects covered in this book have been implemented by the central or state governments across different states of India.¿ Shows how data science intervention can transform the social sector, potentially driving positive change and addressing critical societal challenges.¿ Explained the fundamentals of data science theories, including concepts like classification, regression, predictive analytics, optimization, artificial intelligence, deep learning, and time series forecasting for readers of different disciplines.It serves as a valuable reference for graduate students, researchers, and scholars seeking to deepen their knowledge and engage with real life applications of data science. It will also serve as a valuable resource for government officers and policy practitioners, providing a range of cases on the use of data-based methods for improving governance and policy making.The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC BY-NC-ND)] 4.0 license funded by by the Great Lakes Institute of Management, Gurgaon Campus, Delhi NCR, India.

Indian Case Studies

Data Science for Sustainable Development Goals

Routledge Revivals

First published in 1972, in Descriptive Statistics and Probability Theory the numerical work- the selection of numerical data-is used as a basis for developing the statistical theory. Other starting points could have been chosen but a beginner will readily understand this sort of work and hence be able to 'get the feel of' statistics, untroubled by the learning of fresh concepts.From this numerical work emerges the 'need' to introduce counting techniques, permutations, combinations and the binomial theorem. In a similar way the necessity for probability is shown and the basic ideas are developed intuitively. A final short chapter shows how these ideas can be formalised within an axiomatic system. In all cases, simple intuitive ideas are taken as a starting point and then discussion leads to the final formalization. As concepts are introduced the language of statistics is developed. A large number of examples is used and they are all worked in full detail. Occasionally the reader is placed in a problem situation and invited to attempt his own individual solution before reading on to match his own attempts to other solutions. It is hoped that motivation of this sort will lead the reader to develop greater sensitivity than would otherwise be the case. This is an important read for students of statistics, mathematics and economics.

Descriptive Statistics and Probability Theory

The Practice of Statistics for the AP® Course

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