Über uns

À propos de nous

Nachhaltigkeit

Durabilité

Open Access

Karriere

Carrière

Datenschutz

Protection des données

Impressum

Mentions légales

Zahlung und Lieferung

Paiement et livraison

LEXIA

LEXIA Arbeitsrecht

Was ist LEXIA?

C'est quoi LEXIA ?

LEXIA Familienrecht

Zeitschriften

Revues

recht

Pflegerecht

ZBJV

forumpoenale

ZStrR

Anwaltsrevue

Revue de l'avocat

RSAS

FamPra.ch

ASYL

Life Science Recht

BlAR

Sachbuch

Livres spécialisés

LOKI

Semaphor

Fokusthemen

Focus

Thema

Recht studieren

Arbeitsrecht

Droit du travail

Studium

Études

Familienrecht

Droit de la famille

Sozialversicherungsrecht

Droit des assurances sociales

Strafrecht

Droit pénal

Publikationen

Publications

Literatur

Onlinemodule LEXIA

Modules en ligne LEXIA

Bücher

Livres

Buchreihen

Collections

Datenbanken

recht.ch

Studienliteratur

Littérature pour les études

Services

Dienstleistungen für

Corporate Books

Publishing Lösungen

Aktuelles

Geschäftskunden

Professionnels

Corporate Publishing

Content solutions

Buchhandlungen

Librairies

Veranstaltungen

Événements

Presse

Dozierende

Enseignants

Studierende

Étudiants

Inserierende

Annonceurs

Newsletter

Blog

Autorinnen/Autoren

Auteurs

Unsere Schreibenden

Hilfe

Unsere Autorinnen und Autoren

Liste

Autorenlounge

Espace des auteurs

Bei uns publizieren

Publier chez nous

Autorenstimmen

Témoignages

Verlag

Éditions

Themen

Mehr wissen

Mitarbeitende

Collaborateurs

Geschichte

Histoire

Vision und Mission

Vision et mission

Unser Magazin

Notre magazine

Unternehmenskultur

Culture d'entreprise

Partner

Partenaires

Kontakt

Contact

Shop

Schweizer Recht

Ausländisches Recht

Über unseren Shop

Neuerscheinungen

Nouveautés

Unsere Funktionen

Nos fonctions

Vorankündigungen

À paraître

Nach Rechtsgebiet

Par domaine juridique

Kommentare

Commentaires

Gesamtkatalog

Catalogue complet

Gelman Andrew: Bayesian Workflow

Bayesian Workflow

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

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 behaviour 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.Derivatives: Mathematical Foundations for Finance Students 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

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

Probability and Statistics for Engineering and the Sciences, Loose-Leaf Version

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

The classic bestseller that changed how the world thinks about football'Quite magnificent - a sort of Freakanomicsof football.' Jonathan Wilson, Guardian'If you're a football fan, I'll save you some time: read this book' Daily Telegraph__________Football truly is the world's favourite game, followed in over 200 countries by hundreds of millions of people pouring their hearts and souls into supporting their chosen team every week.But behind the passion are questions that all true football fans want answered: has football spending spun out of control? How much do managers matter? Is hosting a World Cup a poisoned chalice?Soccernomicschallenges the conventional wisdom to reveal the surprising truths that actually determine success and failure on the pitch. From an acclaimed economist and leading sports journalist, this modern classic uses data and economic principles to explore the myths that dominate the game.Fully revised and updated ahead of the 2026 World Cup, Soccernomics takes you behind the passion and into the patterns - revealing why your team wins, why it loses, and why the game you love doesn't always work the way you think it does.__________Soccernomicsis essential reading for anyone in search of a more strategic, systematic perspective on the game, answering the questions that most consume football fans.

Why England Has Started to Win, Why Clubs Sign the Wrong Players and Why Billionaires Still Lose Money in Football

Soccernomics

Routledge-Giappichelli Studies in Business and Management

From Basic Growth Models to Computational Methods

Dynamic Programming for Economists

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

This book provides fundamental concepts and algorithms of the Hidden Markov Model (HMM) and its applications in finance, such as stock price predictions, and other areas such as speech recognition. Their wide range uses make HMMs very attractive to researchers in both academia and industry. Only a basic knowledge of probability, statistics, and programming is necessary, and readers will learn the concepts and algorithms of the HMM through definitions, real-life examples, and R code.Key Features:<ul> <li>A comprehensive introduction to the concepts and algorithms of Hidden Markov Models (HMMs)</li> <li>Real-world examples that can be worked through using a calculator or R</li> <li>Applications across disciplines, including finance, bioinformatics, and speech recognition</li> <li>Fully annotated R code for hands-on learning and practical implementation</li> </ul>

Hidden Markov Models in Finance and Other Applications

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

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

A Generalized Linear Models Approach

Count Time Series

Why do we use eighty-year-old metrics to understand today’s economy? The ways that statisticians and governments measure the economy were developed in the 1940s, when the urgent economic problems were entirely different from those of today. In The Measure of Progress, Diane Coyle argues that the framework underpinning today’s economic statistics is so outdated that it functions as a distorting lens, or even a set of blinkers. When policymakers rely on such an antiquated conceptual tool, how can they measure, understand, and respond with any precision to what is happening in today’s digital economy? Coyle makes the case for a new framework, one that takes into consideration current economic realities. Coyle explains why economic statistics matter. They are essential for guiding better economic policies; they involve questions of freedom, justice, life, and death. Governments use statistics that affect people’s lives in ways large and small. The metrics for economic growth were developed when a lack of physical rather than natural capital was the binding constraint on growth, intangible value was less important, and the pressing economic policy challenge was managing demand rather than supply. Today’s challenges are different. Growth in living standards in rich economies has slowed, despite remarkable innovation, particularly in digital technologies. As a result, politics is contentious and democracy strained. Coyle argues that to understand the current economy, we need different data collected in a different framework of categories and definitions, and she offers some suggestions about what this would entail. Only with a new approach to measurement will we be able to achieve the right kind of growth for the benefit of all.

Counting What Really Matters

The Measure of Progress

Bayesian Workflow

Autres titres sur ce thème