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Filling and Wrapping: Three-Dimensional Measurements (Texas)
by Glenda Lappan James T. Fey William M. Fitzgerald Susan N. Friel Elizabeth Difanis Phillips WestWords Inc.NIMAC-sourced textbook
Filling and Wrapping: Three-Dimensional Measurement
by Glenda Lappan Elizabeth Difanis Phillips James T. Fey Susan N. FrielNIMAC-sourced textbook
Filling and Wrapping: Three-Dimensional Measurement
by Phillips Fey Friel LappanGlenda Lap pan is a University Distinguished Professor in the Program in Mathematics Education (PRIME) and the Department of Mathematics at Michigan State University. Her research and development interests are in the connected areas of students' learning of mathematics and mathematics teachers' professional growth and change related to the development and enactment of K-12 curriculum materials. Elizabeth Difanis Phillips is a Senior Academic Specialist in the Program in Mathematics Education (PRIME) and the Department of Mathematics at Michigan State University. She is interested in teaching and learning mathematics for both teachers and students. These interests have led to curriculum and professional development projects at the middle school and high school levels, as well as projects related to the teaching and learning of algebra across the grades.
Filling and Wrapping, Three-Dimensional Measurement
by Glenda Lappan James T. Fey William M. Fitzgerald Susan N. Friel Elizabeth Difanis PhillipsNIMAC-sourced textbook
Filtering and Control of Stochastic Jump Hybrid Systems
by Xiuming Yao Ligang Wu Wei Xing ZhengThis book presents recent research work on stochastic jump hybrid systems. Specifically, the considered stochastic jump hybrid systems include Markovian jump Ito stochastic systems, Markovian jump linear-parameter-varying (LPV) systems, Markovian jump singular systems, Markovian jump two-dimensional (2-D) systems, and Markovian jump repeated scalar nonlinear systems. Some sufficient conditions are first established respectively for the stability and performances of those kinds of stochastic jump hybrid systems in terms of solution of linear matrix inequalities (LMIs). Based on the derived analysis conditions, the filtering and control problems are addressed. The book presents up-to-date research developments and novel methodologies on stochastic jump hybrid systems. The contents can be divided into two parts: the first part is focused on robust filter design problem, while the second part is put the emphasis on robust control problem. These methodologies provide a framework for stability and performance analysis, robust controller design, and robust filter design for the considered systems. Solutions to the design problems are presented in terms of LMIs. The book is a timely reflection of the developing area of filtering and control theories for Markovian jump hybrid systems with various kinds of imperfect information. It is a collection of a series of latest research results and therefore serves as a useful textbook for senior and/or graduate students who are interested in knowing 1) the state-of-the-art of linear filtering and control areas, and 2) recent advances in stochastic jump hybrid systems. The readers will also benefit from some new concepts, new models and new methodologies with practical significance in control engineering and signal processing.
El fin de la ciencia: Todo Lo Que Un Ciudadano Debe Saber Sobre Ciencia Y No Sabe Cómo Preguntar Ni De
by Manuel Lozano LeyvaTodo lo que un ciudadano (que no sabe qué preguntar ni de quién fiarse) debería saber sobre ciencia. El fin de la ciencia responde a una doble pregunta: ¿se puede acabar la ciencia? y ¿qué busca la ciencia? Tras un irreverente recorrido por la historia de la ciencia y la tecnología, el prestigioso científico y gran divulgador Lozano Leyva realiza un ameno e inteligente recorrido por los riesgos y las amenazas actuales, desde la mediocridad y la falta de recursos hasta las pseudociencias o el negacionismo ambiental, para repasar a continuación sus objetivos en los campos más diversos. La idea de partida del libro es que los ciudadanos no pueden ejercer la democracia apropiadamente sin unos conocimientos básicos de lo que es la ciencia y la tecnología, incluidos no sólo sus grandezas y milagros, sino también sus miserias y peligros. La expansión del conocimiento científico y técnico que arrancó en el Renacimiento,tomó impulso en la Ilustración y eclosionó con las grandes y sangrientas convulsiones del siglo XX es un fenómeno único en la historia al que nos tenemos que enfrentar en el siglo XXI. Lo hemos de hacer ilusionados, sí, pero también alertas. Y nuestros políticos saben de esto lo mismo que los ciudadanos: poco o nada. Esclarecer esas ideas, o contribuir a hacerlo, es lo que pretende El fin de la ciencia. «Lo único que quiero con este libro es que cualquier persona tenga una razón más para meter un voto en la urna.»Manuel Lozano Leyva Reseña:«El fin de la ciencia se ocupa, principalmente, de revelar errores de juicio, supercherías notorias y campos donde la ciencia será decisiva en un futuro inmediato: la pesca, la alimentación, el suministro de agua, la prevención y el combate de enfermedades endémicas, etc.»Diario de Sevilla
Finance with Monte Carlo
by Ronald W. ShonkwilerThis text introduces upper division undergraduate/beginning graduate students in mathematics, finance, or economics, to the core topics of a beginning course in finance/financial engineering. Particular emphasis is placed on exploiting the power of the Monte Carlo method to illustrate and explore financial principles. Monte Carlo is the uniquely appropriate tool for modeling the random factors that drive financial markets and simulating their implications. The Monte Carlo method is introduced early and it is used in conjunction with the geometric Brownian motion model (GBM) to illustrate and analyze the topics covered in the remainder of the text. Placing focus on Monte Carlo methods allows for students to travel a short road from theory to practical applications. Coverage includes investment science, mean-variance portfolio theory, option pricing principles, exotic options, option trading strategies, jump diffusion and exponential Lévy alternative models, and the Kelly criterion for maximizing investment growth. Novel features: inclusion of both portfolio theory and contingent claim analysis in a single text pricing methodology for exotic options expectation analysis of option trading strategies pricing models that transcend the Black-Scholes framework optimizing investment allocations concepts thoroughly explored through numerous simulation exercises numerous worked examples and illustrations The mathematical background required is a year and one-half course in calculus, matrix algebra covering solutions of linear systems, and a knowledge of probability including expectation, densities and the normal distribution. A refresher for these topics is presented in the Appendices. The programming background needed is how to code branching, loops and subroutines in some mathematical or general purpose language. The mathematical background required is a year and one-half course in calculus, matrix algebra covering solutions of linear systems, and a knowledge of probability including expectation, densities and the normal distribution. A refresher for these topics is presented in the Appendices. The programming background needed is how to code branching, loops and subroutines in some mathematical or general purpose language. Also by the author: (with F. Mendivil) Explorations in Monte Carlo, ©2009, ISBN: 978-0-387-87836-2; (with J. Herod) Mathematical Biology: An Introduction with Maple and Matlab, Second edition, ©2009, ISBN: 978-0-387-70983-3.
Financial Algebra: Student Workbook
by Robert K. Gerver Richard J. SgroiThe Student Workbook offers additional resources for mastering algebraic concepts within a financial context.
Financial Algebra: Advanced Algebra With Financial Applications
by Robert Gerver Richard SgroiBy combining algebraic and graphical approaches with practical business and personal finance applications, South-Western's FINANCIAL ALGEBRA, motivates high school students to explore algebraic thinking patterns and functions in a financial context.
Financial Algebra: Advanced Algebra With Financial Applications (Financial Algebra Ser.)
by Robert Gerver Richard SgroiNIMAC-sourced textbook
Financial Algebra
by Robert Gerver Richard SgroiBy combining algebraic and graphical approaches with practical business and personal finance applications, South-Western's FINANCIAL ALGEBRA, motivates high school students to explore algebraic thinking patterns and functions in a financial context. FINANCIAL ALGEBRA will help your students achieve success by offering an applications based learning approach incorporating Algebra I, Algebra II, and Geometry topics. Authors Gerver and Sgroi have spent more than 25 years working with students of all ability levels and they have found the most success when connecting math to the real world. FINANCIAL ALGEBRA encourages students to be actively involved in applying mathematical ideas to their everyday lives.
Financial and Actuarial Statistics: An Introduction, Second Edition
by Dale S. Borowiak Arnold F. ShapiroUnderstand Up-to-Date Statistical Techniques for Financial and Actuarial ApplicationsSince the first edition was published, statistical techniques, such as reliability measurement, simulation, regression, and Markov chain modeling, have become more prominent in the financial and actuarial industries. Consequently, practitioners and students must ac
Financial and Insurance Formulas
by Tomas CipraThis survey contains more than 3,000 formulas and methods from the field of finance and insurance mathematics (as well as related formulas in mathematics, probability theory, statistics, econometrics, index numbers, demography, stochastic processes and time series). The formulas are mostly applicable in financial and actuarial practice. Their mathematical level ranges from simple ones based on arithmetic to very sophisticated matters of higher mathematics (e. g. stochastic calculus), but they are usually presented in the form most frequently used in applications. Explanations and references to related parts of the survey are given so that one can easily browse and look them up in the text; the detailed Index is also helpful for this purpose. The survey will be of benefit for students, researchers and practitioners in finance and insurance.
Financial and Managerial Accounting (Seventeenth Edition)
by Jan Williams Joseph Carcello Mark Bettner Susan HakaWith the seventeenth edition of Financial and Managerial Accounting: The Basis for Business Decisions, the Williams author team continues to be a solid foundation for students who are learning basic accounting concepts. Hallmarks of the text - including the solid Accounting Cycle Presentation, relevant pedagogy, and high quality, end-of-chapter material--have been updated throughout the book.
Financial Calculus
by Martin Baxter Andrew RennieFinance provides a dramatic example of the successful application of advanced mathematical techniques to the practical problem of pricing financial derivatives. This self-contained 2002 text is designed for first courses in financial calculus aimed at students with a good background in mathematics. Key concepts such as martingales and change of measure are introduced in the discrete time framework, allowing an accessible account of Brownian motion and stochastic calculus: proofs in the continuous-time world follow naturally. The Black-Scholes pricing formula is first derived in the simplest financial context. The second half of the book is then devoted to increasing the financial sophistication of the models and instruments. The final chapter introduces more advanced topics including stock price models with jumps, and stochastic volatility. A valuable feature is the large number of exercises and examples, designed to test technique and illustrate how the methods and concepts can be applied to realistic financial questions.
Financial Data Analytics with Machine Learning, Optimization and Statistics (Wiley Finance)
by Sam Chen Ka Chun Cheung Phillip YamAn essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actu
Financial Data Analytics with R: Monte-Carlo Validation
by Jenny K. ChenFinancial Data Analysis with R: Monte-Carlo Validation is a comprehensive exploration of statistical methodologies and their applications in finance. Readers are taken on a journey in each chapter through practical explanations and examples, enabling them to develop a solid foundation of these methods in R and their applications in finance.This book serves as an indispensable resource for finance professionals, analysts, and enthusiasts seeking to harness the power of data-driven decision-making.The book goes beyond just teaching statistical methods in R and incorporates a unique section of informative Monte-Carlo simulations. These Monte-Carlo simulations are uniquely designed to showcase the reader the potential consequences and misleading conclusions that can arise when fundamental model assumptions are violated. Through step-by-step tutorials and realworld cases, readers will learn how and why model assumptions are important to follow.With a focus on practicality, Financial Data Analysis with R: Monte-Carlo Validation equips readers with the skills to construct and validate financial models using R. The Monte-Carlo simulation exercises provide a unique opportunity to understand the methods further, making this book an essential tool for anyone involved in financial analysis, investment strategy, or risk management. Whether you are a seasoned professional or a newcomer to the world of financial analytics, this book serves as a guiding light, empowering you to navigate the landscape of finance with precision and confidence.Key Features: An extensive compilation of commonly used financial data analytics methods from fundamental to advanced levels Learn how to model and analyze financial data with step-by-step illustrations in R and ready-to-use publicly available data Includes Monte-Carlo simulations uniquely designed to showcase the reader the potential consequences and misleading conclusions that arise when fundamental model assumptions are violated Data and computer programs are available for readers to replicate and implement the models and methods themselves
Financial Data Resampling for Machine Learning Based Trading: Application to Cryptocurrency Markets (SpringerBriefs in Applied Sciences and Technology)
by Tomé Almeida Borges Rui NevesThis book presents a system that combines the expertise of four algorithms, namely Gradient Tree Boosting, Logistic Regression, Random Forest and Support Vector Classifier to trade with several cryptocurrencies. A new method for resampling financial data is presented as alternative to the classical time sampled data commonly used in financial market trading. The new resampling method uses a closing value threshold to resample the data creating a signal better suited for financial trading, thus achieving higher returns without increased risk. The performance of the algorithm with the new resampling method and the classical time sampled data are compared and the advantages of using the system developed in this work are highlighted.
Financial Derivative and Energy Market Valuation
by Michael MastroA road map for implementing quantitative financial modelsFinancial Derivative and Energy Market Valuation brings the application of financial models to a higher level by helping readers capture the true behavior of energy markets and related financial derivatives. The book provides readers with a range of statistical and quantitative techniques and demonstrates how to implement the presented concepts and methods in Matlab®.Featuring an unparalleled level of detail, this unique work provides the underlying theory and various advanced topics without requiring a prior high-level understanding of mathematics or finance. In addition to a self-contained treatment of applied topics such as modern Fourier-based analysis and affine transforms, Financial Derivative and Energy Market Valuation also:* Provides the derivation, numerical implementation, and documentation of the corresponding Matlab for each topic* Extends seminal works developed over the last four decades to derive and utilize present-day financial models* Shows how to use applied methods such as fast Fourier transforms to generate statistical distributions for option pricing* Includes all Matlab code for readers wishing to replicate the figures found throughout the bookThorough, practical, and easy to use, Financial Derivative and Energy Market Valuation is a first-rate guide for readers who want to learn how to use advanced numerical methods to implement and apply state-of-the-art financial models. The book is also ideal for graduate-level courses in quantitative finance, mathematical finance, and financial engineering.
Financial Econometrics
by Peijie WangThis book which provides an overview of contemporary topics related to the modelling of financial time series, is set against a backdrop of rapid expansions of interest in both the models themselves and the financial problems to which they are applied.This excellent textbook covers all the major developments in the area in recent years in an informative as well as succinct way.Refreshingly, every chapter has a section of two or more examples and a section of empirical literature, offering the reader the opportunity to practice the kind of research going on in the area. This approach helps the reader develop interest, confidence and momentum in learning contemporary econometric topics
Financial Econometrics (Routledge Advanced Texts In Economics And Finance Ser.)
by Peijie WangThis book provides an essential toolkit for all students wishing to know more about the modelling and analysis of financial data. Applications of econometric techniques are becoming increasingly common in the world of finance and this second edition of an established text covers the following key themes:- unit roots, cointegration and other develop
Financial Econometrics and Empirical Market Microstructure
by Anil K. Bera Sergey Ivliev Fabrizio LilloIn the era of Big Data our society is given the unique opportunity to understand the inner dynamics and behavior of complex socio-economic systems. Advances in the availability of very large databases, in capabilities for massive data mining, as well as progress in complex systems theory, multi-agent simulation and computational social science open the possibility of modeling phenomena never before successfully achieved. This contributed volume from the Perm Winter School address the problems of the mechanisms and statistics of the socio-economics system evolution with a focus on financial markets powered by the high-frequency data analysis.
Financial Econometrics: Bayesian Analysis, Quantum Uncertainty, and Related Topics (Studies in Systems, Decision and Control #427)
by Nguyen Ngoc Thach Vladik Kreinovich Doan Thanh Ha Nguyen Duc TrungThis book overviews latest ideas and developments in financial econometrics, with an emphasis on how to best use prior knowledge (e.g., Bayesian way) and how to best use successful data processing techniques from other application areas (e.g., from quantum physics). The book also covers applications to economy-related phenomena ranging from traditionally analyzed phenomena such as manufacturing, food industry, and taxes, to newer-to-analyze phenomena such as cryptocurrencies, influencer marketing, COVID-19 pandemic, financial fraud detection, corruption, and shadow economy. This book will inspire practitioners to learn how to apply state-of-the-art Bayesian, quantum, and related techniques to economic and financial problems and inspire researchers to further improve the existing techniques and come up with new techniques for studying economic and financial phenomena. The book will also be of interest to students interested in latest ideas and results.
Financial Econometrics, Mathematics and Statistics: Theory, Method and Application
by Cheng-Few Lee Hong-Yi Chen John LeeThis rigorous textbook introduces graduate students to the principles of econometrics and statistics with a focus on methods and applications in financial research. Financial Econometrics, Mathematics, and Statistics introduces tools and methods important for both finance and accounting that assist with asset pricing, corporate finance, options and futures, and conducting financial accounting research. Divided into four parts, the text begins with topics related to regression and financial econometrics. Subsequent sections describe time-series analyses; the role of binomial, multi-nomial, and log normal distributions in option pricing models; and the application of statistics analyses to risk management. The real-world applications and problems offer students a unique insight into such topics as heteroskedasticity, regression, simultaneous equation models, panel data analysis, time series analysis, and generalized method of moments. Written by leading academics in the quantitative finance field, allows readers to implement the principles behind financial econometrics and statistics through real-world applications and problem sets. This textbook will appeal to a less-served market of upper-undergraduate and graduate students in finance, economics, and statistics.
Financial Econometrics Modeling: Derivatives Pricing, Hedge Funds and Term Structure Models
by Greg N. Gregoriou Razvan PascalauThis book proposes new tools and models to price options, assess market volatility, and investigate the market efficiency hypothesis. In particular, it considers new models for hedge funds and derivatives of derivatives, and adds to the literature of testing for the efficiency of markets both theoretically and empirically.