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Haskell A: Graphic Charts in Business:
How to Make and Use Them

Graphic Charts in Business: How to Make and Use Them

DK Essential Histories

The Story of Economics

Economics

The fourth edition of Cybercrime and Digital Forensics provides a concise, cohesive exploration of all forms of cybercrime, including cyberterrorism and cyberwarfare, in an international context. This new edition includes:<ul> <li> A detailed chapter-by-chapter exploration of specific forms of cybercrime, ranging from hacking and malware to fraud to various sexual and interpersonal crimes and even online illicit markets;</li> <li>An updated discussion of criminological theories;</li> <li>Up-to-date examinations of the laws pertaining to cybercrime across the world, inclusive of Australia, Canada, the EU, India, the UK, and the US;</li> <li>Explanations of generative Artificial Intelligence (AI), with examples in each chapter of the ways AI is facilitating cybercrime and transforming the landscape of cybercrime regulation;</li> <li>A detailed history of digital forensics, as well as the current processes of evidence collection and analysis;</li> <li>Examples of real-life cybercrime, cyberterror, and cyberwarfare incidents from around the world.</li> </ul> This text is essential reading for courses on cybercrime, cyber-deviance, digital forensics, cybercrime investigation, and the sociology of technology.

An Introduction to Theory, Policy, and Practice

Cybercrime and Digital Forensics

International Series in Operations Research & Management Science

This book presents a rigorous, practitioner-ready framework for determining when interventional causal claims can be validly drawn from observational data. By integrating potential outcomes (PO), structural causal models (SCMs), directed acyclic graphs (DAGs), quasi-experimental and longitudinal designs, information-theoretic approaches, and modern machine-learning methods with decision-analytic principles, it identifies necessary conditions for each step of the causal inference process, from defining estimands and identifying causal effects to study design, measurement, estimation, interpretation, robustness, and external validity. The framework is operationalized through practical checklists, tables, diagnostic and falsification tests, and AI-assisted tools, making it accessible across fields such as epidemiology, environmental and occupational health, and regulatory risk analysis. Designed to meet the needs of professionals making high-stakes decisions based on nonexperimental data, the book addresses the lack of clear, testable criteria for credible causal inference. Real-world examples throughout the book illustrate diagnostics, falsification tests, and robustness checks, linking evidence directly to decision-making. Bridging multiple causal frameworks and modern machine-learning methods, it equips readers to assess when data support cause-and-effect conclusions, supporting more responsible, evidence-based policy and risk management.The book offers health risk analysts, epidemiologists, and policy professionals a practical guide to valid causal inference from observational data. It is also ideal for graduate students and workshop participants in data science, decision analysis, risk analysis, and AI/ML applications.

Valid Causal Inference from Observational Data

The sixth edition of this leading reference book on insurance medicine provides a comprehensive guide to life expectancy for underwriters and clinicians involved in the life insurance industry. Extensively revised and expanded, the new edition reflects developments in life and healthcare insurance as well as medicine. Almost 20 years since it's last edition, each chapter&nbsp;is&nbsp;significantly updated to include the latest advancements in each disease, risk selection, and classification. Additionally, new content&nbsp;has been&nbsp;added to include rare disorders, the impact of climate and environmental changes on insurance medicine, screening for diseases and risk of disease, and critical illness. The impact of AI and other advanced technologies&nbsp;is&nbsp;also be discussed.&nbsp;

Brackenridge's Medical Selection of Life Risks

Access to Justice in a Changing World

Across Asia, courts are taking a variety of approaches to digital transformations. This book documents the diversity and complexity of the ways in which judiciaries are seeing the promise, as well as the perils, of digital transformations. Through in-depth case studies, the book brings together contributions from those with experience in the administration of technology in courts itself to those who are actively finding ways to develop technology as a tool for adjudication. It also includes reflections from those who are challenging the effects of technology in terms of court processes and arguing for the need for care and caution to ensure digital justice. The pieces take the form of institutional, policy, empirical studies that each analyse the non-linearity of digital transformations. In doing so, they offer practical pathways, as well as critical reflections, which bridge theory and practice, and highlight the uniqueness of Asia as a location for innovation, as well as critical reflection around the digital transformations of courts.

Exploring the Mechanics of Judicial Transformations

Digitalising Courts in Asia

Value Creation Lives in the System Beneath the Strategy The Value Creation Playbook is a systems-based guide for executives and leaders who need to deliver tangible value in high-pressure, complex environments. Tamara Milne, a value creation executive with decades of experience across healthcare, fintech, financial services, and insurance, delivers a systematic approach to identifying and driving long-term value across organizational assessment, process redesign, analytics, and technology alignment that applies across industries and company sizes. Readers will learn how to: <ul> <li> Diagnose an organization honestly and find where value is actually leaking</li> <li> Map the hidden power structure that shapes how decisions really get made</li> <li> Build communication systems that scale trust through the hardest phases of change</li> <li> Calibrate decision-making to the stakes and avoid the high cost of indecision</li> <li> Turn operational data into insight with the right analytics and governance</li> <li> Audit the technology backbone where data and handoffs quietly erode trust</li> <li> Capture the AI opportunity by building the operational foundation first</li> </ul> The Value Creation Playbook is a field-tested guide for every stage of the work, including the human, operations, and technology challenges most books never address.

A Step-By-Step Guide to Optimization, Growth, and Innovation

The Value Creation Playbook

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

Bayesian methods are now central to empirical macroeconomics, with routine use in academic research and at central banks for forecasting and policy analysis. As the models applied in practice have become increasingly high-dimensional and computationally intensive, a working understanding of the estimation algorithms-not only the models themselves-has become essential. While many existing texts treat Bayesian econometrics broadly, Bayesian Macroeconometrics focuses specifically on the models and methods used in modern Bayesian macroeconomic analysis, for readers who want both to understand these methods and to implement them.Bringing together modeling, posterior derivations, algorithmic design, and computation, the book derives each method step by step and integrates MATLAB code directly into the text. It pays particular attention to how posterior samplers are constructed in high-dimensional settings, where implementation choices can determine whether a method is practical for empirical work.A single idea ties the material together: linear regression as a recurring building block, with state space models and vector autoregressions treated as collections of linear regressions linked by latent states or dynamic dependence. From this foundation, the book moves to the models at the center of modern practice, including stochastic volatility, dynamic factor models, vector autoregressions, time-varying parameter VARs, and large VARs, alongside global-local shrinkage priors and Bayesian nonparametrics.Each chapter is anchored by worked examples using real macroeconomic data, closes with exercises, and is supported by a companion website providing datasets and code in MATLAB, Python, and R. The book is suitable as a primary text for graduate courses in Bayesian macroeconometrics and Bayesian time-series analysis, and as a reference for researchers and central-bank practitioners.Key Features:<ul> <li>Comprehensive coverage of Bayesian macroeconometric models - from basic Bayesian regressions and mixture models to linear Gaussian state space models, stochastic volatility models, and vector autoregressions.</li> <li>Emphasis on practical implementation - each model is illustrated with empirical applications using macroeconomic data and supported by reproducible code in MATLAB, Python, and R.</li> <li>Discussion of modern computation techniques - covering a range of advanced Markov chain Monte Carlo methods, including Hamiltonian Monte Carlo.</li> <li>Accessible style - technical results are derived in a step-by-step manner and are complemented by intuitive explanations and worked examples.</li> <li>Coverage of cutting-edge models and methods - including Dirichlet process mixtures, Gaussian process regressions, large VARs, and global-local priors.</li> </ul>

Methods and Applications

Bayesian Macroeconometrics

Routledge Critical Studies in Finance and Stability

Power, Interdependence and Diversity in Finance-led Capitalism

Global Regimes of Financialization

Contributions to Economics

Classical extreme value theory is the only mathematically justified framework for extrapolating into unobserved tail regions, but its standard tools were not designed for the high-dimensional, nonstationary, and causally interconnected systems that define modern risk. This book develops the necessary extensions, integrating foundational EVT with Bayesian nonparametrics, information theory, machine learning, and quantum probability across thirteen chapters in four parts.
The first part develops GEV and GPD foundations with complete measure-theoretic proofs. It extends max-stability to high dimensions using angular measure decompositions that scale to hundreds of risk factors. The second part covers nonparametric tail estimation with boundary bias correction, Bayesian Extreme Learning with entropy-regularized posteriors, Dirichlet process mixtures for heavy-tailed data, and the Extreme Value Information Criterion for tail-focused model selection. The third part introduces dynamic GEV state-space models with particle filtering for nonstationary extremes, threshold-weighted scoring rules for forecast evaluation and combination, Pareto-EVaR as a coherent risk measure that unifies GPD calibration with exponential moment constraints, and portfolio optimization under extremal transfer entropy constraints. The fourth part formalizes causal inference under regularly varying noise, develops tail-adaptive machine learning for extreme quantile estimation, and applies quantum density matrices and quantum copulas to systemic risk detection.
All theoretical results include complete proofs. Methods are implemented in R and Python with reproducible code tested on financial returns, temperature records, flood data, and cryptocurrency markets. The book serves researchers, graduate students, and advanced undergraduates in econometrics, finance, environmental science, and risk management.

Classical extreme value theory is the only mathematically justified framework for extrapolating into unobserved tail regions, but its standard tools were not designed for the high-dimensional, nonstationary, and causally interconnected systems that define modern risk. This book develops the necessary extensions, integrating foundational EVT with Bayesian nonparametrics, information theory, machine learning, and quantum probability across thirteen chapters in four parts.The first part develops GEV and GPD foundations with complete measure-theoretic proofs. It extends max-stability to high dimensions using angular measure decompositions that scale to hundreds of risk factors. The second part covers nonparametric tail estimation with boundary bias correction, Bayesian Extreme Learning with entropy-regularized posteriors, Dirichlet process mixtures for heavy-tailed data, and the Extreme Value Information Criterion for tail-focused model selection. The third part introduces dynamic GEV state-space models with particle filtering for nonstationary extremes, threshold-weighted scoring rules for forecast evaluation and combination, Pareto-EVaR as a coherent risk measure that unifies GPD calibration with exponential moment constraints, and portfolio optimization under extremal transfer entropy constraints. The fourth part formalizes causal inference under regularly varying noise, develops tail-adaptive machine learning for extreme quantile estimation, and applies quantum density matrices and quantum copulas to systemic risk detection.All theoretical results include complete proofs. Methods are implemented in R and Python with reproducible code tested on financial returns, temperature records, flood data, and cryptocurrency markets. The book serves researchers, graduate students, and advanced undergraduates in econometrics, finance, environmental science, and risk management.

Inference, Computation, and Forecasting

Contemporary Extreme Value Methods

Klausurenkurs für Studierende der Betriebswirtschaft im Bachelor

Statistik-Fallstudien mit Excel

Advanced Studies in Theoretical and Applied Econometrics

Professor Robin C. Sickles has been an Associate Editor of Empirical Economics for 20 years (2000–2019). Originally featured as a Special Issue in Empirical Economics in 2025, this book gathers esteemed econometricians to honor Sickles's influential work. This Festschrift celebrates his distinguished service to the journal and his lifelong accomplishments in academics. Professor Robin C. Sickles has made important contributions to many areas of econometrics research, including modeling productivity and efficiency, health and crime, and several areas of applied and empirical economics. Robin is an applied econometrician who focuses on the role that econometrics plays in policy issues, such as market regulation, market transition, and deterrence versus preventive measures in the criminal justice system. This Festschrift has both theoretical and applied contributions in econometrics on topics related to Robin’s work. These include panel data, treatment effects, proxy variables, semiparametric stochastic frontier models, social security disability applications, the impact of operational efficiency (OE) measures on green innovation, a randomly swapped Bootstrap for paired data, estimating flexible functional forms using macroeconomic data, the impact of measurement error on trends in earnings inequality in the USA, racial and ethnic differences in quitting when marijuana retail markets are legal, a spatial stochastic frontier model with a stochastic weighting matrix, sectoral trade elasticities and regional productivity in Europe, efficiency of Queensland public hospitals, homeownership decisions among migrants in China, and peer effects with sample selection in online job training.

Professor Robin C. Sickles has been an Associate Editor of Empirical Economics for 20 years (2000–2019). Originally featured as a Special Issue in Empirical Economics in 2025, this book gathers esteemed econometricians to honor Sickles's influential work. This Festschrift celebrates his distinguished service to the journal and his lifelong accomplishments in academics. Professor Robin C. Sickles has made important contributions to many areas of econometrics research, including modeling productivity and efficiency, health and crime, and several areas of applied and empirical economics. Robin is an applied econometrician who focuses on the role that econometrics plays in policy issues, such as market regulation, market transition, and deterrence versus preventive measures in the criminal justice system. This Festschrift has both theoretical and applied contributions in econometrics on topics related to Robin’s work. These include panel data, treatment effects, proxy variables, semiparametric stochastic frontier models, social security disability applications, the impact of operational efficiency (OE) measures on green innovation, a randomly swapped Bootstrap for paired data, estimating flexible functional forms using macroeconomic data, the impact of measurement error on trends in earnings inequality in the USA, racial and ethnic differences in quitting when marijuana retail markets are legal, a spatial stochastic frontier model with a stochastic weighting matrix, sectoral trade elasticities and regional productivity in Europe, efficiency of Queensland public hospitals, homeownership decisions among migrants in China, and peer effects with sample selection in online job training.

A Festschrift in Honor of Robin C. Sickles

Productivity, Panel Data and Applied Econometrics

Chapman and Hall/CRC Financial Mathematics Series

Introduction to Financial Derivatives with Python, Second Editon continues to provide an accessible introduction to derivatives and quantitative finance. Starting from first principles, the book develops the foundations of derivative pricing before progressing to numerical methods and advanced volatility models. Mathematical concepts are introduced progressively, allowing the reader to develop the necessary tools alongside their financial applications. Financial intuition, mathematical foundations, and Python implementation are integrated throughout the book.The book covers the essential topics in derivative pricing and introduces numerical methods widely used in quantitative finance. It also develops advanced volatility models, including CEV, local volatility, Heston, and SABR.Features<ul> <li>Suitable for undergraduate and graduate students, as well as practitioners and anyone seeking an accessible introduction to quantitative finance</li> <li>Covers derivative pricing from fundamental principles to advanced volatility models</li> <li>Introduces numerical pricing techniques, including binomial trees and Monte Carlo simulation</li> <li>Provides chapter summaries, exercises, and examination material</li> <li>Accompanied by a GitHub repository containing the Python code used throughout the book</li> <li>No prior programming experience is required; introductions to Python and coding are provided.</li> </ul>New to the Second Edition<ul> <li>Fresh material on the Bachelier model and normal implied volatility</li> <li>A new chapter on local volatility covers the motivation for local volatility modelling, the CEV model, and Dupire's formula</li> <li>A new chapter on stochastic volatility develops the Heston and SABR models</li> <li>Python implementations to help the reader understand the concepts presented</li> <li>A new appendix including sample exams. This allows readers to practice the concepts learned throughout the book.</li> </ul>

Introduction to Financial Derivatives with Python

Graphic Charts in Business: How to Make and Use Them

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