- Table View
- List View
Quantitative Corporate Finance
by John B. Guerard Jr. Anureet Saxena Mustafa N. GültekinThis textbook presents a comprehensive treatment of the legal arrangement of the corporation, the instruments and institutions through which capital can be raised, the management of the flow of funds through the individual firm, and the methods of dividing the risks and returns among the various contributors of funds.Now in its third edition, the book covers a wide range of topics in corporate finance, from time series modeling and regression analysis to multi-factor risk models and the Capital Asset Pricing Model. Guerard, Gultekin and Saxena build significantly on the first edition of the text, but retain the core chapters on cornerstone topics such as mergers and acquisitions, regulatory environments, bankruptcy and various other foundational concepts of corporate finance.New to the third edition are examinations of APT portfolio selection and time series modeling and forecasting through SAS, SCA and OxMetrics programming, FactSet fundamental data templates. This is intended to be a graduate-level textbook, and could be used as a primary text in upper level MBA and Financial Engineering courses, as well as a supplementary text for graduate courses in financial data analysis and financial investments.
Quantitative Data Analysis with SPSS 12 and 13: A Guide for Social Scientists
by Alan Bryman Duncan CramerThis new edition has been completely updated to accommodate the needs of users of SPSS Release 12 and 13 for Windows, whilst still being applicable to those using SPSS Release 11 and 10. Alan Bryman and Duncan Cramer provide a non-technical approach to quantitative data analysis and a user-friendly introduction to the widely used SPSS. No previous familiarity with computing or statistics is required to benefit from this step-by-step guide to techniques including: Non-parametric tests Correlation Simple and multiple regression Multivarate analysis of variance and covariance Factor analysis The authors discuss key issues facing the newcomer to research, such as how to decide which statistical procedure is suitable, and how to interpret the subsequent results. Each chapter contains worked examples to illustrate the points raised and ends with a comprehensive range of exercises which allow the reader to test their understanding of the topic. This new edition of this hugely successful textbook will guide the reader through the basics of quantitative data analysis and become an essential reference tool for both students and researchers in the social sciences. The datasets used in Quantitative Data Analysis for SPSS Release 12 and 13 are available online at www.psypress.com/brymancramer/ .
Quantitative Decisions in Drug Development (Springer Series in Pharmaceutical Statistics)
by Christy Chuang-Stein Simon KirbyThis book focuses on important decision points and evidence needed for making decisions at these points during the development of a new drug. It takes a holistic approach towards drug development by incorporating explicitly knowledge learned from the earlier part of the development and available historical information into decisions at later stages. In addition, the book shares lessons learned from several select examples published in the literature since the publication of the first edition. The second edition reiterates the need for making evidence-based Go/No Go decisions in drug development discussed in the first edition. It substantially expands several topics that have seen great advances since the publication of the first edition. The most noticeable additions include three adaptive trials conducted in recent years that offer excellent learning opportunities, the use of historical data in the design and analysis of clinical trials, and extending decision criteria to the cases when the primary endpoint is binary. The examples used to illustrate the additional materials all come from real trials with some post-trial reflections offered by the authors. The book begins with an overview of product development and regulatory approval pathways. It then discusses how to incorporate prior knowledge into study design and decision making at different stages of drug development. Prior knowledge includes information pertaining to historical controls. To assist decision making, the book discusses appropriate metrics and the formulation of go/no-go decisions for progressing a drug candidate to the next development stage. Using the concept of the positive predictive value in the field of diagnostics, the book leads readers to the assessment of the probability that an investigational product is effective given positive study outcomes. Lastly, the book points out common mistakes made by drug developers under the current drug-development paradigm. The book offers useful insights to statisticians, clinicians, regulatory affairs managers and decision-makers in the pharmaceutical industry who have a basic understanding of the drug-development process and the clinical trials conducted to support drug-marketing authorization. The authors provide software codes for select analytical approaches discussed in the book. The book includes enough technical details to allow statisticians to replicate the quantitative illustrations so that they can generate information to facilitate decision-making themselves.
Quantitative Demography and Health Estimates: Healthy Life Expectancy, Templates for Direct Estimates from Life Tables and other Applications (The Springer Series on Demographic Methods and Population Analysis #55)
by Christos H Skiadas Charilaos SkiadasThis book provides new theoretic and applied material with focus on quantitative methods and data analysis techniques applied in demography, population studies, health issues and statistics. It discusses the quantitative techniques to estimate the healthy life expectancy by expanding the classical life tables to include the proportion with disability calculated from life tables, along with the Sullivan method. The provided templates apply immediately to the life tables from WHO, HMD, Eurostat and other life table providers. Furthermore, the book explores the possibility of creating new health indicators along with Covid-19 pandemic management, factors associated to loneliness and an alcohol indicator. Part of the book is devoted to mortality, epidemic models, and the supercentenarians age estimation. Data analysis and artificial intelligence methods are included to apply in demographic and socio-economic cases. By providing a methodology to cope with health problems in demography and society by quantifying important health parameters, this book is a valuable guide for researchers, theoreticians, and practitioners from various disciplines and especially health scientists, statisticians, economists, and sociologists.
Quantitative Drug Safety and Benefit Risk Evaluation: Practical and Cross-Disciplinary Approaches (Chapman & Hall/CRC Biostatistics Series)
by William Wang Melvin Munsaka James Buchanan Judy X. LiQuantitative Methodologies and Process for Safety Monitoring and Ongoing Benefit Risk Evaluation provides a comprehensive coverage on safety monitoring methodologies, covering both global trends and regional initiatives. Pharmacovigilance has traditionally focused on the handling of individual adverse event reports however recently there had been a shift towards aggregate analysis to better understand the scope of product risks. Written to be accessible not only to statisticians but also to safety scientists with a quantitative interest, this book aims to bridge the gap in knowledge between medical and statistical fields creating a truly multi-disciplinary approach that is very much needed for 21st century safety evaluation.
Quantitative Economics with R: A Data Science Approach
by Vikram DayalThis book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader’s R skills are gradually honed, with the help of “your turn” exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods—generalized additive models and random forests (an important and versatile machine learning method)—are introduced intuitively with applications. The book will be of great interest to economists—students, teachers, and researchers alike—who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.
Quantitative Energy Finance: Recent Trends and Developments
by Fred Espen Benth Almut E. D. VeraartPower markets are undergoing a major transformation from gas and oil-fueled generation toward renewable electricity production from wind and solar sources. Simultaneously, there is an increasing demand for electrification, coupled with long-term climate-induced weather changes. The uncertainties confronting energy market participants require sophisticated modelling techniques to effectively understand risk, many of which are covered in this book.Comprising invited papers by high-profile researchers, this volume examines the empirical aspects of forward and futures prices, uncovering patterns of noise factors in various European electricity markets. Additionally, it delves into the recent, influential classes of Hawkes and trawl processes, emphasizing their significance in energy markets. The impact of renewables on energy market prices is a pivotal concern for both producers and consumers. Mean-field games provide a powerful mathematical framework for this, and a dedicated chapter outlining their dynamics is included in the book. The book also explores structural financial products and their connection to climate risk as a risk management tool, underscoring the essential need for a comprehensive understanding of these products in the realm of "green finance," to which the energy industry is integral. Lastly, the book thoroughly analyzes spatial smoothing and power purchase (PPA) contracts, addressing central issues in energy system planning and financial operations.Tailored for researchers, PhD students, and industry energy analysts, this volume equips readers with insights and tools to navigate the constantly evolving energy market landscape. It serves as a sequel to the earlier Quantitative Energy Finance book, featuring all-new chapters.
Quantitative Energy Finance: Modeling, Pricing, and Hedging in Energy and Commodity Markets
by Valery Kholodnyi Fred Espen Benth Peter LaurenceFinance and energy markets have been an active scientific field for some time, even though the development and applications of sophisticated quantitative methods in these areas are relatively new--and referred to in a broader context as energy finance. Energy finance is often viewed as a branch of mathematical finance, yet this area continues to provide a rich source of issues that are fuelling new and exciting research developments. Based on a special thematic year at the Wolfgang Pauli Institute (WPI) in Vienna, Austria, this edited collection features cutting-edge research from leading scientists in the fields of energy and commodity finance. Topics discussed include modeling and analysis of energy and commodity markets, derivatives hedging and pricing, and optimal investment strategies and modeling of emerging markets, such as power and emissions. The book also confronts the challenges one faces in energy markets from a quantitative point of view, as well as the recent advances in solving these problems using advanced mathematical, statistical and numerical methods. By addressing the emerging area of quantitative energy finance, this volume will serve as a valuable resource for graduate-level students and researchers studying financial mathematics, risk management, or energy finance.
Quantitative Epidemiology (Emerging Topics in Statistics and Biostatistics)
by Xinguang ChenThis book is designed to train graduate students across disciplines within the fields of public health and medicine, with the goal of guiding them in the transition to independent researchers. It focuses on theories, principles, techniques, and methods essential for data processing and quantitative analysis to address medical, health, and behavioral challenges. Students will learn to access to existing data and process their own data, quantify the distribution of a medical or health problem to inform decision making; to identify influential factors of a disease/behavioral problem; and to support health promotion and disease prevention. Concepts, principles, methods and skills are demonstrated with SAS programs, figures and tables generated from real, publicly available data. In addition to various methods for introductory analysis, the following are featured, including 4-dimensional measurement of distribution and geographic mapping, multiple linear and logistic regression, Poisson regression, Cox regression, missing data imputing, and statistical power analysis.
Quantitative Ethnography
by David Williamson ShafferQuantitative Ethnography is an engaging introduction to research methods for students, an introduction to data science for qualitative researchers, and an introduction to the humanities for statisticians-but also a compelling philosophical and intellectual journey for anyone who wants to understand learning, culture and behavior in the age of big data.
Quantitative Evaluation of Fire and EMS Mobilization Times
by Robert Upson Kathy A. NotarianniQuantitative Evaluation of Fire and EMS Mobilization Times presents comprehensive empirical data on fire emergency and EMS call processing and turnout times, and aims to improve the operational benchmarks of NFPA peer consensus standards through a close examination of real-world data. The book also identifies and analyzes the elements that can influence EMS mobilization response times. Quantitative Evaluation of Fire and EMS Mobilization Times is intended for practitioners as a tool for analyzing fire emergency response times and developing methods for improving them. Researchers working in a related field will also find the book valuable.
Quantitative Evaluation of Systems: 18th International Conference, QEST 2021, Paris, France, August 23–27, 2021, Proceedings (Lecture Notes in Computer Science #12846)
by Alessandro Abate Andrea MarinThis book constitutes the proceedings of the 18th International Conference on Quantitative Evaluation Systems, QEST 2021, held in Paris, France, in August 2021.The 21 full papers and 2 short papers presented together with 2 keynote papers were carefully reviewed and selected from 47 submissions. The papers are organized in the following topics: probabilistic model checking; quantitative models and metamodels: analysis and validation; queueing systems; learning and verification; simulation; performance evaluation; abstractions and aggregations; and stochastic models.
Quantitative Evaluation of Systems
by Gul Agha Benny Van HoudtThis book constitutes the proceedings of the 13th International Conference on Quantitative Evaluation Systems, QEST 2016, held in Quebec City, Canada, in August 2016. The 21 full papers and 3 tool demonstration papers presented were carefully reviewed and selected from 46 submissions. They are organized in topical sections entitled: Markov processes; tools; sampling, inference, and optimization methods; Markov decision processes and Markovian analysis; networks.
Quantitative Evaluation of Systems
by Javier Campos Boudewijn R. HaverkortThis book constitutes the proceedings of the 12th International Conference on Quantitative Evaluation of Systems, QEST 2015, held in Madrid, Spain, in September 2015. The 19 papers presented were carefully reviewed and selected from 42 submissions. They are organized in topical sections named: modelling and applications; tools; petri nets, process algebra and fault trees; applications; and queuing systems and hybrid systems. The book also contains one full-paper invited talk.
Quantitative Evaluation of Systems: 17th International Conference, QEST 2020, Vienna, Austria, August 31 – September 3, 2020, Proceedings (Lecture Notes in Computer Science #12289)
by Marco Gribaudo Anne Remke David N. JansenThis book constitutes the proceedings of the 17th International Conference on Quantitative Evaluation Systems, QEST 2020, held in Vienna, Austria, in August/September 2020. The 12 full papers presented together with 7 short papers were carefully reviewed and selected from 42 submissions. The papers cover topics such as classic measures involving performance and reliability, quantification of properties that are classically qualitative, such as safety, correctness, and security as well as analytic studies, diversity in the model formalisms and methodologies employed, and development of new formalisms and methodologies.
Quantitative Evaluation of Systems: 15th International Conference, QEST 2018, Beijing, China, September 4-7, 2018, Proceedings (Lecture Notes in Computer Science #11024)
by Annabelle McIver Andras HorvathThis book constitutes the proceedings of the 15th International Conference on Quantitative Evaluation Systems, QEST 2018, held in Beijing, China, in September 2018. The 24 full papers presented were carefully reviewed and selected from 51 submissions. The papers cover topics in the field of quantitative evaluation and verification of computer systems and networks through stochastic models and measurements emphasizing two frontier topics in research: quantitative information flow for security and industrial formal methods.
Quantitative Evaluation of Systems: 16th International Conference, QEST 2019, Glasgow, UK, September 10–12, 2019, Proceedings (Lecture Notes in Computer Science #11785)
by David Parker Verena WolfThis book constitutes the proceedings of the 16th International Conference on Quantitative Evaluation Systems, QEST 2019, held in Glasgow, UK, in September 2019.The 17 full papers presented together with 2 short papers were carefully reviewed and selected from 40 submissions. The papers cover topics in the field of Probabilistic Verification; Learning and Verification; Hybrid Systems; Security; Probabilistic Modelling and Abstraction; and Applications and Tools.
Quantitative Evaluation of Systems and Formal Modeling and Analysis of Timed Systems: First International Joint Conference, QEST+FORMATS 2024, Calgary, AB, Canada, September 9–13, 2024, Proceedings (Lecture Notes in Computer Science #14996)
by Jane Hillston Sadegh Soudjani Masaki WagaThis book constitutes the proceedings of the First International Joint Conference on Quantitative Evaluation of Systems and Formal Modeling and Analysis of Timed Systems, QEST+Formats 2024, which took place in Calgary, AB, Canada, during September 2024. This year the 21th International Conference on Quantitative Evaluation of SysTems (QEST 2024) and the 22nd International Conference on Formal Modeling and Analysis of Timed Systems (FORMATS 2024) joint forces and took place as part of the CONFEST 2024 umbrella conference. The 19 full papers presented in this book were carefully reviewed and selected from 33 submissions. They deal with up-to-date topics in quantitative evaluation and verification of systems, focusing on fundamental and practical aspects of systems with quantitative nature, such as probability, timing, and cost, and modeling, design and analysis of computational systems.
Quantitative Finance: A Simulation-Based Introduction Using Excel
by Matt DavisonTeach Your Students How to Become Successful Working QuantsQuantitative Finance: A Simulation-Based Introduction Using Excel provides an introduction to financial mathematics for students in applied mathematics, financial engineering, actuarial science, and business administration. The text not only enables students to practice with the basic techn
Quantitative Finance: An Object-Oriented Approach in C++ (Chapman and Hall/CRC Financial Mathematics Series)
by Erik SchloglQuantitative Finance: An Object-Oriented Approach in C++ provides readers with a foundation in the key methods and models of quantitative finance. Keeping the material as self-contained as possible, the author introduces computational finance with a focus on practical implementation in C++. Through an approach based on C++ classes and templates, the text highlights the basic principles common to various methods and models while the algorithmic implementation guides readers to a more thorough, hands-on understanding. By moving beyond a purely theoretical treatment to the actual implementation of the models using C++, readers greatly enhance their career opportunities in the field. The book also helps readers implement models in a trading or research environment. It presents recipes and extensible code building blocks for some of the most widespread methods in risk management and option pricing. Web ResourceThe author’s website provides fully functional C++ code, including additional C++ source files and examples. Although the code is used to illustrate concepts (not as a finished software product), it nevertheless compiles, runs, and deals with full, rather than toy, problems. The website also includes a suite of practical exercises for each chapter covering a range of difficulty levels and problem complexity.
Quantitative Finance with Python: A Practical Guide to Investment Management, Trading, and Financial Engineering (Chapman and Hall/CRC Financial Mathematics Series)
by Chris KelliherQuantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on, rigorous introduction to foundational topics in quant finance, such as options pricing, portfolio optimization and machine learning. Simultaneously, the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors. Features Useful as both a teaching resource and as a practical tool for professional investors. Ideal textbook for first year graduate students in quantitative finance programs, such as those in master’s programs in Mathematical Finance, Quant Finance or Financial Engineering. Includes a perspective on the future of quant finance techniques, and in particular covers some introductory concepts of Machine Learning. Free-to-access repository with Python codes available at www.routledge.com/ 9781032014432.
Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling
by Y. Z. MaEarth science is becoming increasingly quantitative in the digital age. Quantification of geoscience and engineering problems underpins many of the applications of big data and artificial intelligence. This book presents quantitative geosciences in three parts. Part 1 presents data analytics using probability, statistical and machine-learning methods. Part 2 covers reservoir characterization using several geoscience disciplines: including geology, geophysics, petrophysics and geostatistics. Part 3 treats reservoir modeling, resource evaluation and uncertainty analysis using integrated geoscience, engineering and geostatistical methods. As the petroleum industry is heading towards operating oil fields digitally, a multidisciplinary skillset is a must for geoscientists who need to use data analytics to resolve inconsistencies in various sources of data, model reservoir properties, evaluate uncertainties, and quantify risk for decision making. This book intends to serve as a bridge for advancing the multidisciplinary integration for digital fields. The goal is to move beyond using quantitative methods individually to an integrated descriptive-quantitative analysis. In big data, everything tells us something, but nothing tells us everything. This book emphasizes the integrated, multidisciplinary solutions for practical problems in resource evaluation and field development.
Quantitative Investigations in the Biosciences using MINITAB
by John EddisonUntil recently, acquiring a background in the basic methodological principles that apply to most types of investigations meant struggling to obtain results through laborious calculations. The advent of statistical software packages has removed much of the tedium and many of the errors of manual calculations and allowed a marked increase in the depth and sophistication of analyses. Although most statistics classes now incorporate some instruction in using a statistics package, most introductory texts do not.Quantitative Investigations in the Biosciences using MINITAB fills this void by providing an introduction to investigative methods that, in addition to outlining statistical principles and describing methods of calculations, also presents essential commands and interprets output from the statistics package MINITAB.The author introduces the three basic elements of investigations-design, analysis, and reporting-using an extremely accessible approach that keeps mathematical detail to a minimum. He groups statistical tests according to the type of problem they are used to examine, such as comparisons, sequential relationships, and associations.Quantitative Investigations in the Biosciences using MINITAB draws techniques and examples from a variety of subjects, ranging from physiology and biochemistry through to ecology, behavioral sciences, medicine, agriculture and horticulture, and complements the mathematical results with formal conclusions for all of the worked examples. It thus provides an ideal handbook for anyone in virtually any field who wants to apply statistical techniques to their investigations.
Quantitative Investing: From Theory to Industry
by Lingjie MaThis book provides readers with a systematic approach to quantitative investments and bridges the gap between theory and practice, equipping students to more seamlessly enter the world of industry. A successful quantitative investment strategy requires an individual to possess a deep understanding of the financial markets, investment theories and econometric modelings, as well as the ability to program and analyze real-world data sets. In order to connect finance theories and practical industry experience, each chapter begins with a real-world finance case study. The rest of the chapter introduces fundamental insights and theories, and teaches readers to use statistical models and R programming to analyze real-world data, therefore grounding the learning process in application. Additionally, each chapter profiles significant figures in investment and quantitative studies, so that readers can more fully understand the history of the discipline. This volume will be particularly useful to advanced students and practitioners in finance and investments.
Quantitative Literacy
by Bruce C. Crauder Benny Evans Jerry A. Johnson Alan V. Noell"I finally understand why I need to learn some math!" says a student after finishing a course that used Quantitative Literacy. That enthusiastic response gets to the heart of how this remarkable textbook works. Quantitative Literacy shows students that they use math in their everyday lives more than they realize, and that learning math in real-world contexts not only makes it easier to get better grades, but prepares them for decisions they'll face about money, voting and politics, health issues, and much more. The authors draw on a wide range of examples to give students basic mathematical tools-- from sports to personal finance to sociopolitical action to medical tests to the arts--with coverage that neatly balances discussions of ideas with computational practice.