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Extreme Value Modeling and Risk Analysis Methods and Applications

Extreme Value Modeling and Risk Analysis Methods and Applications

Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics eliminating the need to sort through the massive amount of literature on the subject. After reviewing univariate extreme value analysis and multivariate extremes the book explains univariate extreme value mixture modeling threshold selection in extreme value analysis and threshold modeling of non-stationary extremes. It presents new results for block-maxima of vine copulas develops time series of extremes with applications from climatology describes max-autoregressive and moving maxima models for extremes and discusses spatial extremes and max-stable processes. The book then covers simulation and conditional simulation of max-stable processes; inference methodologies such as composite likelihood Bayesian inference and approximate Bayesian computation; and inferences about extreme quantiles and extreme dependence. It also explores novel applications of extreme value modeling including financial investments insurance and financial risk management weather and climate disasters clinical trials and sports statistics. Risk analyses related to extreme events require the combined expertise of statisticians and domain experts in climatology hydrology finance insurance sports and other fields. This book connects statistical/mathematical research with critical decision and risk assessment/management applications to stimulate more collaboration between these statisticians and specialists. | Extreme Value Modeling and Risk Analysis Methods and Applications

GBP 44.99
1

The New S Language

The New S Language

This book provides documentation for a new version of the S system released in 1988. The new S enhances the features that have made S popular: interactive computing flexible graphics data management and a large collection of functions. The new S features make possible new applications and higher-level programming including a single unified language user defined functions as first-class objects symbolic computations more accurate numerical calculations and a new approach to graphics. S now provides direct interfaces to the poowerful tool of the UNIX operating system and to algorithms implemented in Fortran and C. | The New S Language

GBP 325.00
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Basic Matrix Algebra with Algorithms and Applications

Basic Matrix Algebra with Algorithms and Applications

Clear prose tight organization and a wealth of examples and computational techniques make Basic Matrix Algebra with Algorithms and Applications an outstanding introduction to linear algebra. The author designed this treatment specifically for freshman majors in mathematical subjects and upper-level students in natural resources the social sciences business or any discipline that eventually requires an understanding of linear models. With extreme pedagogical clarity that avoids abstraction wherever possible the author emphasizes minimal polynomials and their computation using a Krylov algorithm. The presentation is highly visual and relies heavily on work with a graphing calculator to allow readers to focus on concepts and techniques rather than on tedious arithmetic. Supporting materials including test preparation Maple worksheets are available for download from the Internet. This unassuming but insightful and remarkably original treatment is organized into bite-sized clearly stated objectives. It goes well beyond the LACSG recommendations for a first course while still implementing their philosophy and core material. Classroom tested with great success it prepares readers well for the more advanced studies their fields ultimately will require.

GBP 175.00
1

Evaluating Climate Change Impacts

Evaluating Climate Change Impacts

Evaluating Climate Change Impacts discusses assessing and quantifying climate change and its impacts from a multi-faceted perspective of ecosystem social and infrastructure resilience given through a lens of statistics and data science. It provides a multi-disciplinary view on the implications of climate variability and shows how the new data science paradigm can help us to mitigate climate-induced risk and to enhance climate adaptation strategies. This book consists of chapters solicited from leading topical experts and presents their perspectives on climate change effects in two general areas: natural ecosystems and socio-economic impacts. The chapters unveil topics of atmospheric circulation climate modeling and long-term prediction; approach the problems of increasing frequency of extreme events sea level rise and forest fires as well as economic losses analysis of climate impacts for insurance agriculture fisheries and electric and transport infrastructures. The reader will be exposed to the current research using a variety of methods from physical modeling statistics and machine learning including the global circulation models (GCM) and ocean models statistical generalized additive models (GAM) and generalized linear models (GLM) state space and graphical models causality networks Bayesian ensembles a variety of index methods and statistical tests and machine learning methods. The reader will learn about data from various sources including GCM and ocean model outputs satellite observations and data collected by different agencies and research units. Many of the chapters provide references to open source software R and Python code that are available for implementing the methods.

GBP 54.99
1

Introduction to NFL Analytics with R

Introduction to NFL Analytics with R

It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plans and commentators throughout broadcasts regularly use terms such as air yards CPOE and EPA on a casual basis. This rapid growth combined with an increasing accessibility to NFL data has helped create a burgeoning amateur analytics movement highlighted by the NFL’s annual Big Data Bowl. Because learning a coding language can be a difficult enough endeavor Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to opting to provide step-by-step instructions in a structured jargon-free manner. Key Coverage: • Installing R RStudio and necessary packages • Working and becoming fluent in the tidyverse • Finding meaning in NFL data with examples from all the functions in the nflverse family of packages • Using NFL data to create eye-catching data visualizations • Building statistical models starting with simple regressions and progressing to advanced machine learning models using tidymodels and eXtreme Gradient Boosting The book is written for novices of R programming all the way to more experienced coders as well as audiences with differing expected outcomes. Professors can use Introduction to NFL Analytics with R to provide data science lessons through the lens of the NFL while students can use it as an educational tool to create robust visualizations and machine learning models for assignments. Journalists bloggers and arm-chair quarterbacks alike will find the book helpful to underpin their arguments by providing hard data and visualizations to back up their claims.

GBP 52.99
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Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques reinforced via realistic applications. The book is accessible and doesn’t prove theorems or dwell on mathematical theory. The goal is to present topics at an intuitive level with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth including Hidden Markov Models (HMM) Support Vector Machines (SVM) and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN) boosting Random Forests and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation Convolutional Neural Networks (CNN) Multilayer Perceptrons (MLP) and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented including Long Short-Term Memory (LSTM) Generative Adversarial Networks (GAN) Extreme Learning Machines (ELM) Residual Networks (ResNet) Deep Belief Networks (DBN) Bidirectional Encoder Representations from Transformers (BERT) and Word2Vec. Finally several cutting-edge deep learning topics are discussed including dropout regularization attention explainability and adversarial attacks. Most of the examples in the book are drawn from the field of information security with many of the machine learning and deep learning applications focused on malware. The applications presented serve to demystify the topics by illustrating the use of various learning techniques in straightforward scenarios. Some of the exercises in this book require programming and elementary computing concepts are assumed in a few of the application sections. However anyone with a modest amount of computing experience should have no trouble with this aspect of the book. Instructor resources including PowerPoint slides lecture videos and other relevant material are provided on an accompanying website: http://www. cs. sjsu. edu/~stamp/ML/.

GBP 62.99
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Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences

From the Foreword:While large-scale machine learning and data mining have greatly impacted a range of commercial applications their use in the field of Earth sciences is still in the early stages. This book edited by AshokSrivastava Ramakrishna Nemani and Karsten Steinhaeuser serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest…I hope that this book will inspire more computer scientists to focus on environmental applications and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences. Vipin Kumar University of MinnesotaLarge-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science computer science statistics and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources in the final chapter of the book.

GBP 44.99
1

Clinical Trial Methodology

Clinical Trial Methodology

Now viewed as its own scientific discipline clinical trial methodology encompasses the methods required for the protection of participants in a clinical trial and the methods necessary to provide a valid inference about the objective of the trial. Drawing from the authors’ courses on the subject as well as the first author’s more than 30 years working in the pharmaceutical industry Clinical Trial Methodology emphasizes the importance of statistical thinking in clinical research and presents the methodology as a key component of clinical research. From ethical issues and sample size considerations to adaptive design procedures and statistical analysis the book first covers the methodology that spans every clinical trial regardless of the area of application. Crucial to the generic drug industry bioequivalence clinical trials are then discussed. The authors describe a parallel bioequivalence clinical trial of six formulations incorporating group sequential procedures that permit sample size re-estimation. The final chapters incorporate real-world case studies of clinical trials from the authors’ own experiences. These examples include a landmark Phase III clinical trial involving the treatment of duodenal ulcers and Phase III clinical trials that contributed to the first drug approved for the treatment of Alzheimer’s disease. Aided by the U. S. FDA the U. S. National Institutes of Health the pharmaceutical industry and academia the area of clinical trial methodology has evolved over the last six decades into a scientific discipline. This guide explores the processes essential for developing and conducting a quality clinical trial protocol and providing quality data collection biostatistical analyses and a clinical study report all while maintaining the highest standards of ethics and excellence.

GBP 44.99
1

Artificial Intelligence and the Two Singularities

Artificial Intelligence and the Two Singularities

The science of AI was born a little over 60 years ago but for most of that time its achievements were modest. In 2012 it experienced a big bang when a branch of statistics called Machine Learning (and a sub-branch called Deep Learning) was applied to it. Now machines have surpassed humans in image recognition and they are catching up with us at speech recognition and natural language processing. Every day the media reports the launch of a new service a new product and a new demonstration powered by AI. When will it end? The surprising truth is the AI revolution has only just begun. Artificial Intelligence and the Two Singularities argues that in the course of this century the exponential growth in the capability of AI is likely to bring about two singularities - points at which conditions are so extreme that the normal rules break down. The first is the economic singularity when machine skill reaches a level that renders many of us unemployable and requires an overhaul of our current economic and social systems. The second is the technological singularity when machine intelligence reaches and then surpasses the cognitive abilities of an adult human relegating us to the second smartest species on the planet. These singularities will present huge challenges but this book argues that we can meet these challenges and overcome them. If we do the rewards could be almost unimaginable. This book covers: • Recent developments in AI and its future potential • The economic singularity and the technological singularity in depth • The risks and opportunities presented by AI • What actions we should take Artificial intelligence can turn out to be the best thing ever to happen to humanity making our future wonderful almost beyond imagination. But only if we address head-on the challenges that it will raise. Calum Chace is a best-selling author of fiction and non-fiction books and articles focusing on the subject of artificial intelligence. He is a regular speaker on artificial intelligence and related technologies and runs a blog on the subject at www. pandoras-brain. com. Prior to becoming a full-time writer and speaker he spent 30 years in business as a marketer a strategy consultant and a CEO. He studied philosophy at Oxford University where he discovered that the science fiction he had been reading since boyhood was simply philosophy in fancy dress.

GBP 46.99
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Programming for Hybrid Multi/Manycore MPP Systems

Programming for Hybrid Multi/Manycore MPP Systems

Ask not what your compiler can do for you ask what you can do for your compiler. John Levesque Director of Cray’s Supercomputing Centers of ExcellenceThe next decade of computationally intense computing lies with more powerful multi/manycore nodes where processors share a large memory space. These nodes will be the building block for systems that range from a single node workstation up to systems approaching the exaflop regime. The node itself will consist of 10’s to 100’s of MIMD (multiple instruction multiple data) processing units with SIMD (single instruction multiple data) parallel instructions. Since a standard affordable memory architecture will not be able to supply the bandwidth required by these cores new memory organizations will be introduced. These new node architectures will represent a significant challenge to application developers. Programming for Hybrid Multi/Manycore MPP Systems attempts to briefly describe the current state-of-the-art in programming these systems and proposes an approach for developing a performance-portable application that can effectively utilize all of these systems from a single application. The book starts with a strategy for optimizing an application for multi/manycore architectures. It then looks at the three typical architectures covering their advantages and disadvantages. The next section of the book explores the other important component of the target—the compiler. The compiler will ultimately convert the input language to executable code on the target and the book explores how to make the compiler do what we want. The book then talks about gathering runtime statistics from running the application on the important problem sets previously discussed. How best to utilize available memory bandwidth and virtualization is covered next along with hybridization of a program. The last part of the book includes several major applications and examines future hardware advancements and how the application developer may prepare for those advancements.

GBP 44.99
1

Random Circulant Matrices

Random Circulant Matrices

Circulant matrices have been around for a long time and have been extensively used in many scientific areas. This book studies the properties of the eigenvalues for various types of circulant matrices such as the usual circulant the reverse circulant and the k-circulant when the dimension of the matrices grow and the entries are random. In particular the behavior of the spectral distribution of the spectral radius and of the appropriate point processes are developed systematically using the method of moments and the various powerful normal approximation results. This behavior varies according as the entries are independent are from a linear process and are light- or heavy-tailed. Arup Bose obtained his B. Stat. M. Stat. and Ph. D. degrees from the Indian Statistical Institute. He has been on its faculty at the Theoretical Statistics and Mathematics Unit Kolkata India since 1991. He is a Fellow of the Institute of Mathematical Statistics and of all three national science academies of India. He is a recipient of the S. S. Bhatnagar Prize and the C. R. Rao Award. He is the author of three books: Patterned Random Matrices Large Covariance and Autocovariance Matrices (with Monika Bhattacharjee) and U-Statistics M_m-Estimators and Resampling (with Snigdhansu Chatterjee). Koushik Saha obtained a B. Sc. in Mathematics from Ramakrishna Mission Vidyamandiara Belur and an M. Sc. in Mathematics from Indian Institute of Technology Bombay. He obtained his Ph. D. degree from the Indian Statistical Institute under the supervision of Arup Bose. His thesis on circulant matrices received high praise from the reviewers. He has been on the faculty of the Department of Mathematics Indian Institute of Technology Bombay since 2014. | Random Circulant Matrices

GBP 44.99
1

Statistical Machine Learning A Unified Framework

Statistical Machine Learning A Unified Framework

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing analyzing evaluating and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students engineers and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular the material in this text directly supports the mathematical analysis and design of old new and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised unsupervised and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive batch minibatch MCEM and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics computer science electrical engineering and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students professional engineers and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph. D. M. S. E. E. B. S. E. E. ) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models. | Statistical Machine Learning A Unified Framework

GBP 99.99
1

Direct Sum Decompositions of Torsion-Free Finite Rank Groups

Direct Sum Decompositions of Torsion-Free Finite Rank Groups

With plenty of new material not found in other books Direct Sum Decompositions of Torsion-Free Finite Rank Groups explores advanced topics in direct sum decompositions of abelian groups and their consequences. The book illustrates a new way of studying these groups while still honoring the rich history of unique direct sum decompositions of groups. Offering a unified approach to theoretic concepts this reference covers isomorphism endomorphism refinement the Baer splitting property Gabriel filters and endomorphism modules. It shows how to effectively study a group G by considering finitely generated projective right End(G)-modules the left End(G)-module G and the ring E(G) = End(G)/N(End(G)). For instance one of the naturally occurring properties considered is when E(G) is a commutative ring. Modern algebraic number theory provides results concerning the isomorphism of locally isomorphic rtffr groups finitely faithful S-groups that are J-groups and each rtffr L-group that is a J-group. The book concludes with useful appendices that contain background material and numerous examples.

GBP 59.99
1

The Shape of Space

The Shape of Space

The Shape of Space Third Edition maintains the standard of excellence set by the previous editions. This lighthearted textbook covers the basic geometry and topology of two- and three-dimensional spaces—stretching students’ minds as they learn to visualize new possibilities for the shape of our universe. Written by a master expositor leading researcher in the field and MacArthur Fellow its informal exposition and engaging exercises appeal to an exceptionally broad audience from liberal arts students to math undergraduate and graduate students looking for a clear intuitive understanding to supplement more formal texts and even to laypeople seeking an entertaining self-study book to expand their understanding of space. Features of the Third Edition: Full-color figures throughout Picture proofs have replaced algebraic proofs Simpler handles-and-crosscaps approach to surfaces Updated discussion of cosmological applications Intuitive examples missing from many college and graduate school curricula About the Author: Jeffrey R. Weeks is a freelance geometer living in Canton New York. With support from the U. S. National Science Foundation the MacArthur Foundation and several science museums his work spans pure mathematics applications in cosmology and—closest to his heart—exposition for the general public.

GBP 44.99
1

Algebraic Number Theory A Brief Introduction

Algebraic Number Theory A Brief Introduction

This book offers the basics of algebraic number theory for students and others who need an introduction and do not have the time to wade through the voluminous textbooks available. It is suitable for an independent study or as a textbook for a first course on the topic. The author presents the topic here by first offering a brief introduction to number theory and a review of the prerequisite material then presents the basic theory of algebraic numbers. The treatment of the subject is classical but the newer approach discussed at the end provides a broader theory to include the arithmetic of algebraic curves over finite fields and even suggests a theory for studying higher dimensional varieties over finite fields. It leads naturally to the Weil conjecture and some delicate questions in algebraic geometry. About the Author Dr. J. S. Chahal is a professor of mathematics at Brigham Young University. He received his Ph. D. from Johns Hopkins University and after spending a couple of years at the University of Wisconsin as a post doc he joined Brigham Young University as an assistant professor and has been there ever since. He specializes and has published several papers in number theory. For hobbies he likes to travel and hike. His book Fundamentals of Linear Algebra is also published by CRC Press. | Algebraic Number Theory A Brief Introduction

GBP 52.99
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Measuring Society

Measuring Society

Collecting and analyzing data on unemployment inflation and inequality help describe the complex world around us. When published by the government such data are called official statistics. They are reported by the media used by politicians to lend weight to their arguments and by economic commentators to opine about the state of society. Despite such widescale use explanations about how these measures are constructed are seldom provided for a non-technical reader. This Measuring Society book is a short accessible guide to six topics: jobs house prices inequality prices for goods and services poverty and deprivation. Each relates to concepts we use on a personal level to form an understanding of the society in which we live: We need a job a place to live and food to eat. Using data from the United States we answer three basic questions: why how and for whom these statistics have been constructed. We add some context and flavor by discussing the historical background. This book provides the reader with a good grasp of these measures. Chaitra H. Nagaraja is an Associate Professor of Statistics at the Gabelli School of Business at Fordham University in New York. Her research interests include house price indices and inequality measurement. Prior to Fordham Dr. Nagaraja was a researcher at the U. S. Census Bureau. While there she worked on projects relating to the American Community Survey.

GBP 24.99
1

A Primer on Wavelets and Their Scientific Applications

A Primer on Wavelets and Their Scientific Applications

In the first edition of his seminal introduction to wavelets James S. Walker informed us that the potential applications for wavelets were virtually unlimited. Since that time thousands of published papers have proven him true while also necessitating the creation of a new edition of his bestselling primer. Updated and fully revised to include the latest developments this second edition of A Primer on Wavelets and Their Scientific Applications guides readers through the main ideas of wavelet analysis in order to develop a thorough appreciation of wavelet applications. Ingeniously relying on elementary algebra and just a smidgen of calculus Professor Walker demonstrates how the underlying ideas behind wavelet analysis can be applied to solve significant problems in audio and image processing as well in biology and medicine. Nearly twice as long as the original this new edition provides 104 worked examples and 222 exercises constituting a veritable book of review material Two sections on biorthogonal wavelets A mini-course on image compression including a tutorial on arithmetic compression Extensive material on image denoising featuring a rarely covered technique for removing isolated randomly positioned clutter Concise yet complete coverage of the fundamentals of time-frequency analysis showcasing its application to audio denoising and musical theory and synthesis An introduction to the multiresolution principle a new mathematical concept in musical theory Expanded suggestions for research projects An enhanced list of references

GBP 180.00
1

Banach Limit and Applications

Banach Limit and Applications

Banach Limit and Applications provides all the results in the area of Banach Limit its extensions generalizations and applications to various fields in one go (as far as possible). All the results in this field after Banach introduced this concept in 1932 were scattered till now. Sublinear functionals generating and dominating Banach Limit unique Banach Limit (almost convergence) invariant means and invariant limits absolute and strong almost convergence applications to ergodicity law of large numbers Fourier series uniform distribution of sequences uniform density core theorems and functional Banach limits are discussed in this book. The discovery of functional analysis such as the Hahn-Banach Theorem and the Banach-Steinhaus Theorem helped the researchers to develop a modern rich and unified theory of sequence spaces by encompassing classical summability theory via matrix transformations and the topics related to sequence spaces which arose from the concept of Banach limits all of which are presented in this book. The unique features of this book are as follows: All the results in this area which were scattered till now are in one place. The book is the first of its kind in the sense that there is no other competitive book. The contents of this monograph did not appear in any book form before. The audience of this book are the researchers in this area and Ph. D. and advanced master’s students. The book is suitable for one- or two-semester course work for Ph. D. students M. S. students in North America and Europe and M. Phil. and master’s students in India.

GBP 130.00
1

Time Series A First Course with Bootstrap Starter

Time Series A First Course with Bootstrap Starter

Time Series: A First Course with Bootstrap Starter provides an introductory course on time series analysis that satisfies the triptych of (i) mathematical completeness (ii) computational illustration and implementation and (iii) conciseness and accessibility to upper-level undergraduate and M. S. students. Basic theoretical results are presented in a mathematically convincing way and the methods of data analysis are developed through examples and exercises parsed in R. A student with a basic course in mathematical statistics will learn both how to analyze time series and how to interpret the results. The book provides the foundation of time series methods including linear filters and a geometric approach to prediction. The important paradigm of ARMA models is studied in-depth as well as frequency domain methods. Entropy and other information theoretic notions are introduced with applications to time series modeling. The second half of the book focuses on statistical inference the fitting of time series models as well as computational facets of forecasting. Many time series of interest are nonlinear in which case classical inference methods can fail but bootstrap methods may come to the rescue. Distinctive features of the book are the emphasis on geometric notions and the frequency domain the discussion of entropy maximization and a thorough treatment of recent computer-intensive methods for time series such as subsampling and the bootstrap. There are more than 600 exercises half of which involve R coding and/or data analysis. Supplements include a website with 12 key data sets and all R code for the book's examples as well as the solutions to exercises. | Time Series A First Course with Bootstrap Starter

GBP 38.99
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A Course in Categorical Data Analysis

A Course in Categorical Data Analysis

Categorical data-comprising counts of individuals objects or entities in different categories-emerge frequently from many areas of study including medicine sociology geology and education. They provide important statistical information that can lead to real-life conclusions and the discovery of fresh knowledge. Therefore the ability to manipulate understand and interpret categorical data becomes of interest-if not essential-to professionals and students in a broad range of disciplines. Although t-tests linear regression and analysis of variance are useful valid methods for analysis of measurement data categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. Developed from long experience in teaching categorical analysis to a multidisciplinary mix of undergraduate and graduate students A Course in Categorical Data Analysis presents the easiest most straightforward ways of extracting real-life conclusions from contingency tables. The author uses a Fisherian approach to categorical data analysis and incorporates numerous examples and real data sets. Although he offers S-PLUS routines through the Internet readers do not need full knowledge of a statistical software package. In this unique text the author chooses methods and an approach that nurtures intuitive thinking. He trains his readers to focus not on finding a model that fits the data but on using different models that may lead to meaningful conclusions. The book offers some simple innovative techniques not highighted in other texts that help make the book accessible to a broad interdisciplinary audience. A Course in Categorical Data Analysis enables readers to quickly use its offering of tools for drawing scientific medical or real-life conclusions from categorical data sets.

GBP 170.00
1

Statistics and Health Care Fraud How to Save Billions

Statistics and Health Care Fraud How to Save Billions

Statistics and Health Care Fraud: How to Save Billions helps the public to become more informed citizens through discussions of real world health care examples and fraud assessment applications. The author presents statistical and analytical methods used in health care fraud audits without requiring any mathematical background. The public suffers from health care overpayments either directly as patients or indirectly as taxpayers and fraud analytics provides ways to handle the large size and complexity of these claims. The book starts with a brief overview of global healthcare systems such as U. S. Medicare. This is followed by a discussion of medical overpayments and assessment initiatives using a variety of real world examples. The book covers subjects as: • Description and visualization of medical claims data • Prediction of fraudulent transactions • Detection of excessive billings • Revealing new fraud patterns • Challenges and opportunities with health care fraud analytics Dr. Tahir Ekin is the Brandon Dee Roberts Associate Professor of Quantitative Methods in McCoy College of Business Texas State University. His previous work experience includes a working as a statistician on health care fraud detection. His scholarly work on health care fraud has been published in a variety of academic journals including International Statistical Review The American Statistician and Applied Stochastic Models in Business and Industry. He is a recipient of the Texas State University 2018 Presidential Distinction Award in Scholar Activities and the ASA/NISS y-Bis 2016 Best Paper Awards. He has developed and taught courses in the areas of business statistics optimization data mining and analytics. Dr. Ekin also serves as Vice President of the International Society for Business and Industrial Statistics. | Statistics and Health Care Fraud How to Save Billions

GBP 24.99
1

Introduction to Information Theory and Data Compression

Introduction to Information Theory and Data Compression

An effective blend of carefully explained theory and practical applications this text imparts the fundamentals of both information theory and data compression. Although the two topics are related this unique text allows either topic to be presented independently and it was specifically designed so that the data compression section requires no prior knowledge of information theory. The treatment of information theory while theoretical and abstract is quite elementary making this text less daunting than many others. After presenting the fundamental definitions and results of the theory the authors then apply the theory to memoryless discrete channels with zeroth-order one-state sources. The chapters on data compression acquaint students with a myriad of lossless compression methods and then introduce two lossy compression methods. Students emerge from this study competent in a wide range of techniques. The authors' presentation is highly practical but includes some important proofs either in the text or in the exercises so instructors can if they choose place more emphasis on the mathematics. Introduction to Information Theory and Data Compression Second Edition is ideally suited for an upper-level or graduate course for students in mathematics engineering and computer science. Features:Expanded discussion of the historical and theoretical basis of information theory that builds a firm intuitive grasp of the subjectReorganization of theoretical results along with new exercises ranging from the routine to the more difficult that reinforce students' ability to apply the definitions and results in specific situations. Simplified treatment of the algorithm(s) of Gallager and KnuthDiscussion of the information rate of a code and the trade-off between error correction and information rateTreatment of probabilistic finite state source automata including basic resul

GBP 59.99
1

Benefit-Risk Assessment Methods in Medical Product Development Bridging Qualitative and Quantitative Assessments

Benefit-Risk Assessment Methods in Medical Product Development Bridging Qualitative and Quantitative Assessments

Guides You on the Development and Implementation of B–R EvaluationsBenefit–Risk Assessment Methods in Medical Product Development: Bridging Qualitative and Quantitative Assessments provides general guidance and case studies to aid practitioners in selecting specific benefit–risk (B–R) frameworks and quantitative methods. Leading experts from industry regulatory agencies and academia present practical examples lessons learned and best practices that illustrate how to conduct structured B–R assessment in clinical development and regulatory submission. The first section of the book discusses the role of B–R assessments in medicine development and regulation the need for both a common B–R framework and patient input into B–R decisions and future directions. The second section focuses on legislative and regulatory policy initiatives as well as decisions made at the U. S. FDA’s Center for Devices and Radiological Health. The third section examines key elements of B–R evaluations in a product’s life cycle such as uncertainty evaluation and quantification quantifying patient B–R trade-off preferences ways to identify subgroups with the best B–R profiles and data sources used to assist B–R assessment. The fourth section equips practitioners with tools to conduct B–R evaluations including assessment methodologies a quantitative joint modeling and joint evaluation framework and several visualization tools. The final section presents a rich collection of case studies. With top specialists sharing their in-depth knowledge thought-provoking considerations and practical advice this book offers comprehensive coverage of B–R evaluation methods tools and case studies. It gives practitioners a much-needed toolkit to develop and conduct their own B–R evaluations. | Benefit-Risk Assessment Methods in Medical Product Development Bridging Qualitative and Quantitative Assessments

GBP 44.99
1

Modeling and Inverse Problems in the Presence of Uncertainty

Modeling and Inverse Problems in the Presence of Uncertainty

Modeling and Inverse Problems in the Presence of Uncertainty collects recent research—including the authors’ own substantial projects—on uncertainty propagation and quantification. It covers two sources of uncertainty: where uncertainty is present primarily due to measurement errors and where uncertainty is present due to the modeling formulation itself. After a useful review of relevant probability and statistical concepts the book summarizes mathematical and statistical aspects of inverse problem methodology including ordinary weighted and generalized least-squares formulations. It then discusses asymptotic theories bootstrapping and issues related to the evaluation of correctness of assumed form of statistical models. The authors go on to present methods for evaluating and comparing the validity of appropriateness of a collection of models for describing a given data set including statistically based model selection and comparison techniques. They also explore recent results on the estimation of probability distributions when they are embedded in complex mathematical models and only aggregate (not individual) data are available. In addition they briefly discuss the optimal design of experiments in support of inverse problems for given models. The book concludes with a focus on uncertainty in model formulation itself covering the general relationship of differential equations driven by white noise and the ones driven by colored noise in terms of their resulting probability density functions. It also deals with questions related to the appropriateness of discrete versus continuum models in transitions from small to large numbers of individuals. With many examples throughout addressing problems in physics biology and other areas this book is intended for applied mathematicians interested in deterministic and/or stochastic models and their interactions. It is also s

GBP 59.99
1

The Sharpe Ratio Statistics and Applications

The Sharpe Ratio Statistics and Applications

The Sharpe ratio is the most widely used metric for comparing theperformance of financial assets. The Markowitz portfolio is the portfolio withthe highest Sharpe ratio. The Sharpe Ratio: Statistics and Applications examines the statistical propertiesof the Sharpe ratio and Markowitz portfolio both under the simplifying assumption of Gaussian returns and asymptotically. Connections are drawn between the financial measures and classical statistics includingStudent's t Hotelling's T^2 and the Hotelling-Lawley trace. The robustness of these statistics to heteroskedasticity autocorrelation fat tails and skew of returns are considered. The construction of portfolios to maximizethe Sharpe is expanded from the usual static unconditional model to include subspace constraints heding out assets and the use of conditioning information on both expected returns and risk. {book title} is the most comprehensivetreatment of the statistical properties of the Sharpe ratio and Markowitzportfolio ever published. Features: * Material on single asset problems market timing unconditional and conditional portfolio problems hedged portfolios. * Inference via both Frequentist and Bayesian paradigms. *A comprehensive treatment of overoptimism and overfitting of trading strategies. *Advice on backtesting strategies. *Dozens of examples and hundreds of exercises for self study. This book is an essential reference for the practicing quant strategist and the researcher alike and an invaluable textbook for the student. Steven E. Pav holds a PhD in mathematics from Carnegie Mellon University and degrees in mathematics and ceramic engineering sciencefrom Indiana University Bloomington and Alfred University. He was formerly a quantitative strategist at Convexus Advisors and CerebellumCapital and a quantitative analyst at Bank of America. He is the author of a dozen R packages including those for analyzing the significance of the Sharpe ratio and Markowitz portfolio. He writes about the Sharpe ratio at https://protect-us. mimecast. com/s/BUveCPNMYvt0vnwX8Cj689u?domain=sharperat. io . | The Sharpe Ratio Statistics and Applications

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