Single-Arm Phase II Survival Trial Design
Single-arm phase II trial is a key component for developing advanced cancer drug and treatment to target therapy and immunotherapy in which time-to-event endpoints are the primary endpoints. The proposed book provides a comprehensive summary to the most commonly used methods for single-arm phase II trial design with time-to-event endpoints.
Computational Methods for Deep Learning
The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book. The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.
Analyticity and Sparsity in Uncertainty Quantification for Pdes with Gaussian Random Field Inputs
The present book develops the mathematical and numerical analysis of linear, elliptic and parabolic partial differential equations (PDEs) with coefficients whose logarithms are modelled as Gaussian random fields (GRFs), in polygonal and polyhedral physical domains. Both, forward and Bayesian inverse PDE problems subject to GRF priors are considered.Adopting a pathwise, affine-parametric representation of the GRFs, turns the random PDEs into equivalent, countably-parametric, deterministic PDEs, with nonuniform ellipticity constants. A detailed sparsity analysis of Wiener-Hermite polynomial chaos expansions of the corresponding parametric PDE solution families by analytic continuation into the complex domain is developed, in corner- and edge-weighted function spaces on the physical domain.The presented Algorithms and results are relevant for the mathematical analysis of many approximation methods for PDEs with GRF inputs, such as model order reduction, neural network and tensor-formatted surrogates of parametric solution families. They are expected to impact computational uncertainty quantification subject to GRF models of uncertainty in PDEs, and are of interest for researchers and graduate students in both, applied and computational mathematics, as well as in computational science and engineering.
A First Course in Ergodic Theory
This book provides readers with an introductory course in Ergodic Theory. This textbook has been developed from the authors' own notes on the subject, which they have been teaching since the 1990s. Over the years they have added topics, theorems, examples and explanations from various sources.
Mathematicians Playing Games
This book explores a wide variety of popular mathematical games, including their historical beginnings and the mathematical theories that underpin them. Its academic level is suitable for high school students and higher, but people of any age or level will find something to entertain them, and something new to learn.
Mathematicians Playing Games
This book explores a wide variety of popular mathematical games, including their historical beginnings and the mathematical theories that underpin them. Its academic level is suitable for high school students and higher, but people of any age or level will find something to entertain them, and something new to learn.
Time Series for Data Scientists
Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines
Asymptotic and Stationary Preserving Schemes for Kinetic and Hyperbolic Partial Differential Equations
In this thesis, we are interested in numerically preserving stationary solutions of balance laws. We start by developing finite volume well-balanced schemes for the system of Euler equations and the system of Magnetohydrodynamics (MHD) equations with gravitational source term. Since fluid models and kinetic models are related, this leads us to investigate Asymptotic Preserving (AP) schemes for kinetic equations and their ability to preserve stationary solutions. In an attempt to mimic our result for kinetic equations in the context of fluid models, for the isentropic Euler equations we developed an AP scheme in the limit of the Mach number going to zero. The properties of the schemes we developed and its criteria are validated numerically by various test cases from the literature.
Mathematical Topics on Modelling Complex Systems
This book explores recent developments in theoretical research and mathematical modelling of real-world complex systems, organized in four parts. The first part of the book is devoted to the mathematical tools for the design and analysis in engineering and social science study cases. We discuss the periodic evolutions in nonlinear chemical processes, vibro-compact systems and their behaviour, different types of metal-semiconductor self-assembled samples, made of silver nanowires and zinc oxide nanorods. The second part of the book is devoted to mathematical description and modelling of the critical events, climate change and robust emergency scales. In three chapters, we consider a climate-economy model with endogenous carbon intensity and the behaviour of Tehran Stock Exchange market under international sanctions. The third part of the book is devoted to fractional dynamic and fractional control problems. We discuss the novel operational matrix technique for variable-order fractional optimal control problems, the nonlinear variable-order time fractional convection-diffusion equation with generalized polynomials The fourth part of the book concerns solvability and inverse problems in differential and integro-differential equations. The book facilitates a better understanding of the mechanisms and phenomena in nonlinear dynamics and develops the corresponding mathematical theory to apply nonlinear design to practical engineering. It can be read by mathematicians, physicists, complex systems scientists, IT specialists, civil engineers, data scientists and urban planners.
Modeling, Simulation and Optimization in the Health- And Energy-Sector
This volume is addressed to people who are interested in modern mathematical solutions for real life applications. In particular, mathematical modeling, simulation and optimization is nowadays successfully used in various fields of application, like the energy- or health-sector. Here, mathematics is often the driving force for new innovations and most relevant for the success of many interdisciplinary projects. The presented chapters demonstrate the power of this emerging research field and show how society can benefit from applied mathematics.
Art and IR Theory
This book examines the correspondence between international relations (IR) theories of structural realism and constructivism and paintings, notably the artwork of Mark Rothko and Jackson Pollock, in a game theory setting. This interdisciplinary approach, through the lens of game theory and semiotics, permits different, enriched interpretations of structural realism and constructivism. These theories constitute an axis of debate between social and systemic approaches to international politics, as well as an axis of differentiation between scientific realism and positivism as philosophies of science. As such, the interpretations explored in this book contribute to what we know about international relations, how semiotics intersect with strategic uncertainty, and explains these interactions in the proposed games model.The book's use of game theory and semiotics generate 'visual semiotic games' (VSGs) that shed light on the debate axes through strategic uncertainty, interactions, and players' interactive belief systems. VSGs will contribute to literature on experimental semiotics in the sense of players' coordination behavior, beliefs, and artistic evaluations. The equilibria, interpreted through branches of philosophy of mind and theories of explanation, will reveal possibilities of agreement among players about which artwork representing the theory at hand is the best, opening innovative research perspectives for the discipline of IR theory.
The fast guide to statistical testing with JASP
FeaturesWorked examples from the social sciencesClearly written, without mathematical formulaeWell-structured, for beginnersComprehensive chapter on categorical analysis - not just 'Chi squared'Effect sizes and confidence intervalsIntroduces Bayesian statisticsReproduces a well-received short chapter on making presentationsSummary chapter on intermediate statistical testsData sets and case studies on the website
The fast guide to statistical testing with JASP
FeaturesWorked examples from the social sciencesClearly written, without mathematical formulaeWell-structured, for beginnersComprehensive chapter on categorical analysis - not just 'Chi squared'Effect sizes and confidence intervalsIntroduces Bayesian statisticsReproduces a well-received short chapter on making presentationsSummary chapter on intermediate statistical testsData sets and case studies on the website
Sequences and Series in Calculus
The book Sequences and Series in Calculus is designed as the first college/university calculus course for students who take and do well on the AP AB exam in high school and who are interested in a more proof-oriented treatment of calculus. The text begins with an ε-ℕ treatment of sequence convergence, then builds on this to discuss convergence of series--first series of real numbers, then series of functions. The difference between uniform and pointwise convergence is discussed in some detail. This is followed by a discussion of calculus on power series and Taylor series. Finally improper integrals, integration by parts and partial fractions integration all are introduced. This book is designed both to teach calculus, and to give the readers and students a taste of analysis to help them determine if they wish to study this material even more deeply. It might be used by colleges and universities who teach special versions of calculus courses for their most mathematically advanced entering first-year students, as might its older sibling text Multivariable and Vector Calculus which appeared in 2020 and is intended for students who take and do well on the AP BC exam.
Metric Spaces and Related Analysis
This book offers the comprehensive study of one of the foundational topics in Mathematics, known as Metric Spaces. The book delivers the concepts in an appropriate and concise manner, at the same time rich in illustrations and exercise problems. Special focus has been laid on important theorems like Baire's Category theorem, Heine-Borel theorem, Ascoli-Arzela Theorem, etc, which play a crucial role in the study of metric spaces.The additional chapter on Cofinal completeness, UC spaces and finite chainability makes the text unique of its kind. This helps the students in: Readers will also find brief discussions on various subtleties of continuity like subcontinuity, upper semi-continuity, lower semi-continuity, etc. The interested readers will be motivated to explore the special classes of functions between metric spaces to further extent.Consequently, the book becomes a complete package: it makes the foundational pillars strong and develops the interest of students to pursue research in metric spaces. The book is useful for third and fourth year undergraduate students and it is also helpful for graduate students and researchers.
Mathematics of Multilevel Systems: Data, Scaling, Images, Signals, and Fractals
This book presents the mathematics of wavelet theory and its applications in a broader sense, comprising entropy encoding, lifting scheme, matrix factorization, and fractals. It also encompasses image compression examples using wavelet transform and includes the principal component analysis which is a hot topic on data dimension reduction in machine learning.Readers will find equal coverage on the following three themes: The book entails a varied choice of diverse interdisciplinary themes. While the topics can be found in various parts of the pure and applied literature, this book fulfills the need for an accessible presentation which cuts across the fields.As the target audience is wide-ranging, a detailed and systematic discussion of issues involving infinite dimensions and Hilbert space is presented in later chapters on wavelets, transform theory and, entropy encoding and probability. For the problems addressed there, the case of infinite dimension will be more natural, and well-motivated.
R Programming
R is an open-source statistical environment and programming language that has grown in popularity for data management and analysis in various industries. "R" Programming teaches you all the R you'll ever need in a rapid and painless manner. This accessible tutorial taught you your way around a list with no previous programming expertise and loads of practical examples, step-by-step exercises, and sample code. This book covers the most significant modeling and prediction methods, as well as their applications.Learn how to use R to transform raw data into knowledge, understanding, and insight. This book introduces you to R, RStudio, and the tidyverse, a set of R tools that work together to make data research simple, fluent, and enjoyable. This book is meant to get you practicing data science as fast as possible, even if you have no prior programming expertise. You'll get a comprehensive grasp of the data science cycle and the fundamental tools you'll need to handle the details.R is becoming more well-known by the day, as large institutions embrace it as a standard. Its popularity stems partly from the fact that it is a free tool replacing expensive statistical software products that may take an undue amount of time to master. Furthermore, R allows a user to do complicated statistical analyses with only a few keystrokes, making advanced studies accessible and clear to a broad audience.Learn how to import data, construct and dismantle data objects, traverse R's environment system, develop your own functions, and utilize all of R's programming tools with this book. This book will not only teach you how to program but also how to use R for more than simply displaying and analyzing data.Most of the chapters are written for you to understand statistical data, so if you are a student, this book can guarantee to teach you some basic statistics that will help you get good grades.The first two chapters are based on beginner's level of R programming, mainly teaching how to successfully install and run the program on your device and introducing Rstudio to you. You will get complete guidance.Chapters three and four will teach you some basic and simple lines of code and how you can successfully execute them on your device. There will be some simple maths included. After that, you will start chapter five, which is completely based on statistics. Chapters six and seven will take your attention to more IT. If you already know basic HTML you will get more information in these chapters as they include similar topics as HTML.Finally, you arrived at chapters eight, nine, and ten, which will enable you to create and run your data. These chapters will complete your journey by allowing you to understand how data frames work and how you can work with different types of data. You will also see how you can install premade data packages that can be helpful when you want to practice more. After that, you will know how to visualize your data in different forms of charts, graphs, etc.The book is highly informative with lots of code, and also I added tons of pictures of how the code will look in r studio. Following the book, you will be able to start Rstudio and use the program smoothly.
Mathematics for Human Flourishing in the Time of Covid-19 and Post Covid-19
The International Chair in Mathematical Physics and Applications (ICMPA - UNESCO chair), University of Abomey-Calavi, Benin, and the Center for Applied Mathematics of the Faculty of Mechanical Engineering Nis, CAM-FMEN, organized a webinar on Mathematics for human flourishing in the time of COVID-19 and post COVID-19, 21 October 2020, supported by the City of Nis. The objectives of the webinar were to give precise information about the work that scientists do to cure the disease, to push forward technology, to understand our society and create new expressions of humanity, and to question the role of mathematics in the responses to this pandemic.
An Introduction to Optimization with Applications in Machine Learning and Data Analytics
The text introduces students to numerous methods in solving a variety of Optimization problems. Also, the narrow focus of most math textbooks is completely dedicated to nonlinear programming, linear programming, combinatorial or convex optimization.
Ordinary Differential Equations
The textbook presents a rather unique combination of topics in ODEs, examples and presentation style. The primary intended audience is undergraduate (2nd, 3rd, or 4th year) students in engineering and science (physics, biology, economics). The needed pre-requisite is a mastery of single-variable calculus. A wealth of included topics allows using the textbook in up to three sequential, one-semester ODE courses. Presentation emphasizes the development of practical solution skills by including a very large number of in-text examples and end-of-section exercises. All in-text examples, be they of a mathematical nature or a real-world examples, are fully solved, and the solution logic and flow are explained. Even advanced topics are presented in the same undergraduate-friendly style as the rest of the textbook. Completely optional interactive laboratory-type software is included with the textbook. Email Mikhail.Khenner@wku.edu with proof of textbook purchase to request access to optional software download.
Bayesian Filtering and Smoothing
Now in its second edition, this accessible text presents a unified Bayesian treatment of state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models. The book focuses on discrete-time state space models and carefully introduces fundamental aspects related to optimal filtering and smoothing. In particular, it covers a range of efficient non-linear Gaussian filtering and smoothing algorithms, as well as Monte Carlo-based algorithms. This updated edition features new chapters on constructing state space models of practical systems, the discretization of continuous-time state space models, Gaussian filtering by enabling approximations, posterior linearization filtering, and the corresponding smoothers. Coverage of key topics is expanded, including extended Kalman filtering and smoothing, and parameter estimation. The book's practical, algorithmic approach assumes only modest mathematical prerequisites, suitable for graduate and advanced undergraduate students. Many examples are included, with Matlab and Python code available online, enabling readers to implement algorithms in their own projects.
Computational Mathematics
This textbook is a comprehensive introduction to computational mathematics and scientific computing suitable for undergraduate and postgraduate courses. It presents both practical and theoretical aspects of the subject, as well as advantages and pitfalls of classical numerical methods alongside with computer code and experiments in Python. Each chapter closes with modern applications in physics, engineering, and computer science.Features: No previous experience in Python is required. Includes simplified computer code for fast-paced learning and transferable skills development. Includes practical problems ideal for project assignments and distance learning. Presents both intuitive and rigorous faces of modern scientific computing. Provides an introduction to neural networks and machine learning.
Artificial Intelligence for Financial Markets
This book introduces the novel artificial intelligence technique of polymodels and applies it to the prediction of stock returns. The idea of polymodels is to describe a system by its sensitivities to an environment, and to monitor it, imitating what a natural brain does spontaneously. In practice this involves running a collection of non-linear univariate models. This very powerful standalone technique has several advantages over traditional multivariate regressions. With its easy to interpret results, this method provides an ideal preliminary step towards the traditional neural network approach. The first two chapters compare the technique with other regression alternatives and introduces an estimation method which regularizes a polynomial regression using cross-validation. The rest of the book applies these ideas to financial markets. Certain equity return components are predicted using polymodels in very different ways, and a genetic algorithm is describedwhich combines these different predictions into a single portfolio, aiming to optimize the portfolio returns net of transaction costs. Addressed to investors at all levels of experience this book will also be of interest to both seasoned and non-seasoned statisticians.
Nonautonomous Bifurcation Theory
Bifurcation theory is a major topic in dynamical systems theory with profound applications. However, in contrast to autonomous dynamical systems, it is not clear what a bifurcation of a nonautonomous dynamical system actually is, and so far, various different approaches to describe qualitative changes have been suggested in the literature. The aim of this book is to provide a concise survey of the area and equip the reader with suitable tools to tackle nonautonomous problems. A review, discussion and comparison of several concepts of bifurcation is provided, and these are formulated in a unified notation and illustrated by means of comprehensible examples. Additionally, certain relevant tools needed in a corresponding analysis are presented.
Oblique Derivative Problems for Elliptic Equations in Conical Domains
The aim of our book is the investigation of the behavior of strong and weak solutions to the regular oblique derivative problems for second order elliptic equations, linear and quasi-linear, in the neighborhood of the boundary singularities. The main goal is to establish the precise exponent of the solution decrease rate and under the best possible conditions. The question on the behavior of solutions of elliptic boundary value problems near boundary singularities is of great importance for its many applications, e.g., in hydrodynamics, aerodynamics, fracture mechanics, in the geodesy etc. Only few works are devoted to the regular oblique derivative problems for second order elliptic equations in non-smooth domains. All results are given with complete proofs. The monograph will be of interest to graduate students and specialists in elliptic boundary value problems and their applications.
Measurement Uncertainty
This reprint focuses on a very important topic in metrology, which is represent by measurement uncertainty. Any good metrologist or scientist in engineering knows that no measurement makes sense without an associated uncertainty value: without an uncertainty value, no decision can be taken; no comparisons can be made; no conformity can be assessed. Any decision, comparison or conformity assessment made without considering the measurement uncertainty affecting the measurement value is completely useless and meaningless. Stated that, it becomes very clear that uncertainty in measurement plays indeed a very important rule in our everyday life. This is the reason why there is a great need to have a fruitful academic and scientific discussion on this topic.We have been speaking about measurement uncertainty for less than 30 years, since the concept of "measurement uncertainty" has been introduced in 1995 by the "Guide to the expression of uncertainty in measurement" (GUM). Thirty years seems to be many, but still the concept of measurement uncertainty has not been spread worldwide and the GUM is a document that is not known everywhere. On the other hand, this document should be considered not only in academic scenario, but also in any technical and industrial scenario, where it is pivotal to know the meaning of measurement uncertainty, identify the uncertainty contributions and know how these contributions affect the final measurement result.
Statistical Inference Based on Kernel Distribution Function Estimators
This book presents a study of statistical inferences based on the kernel-type estimators of distribution functions. The inferences involve matters such as quantile estimation, nonparametric tests, and mean residual life expectation, to name just some. Convergence rates for the kernel estimators of density functions are slower than ordinary parametric estimators, which have root-n consistency. If the appropriate kernel function is used, the kernel estimators of the distribution functions recover the root-n consistency, and the inferences based on kernel distribution estimators have root-n consistency. Further, the kernel-type estimator produces smooth estimation results. The estimators based on the empirical distribution function have discrete distribution, and the normal approximation cannot be improved--that is, the validity of the Edgeworth expansion cannot be proved. If the support of the population density function is bounded, there is a boundary problem, namely the estimator does not have consistency near the boundary. The book also contains a study of the mean squared errors of the estimators and the Edgeworth expansion for quantile estimators.
Numerical Methods Fundamentals
The book is designed to cover all major aspects of applied numerical methods, including numerical computations, solution of algebraic and transcendental equations, finite differences and interpolation, curve fitting, correlation and regression, numerical differentiation and integration, matrices and linear system of equations, numerical solution of ordinary differential equations, and numerical solution of partial differential equations. It uses a numerical problem-solving orientation with numerous examples, figures, and end of chapter exercises. Presentations are limited to very basic topics to serve as an introduction to more advanced topics.
An Introduction to R and Python for Data Analysis
An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and students to teach and learn more and save time.
An Introduction to Bond Graph Modeling with Applications
An Introduction to Bond Graph Modeling with Applications presents a collection of exercises on dynamical systems, modeling and control for university students in the areas of engineering, physics and applied mathematics. We can find several books on bond graphs, but most merely a small set of exercises and, in a few cases, some commands for computer packages like MATLAB or Mathematica. It is difficult to find books with a broad set of solved exercises and proposed exercises with solutions, guiding researchers starting their work with bond graphs, or students who are just beginning their study of the topic. This book aims to fill that gap, and provide a comprehensive, reader-friendly introduction to the Bond Graph modeling tool. Features Gives in-depth theoretical background coupled with practical, hands-on instructions. Provides a clear pedagogical framework, with numerous exercises and problems. Suitable for students and researchers who work with bond graphs: principally such as applied mathematicians, physicist and engineers.
An Introduction to Numerical Methods
An Introduction to Numerical Methods: A MATLAB(R) Approach, Fifth Edition continues to offer readers an accessible and practical introduction to numerical analysis. It presents a wide range of algorithms for scientific and engineering applications, using MATLAB to illustrate each numerical method.
Stochastic Differential Equations for Science and Engineering
The book describes the mathematical construction of stochastic differential equations with a level of detail suitable to the audience, while also discussing applications to estimation, stability analysis, and control. The book includes numerous examples and challenging exercises.
Game Theory: A Nontechnical Introduction to the Analysis of Strategy (Fourth Edition)
As with the previous editions, this fourth edition relies on teaching by example and the Karplus Learning Cycle to convey the ideas of game theory in a way that is approachable, intuitive, and interdisciplinary. Noncooperative equilibrium concepts such as Nash equilibrium, mixed strategy equilibria, and subgame perfect equilibrium are systematically introduced in the first half of the book. Bayesian Nash equilibrium is briefly introduced. The subsequent chapters discuss cooperative solutions with and without side payments, rationalizable strategies and correlated equilibria, and applications to elections, social mechanism design, and larger-scale games. New examples include panic buying, supply-chain shifts in the pandemic, and global warming.
A Journey Into the World of Exponential Functions
This book illustrates why abstract mathematical entities are needed to represent some aspects of physical reality. It provides an overview of different types of numbers and functions along with their historical background and applications.
Intermittent Convex Integration for the 3D Euler Equations
A new threshold for the existence of weak solutions to the incompressible Euler equations To gain insight into the nature of turbulent fluids, mathematicians start from experimental facts, translate them into mathematical properties for solutions of the fundamental fluids PDEs, and construct solutions to these PDEs that exhibit turbulent properties. This book belongs to such a program, one that has brought convex integration techniques into hydrodynamics. Convex integration techniques have been used to produce solutions with precise regularity, which are necessary for the resolution of the Onsager conjecture for the 3D Euler equations, or solutions with intermittency, which are necessary for the construction of dissipative weak solutions for the Navier-Stokes equations. In this book, weak solutions to the 3D Euler equations are constructed for the first time with both non-negligible regularity and intermittency. These solutions enjoy a spatial regularity index in L^2 that can be taken as close as desired to 1/2, thus lying at the threshold of all known convex integration methods. This property matches the measured intermittent nature of turbulent flows. The construction of such solutions requires technology specifically adapted to the inhomogeneities inherent in intermittent solutions. The main technical contribution of this book is to develop convex integration techniques at the local rather than global level. This localization procedure functions as an ad hoc wavelet decomposition of the solution, carrying information about position, amplitude, and frequency in both Lagrangian and Eulerian coordinates.
More (Almost) Impossible Integrals, Sums, and Series
This book, the much-anticipated sequel to (Almost) Impossible, Integrals, Sums, and Series, presents a whole new collection of challenging problems and solutions that are not commonly found in classical textbooks. As in the author's previous book, these fascinating mathematical problems are shown in new and engaging ways, and illustrate the connections between integrals, sums, and series, many of which involve zeta functions, harmonic series, polylogarithms, and various other special functions and constants. Throughout the book, the reader will find both classical and new problems, with numerous original problems and solutions coming from the personal research of the author. Classical problems are shown in a fresh light, with new, surprising or unconventional ways of obtaining the desired results devised by the author. This book is accessible to readers with a good knowledge of calculus, from undergraduate students to researchers. It will appeal to all mathematical puzzlers who love a good integral or series and aren't afraid of a challenge.
Chaos, Complexity, and Nonlinear Economic Theory
What do economic chaos and uncertainties mean in rational or irrational economic theories? How do simple deterministic interactions among a few variables lead to unpredictable complex phenomena? Why is complexity of economies causing so many conflicts and confusions worldwide?This book provides a comprehensive introduction to recent developments of complexity theory in economics. It presents different models based on well-accepted economic mechanisms such as the Solow model, Ramsey model, and Lucas model. It is focused on presenting complex behaviors, such as business cycles, aperiodic motion, bifurcations, catastrophes, chaos, and hidden attractors, in basic economic models with nonlinear behavior. It shows how complex nonlinear phenomena are identified from various economic mechanisms and theories. These models demonstrate that the traditional or dominant economic views on evolution of, for instance, capitalism market, free competition, or Keynesian economics, are not generally valid. Markets are unpredictable and nobody knows with certainty the consequences of policies or other external factors in economic systems with simple interactions.
Discrete Event Simulation Using ExtendSim 10
This book characterizes the discrete event simulation and analysis using ExtendSim 10. It is a blend between theory and application leaning largely to the weight of the latter. Since the ExtendSim 8 version of the book (13 years ago) there has been significant improvements to ExtendSim, including the new Reliability library incorporated in this new, enhanced edition of the first book. There are two new chapters, one include a model simulating software reliability and inherent availability and the other is a guided project addressing the Launch Availability of a crew launch vehicle (CLV) for a limited launch window. For those unfamiliar with the first edition, there is coverage of just-enough queuing theory for building discrete event models, using the M/M/1 queuing problem involving warmup and steady-state phenomena, as well as methods for analysis and corrective adjustments. Probability distributions and their inverse transfers for random sampling are covered. The StatFit application is used for fitting and analyzing data, including goodness of fit testing. Also, there is an in-depth treatment of random number generators. A bank model is used to demonstrate hierarchical modeling and basic simulation animation. Advanced queuing processes are addressed using a circuit board production example. Detailed modeling is covered using a delivery system transfer depot handing packages for domestic delivery.
Guide to Advanced Statistical Analysis in R
Features- Worked examples- Clear programming code, usable by R novices- Without mathematical formulae- structural equation modeling- survival analysis- longitudinal analysis- multivariate analysis- GLM, Poisson regression, multilevel modeling- power analysis, reliability- Further reading recommendations in each chapter- Data sets on the website
Guide to Advanced Statistical Analysis in R
Features- Worked examples- Clear programming code, usable by R novices- Without mathematical formulae- structural equation modeling- survival analysis- longitudinal analysis- multivariate analysis- GLM, Poisson regression, multilevel modeling- power analysis, reliability- Further reading recommendations in each chapter- Data sets on the website
Statistical Testing with jamovi Sport
Features Worked examples from sportClearly written, without mathematical formulaeWell-structured, for beginners and intermediate readersComprehensive chapter on categorical analysis - not just 'Chi squared'Effect sizes and confidence intervalsClear explanation of factor analysisCovers MANOVA and logistic regressionCovers partial correlations, survival analysis and cluster analysisIntroduces Bayesian statisticsReproduces a well-received short chapter on making presentationsData sets and case studies on the website
Statistical Testing with jamovi Sport
FeaturesWorked examples from sportClearly written, without mathematical formulaeWell-structured, for beginners and intermediate readersComprehensive chapter on categorical analysis - not just 'Chi squared'Effect sizes and confidence intervalsClear explanation of factor analysisCovers MANOVA and logistic regressionCovers partial correlations, survival analysis and cluster analysisIntroduces Bayesian statisticsReproduces a well-received short chapter on making presentationsData sets and case studies on the website
Multidimensional Stationary Time Series
This book gives a brief survey of the theory of multidimensional (multivariate), weakly stationary time series, with emphasis on dimension reduction and prediction.
Foundations of Quantitative Finance
Published under the collective title of Foundations of Quantitative Finance, this set of ten books presents the advanced mathematics finance professionals need to advance their careers. These books develop the theory most do not learn in Graduate Finance programs, or in most Financial Mathematics undergraduate and graduate courses.
Forecasting and Analytics with the Augmented Dynamic Adaptive Model (Adam)
Forecasting and Analytics with ADAM focuses on a time series model in Single Source of Error state space form, called "ADAM" (Augmented Dynamic Adaptive Model). The book demonstrates a holistic view to forecasting and time series analysis using dynamic models to solve real life problems.
