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Model-free Hedging A Martingale Optimal Transport Viewpoint

Model-free Hedging: A Martingale Optimal Transport Viewpoint focuses on the computation of model-independent bounds for exotic options consistent with market prices of liquid instruments such as Vanilla options. The author gives an overview of Martingale Optimal Transport highlighting the differences between the optimal transport and its martingale counterpart. This topic is then discussed in the context of mathematical finance. | Model-free Hedging A Martingale Optimal Transport Viewpoint

GBP 46.99

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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

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Introduction To The Calculus of Variations And Its Applications

This comprehensive text provides all information necessary for an introductory course on the calculus of variations and optimal control theory. Following a thorough discussion of the basic problem including sufficient conditions for optimality the theory and techniques are extended to problems with a free end point a free boundary auxiliary and inequality constraints leading to a study of optimal control theory. | Introduction To The Calculus of Variations And Its Applications

GBP 56.99

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Computational Fluid Dynamics

Exploring new variations of classical methods as well as recent approaches appearing in the field Computational Fluid Dynamics demonstrates the extensive use of numerical techniques and mathematical models in fluid mechanics. It presents various numerical methods including finite volume finite difference finite element spectral smoothed particle hydrodynamics (SPH) mixed-element-volume and free surface flow. Taking a unified point of view the book first introduces the basis of finite volume weighted residual and spectral approaches. The contributors present the SPH method a novel approach of computational fluid dynamics based on the mesh-free technique and then improve the method using an arbitrary Lagrange Euler (ALE) formalism. They also explain how to improve the accuracy of the mesh-free integration procedure with special emphasis on the finite volume particle method (FVPM). After describing numerical algorithms for compressible computational fluid dynamics the text discusses the prediction of turbulent complex flows in environmental and engineering problems. The last chapter explores the modeling and numerical simulation of free surface flows including future behaviors of glaciers. The diverse applications discussed in this book illustrate the importance of numerical methods in fluid mechanics. With research continually evolving in the field there is no doubt that new techniques and tools will emerge to offer greater accuracy and speed in solving and analyzing even more fluid flow problems.

GBP 59.99

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Introduction to Statistical Modelling and Inference

The complexity of large-scale data sets (“Big Data”) has stimulated the development of advanced computational methods for analysing them. There are two different kinds of methods to aid this. The model-based method uses probability models and likelihood and Bayesian theory while the model-free method does not require a probability model likelihood or Bayesian theory. These two approaches are based on different philosophical principles of probability theory espoused by the famous statisticians Ronald Fisher and Jerzy Neyman. Introduction to Statistical Modelling and Inference covers simple experimental and survey designs and probability models up to and including generalised linear (regression) models and some extensions of these including finite mixtures. A wide range of examples from different application fields are also discussed and analysed. No special software is used beyond that needed for maximum likelihood analysis of generalised linear models. Students are expected to have a basic mathematical background in algebra coordinate geometry and calculus. Features• Probability models are developed from the shape of the sample empirical cumulative distribution function (cdf) or a transformation of it. • Bounds for the value of the population cumulative distribution function are obtained from the Beta distribution at each point of the empirical cdf. • Bayes’s theorem is developed from the properties of the screening test for a rare condition. • The multinomial distribution provides an always-true model for any randomly sampled data. • The model-free bootstrap method for finding the precision of a sample estimate has a model-based parallel – the Bayesian bootstrap – based on the always-true multinomial distribution. • The Bayesian posterior distributions of model parameters can be obtained from the maximum likelihood analysis of the model. This book is aimed at students in a wide range of disciplines including Data Science. The book is based on the model-based theory used widely by scientists in many fields and compares it in less detail with the model-free theory popular in computer science machine learning and official survey analysis. The development of the model-based theory is accelerated by recent developmentsin Bayesian analysis.

GBP 82.99

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Real World AI Ethics for Data Scientists Practical Case Studies

In the midst of the fourth industrial revolution big data is weighed in gold placing enormous power in the hands of data scientists – the modern AI alchemists. But great power comes with greater responsibility. This book seeks to shape in a practical diverse and inclusive way the ethical compass of those entrusted with big data. Being practical this book provides seven real-world case studies dealing with big data abuse. These cases span a range of topics from the statistical manipulation of research in the Cornell food lab through the Facebook user data abuse done by Cambridge Analytica to the abuse of farm animals by AI in a chapter co-authored by renowned philosophers Peter Singer and Yip Fai Tse. Diverse and inclusive given the global nature of this revolution this book provides case-by-case commentary on the cases by scholars representing non-Western ethical approaches (Buddhist Jewish Indigenous and African) as well as Western approaches (consequentialism deontology and virtue). We hope this book will be a lighthouse for those debating ethical dilemmas in this challenging and ever-evolving field. | Real World AI Ethics for Data Scientists Practical Case Studies

GBP 42.99

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Applications of Regression for Categorical Outcomes Using R

This book covers the main models within the GLM (i. e. logistic Poisson negative binomial ordinal and multinomial). For each model estimations interpretations model fit diagnostics and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata SPSS and SAS to using R and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge and for Quantitative social scientists due to it’s ability to act as a practitioners guide. Key Features: Applied- in the sense that we will provide code that others can easily adapt Flexible- R is basically just a fancy calculator. Our programs will enable users to derive quantities that they can use in their work Timely- many in the social sciences are currently transitioning to R or are learning it now. Our book will be a useful resource Versatile- we will write functions into an R package that can be applied to all of the regression models we will cover in the book Aesthetically pleasing- one advantage of R relative to other software packages is that graphs are fully customizable. We will leverage this feature to yield high-end graphical displays of results Affordability- R is free. R packages are free. There is no need to purchase site licenses or updates.

GBP 59.99

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Metabolomics Practical Guide to Design and Analysis

Metabolomics is the scientific study of the chemical processes in a living system environment and nutrition. It is a relatively new omics science but the potential applications are wide including medicine personalized medicine and intervention studies food and nutrition plants agriculture and environmental science. The topics presented and discussed in this book are based on the European Molecular Biology Organization (EMBO) practical courses in metabolomics bioinformatics taught to those working in the field from masters to postgraduate students PhDs postdoctoral and early PIs. The book covers the basics and fundamentals of data acquisition and analytical technologies but the primary focus is data handling and data analysis. The mentioning and usage of a particular data analysis tool has been avoided; rather the focus is on the concepts and principles of data processing and analysis. The material has been class-tested and includes lots of examples computing and exercises. Key Features:Provides an overview of qualitative /quantitative methods in metabolomicsOffers an introduction to the key concepts of metabolomics including experimental design and technologyCovers data handling processing analysis data standards and sharingContains lots of examples to illustrate the topicsIncludes contributions from some of the leading researchers in the field of metabolomics with extensive teaching experiences | Metabolomics Practical Guide to Design and Analysis

GBP 44.99

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Hands-On Data Analysis in R for Finance

The subject of this textbook is to act as an introduction to data science / data analysis applied to finance using R and its most recent and freely available extension libraries. The targeted academic level is undergrad students with a major in data science and/or finance and graduate students and of course practitioners or professionals who need a desk reference. Assumes no prior knowledge of R The content has been tested in actual university classes Makes the reader proficient in advanced methods such as machine learning time series analysis principal component analysis and more Gives comprehensive and detailed explanations on how to use the most recent and free resources such as financial and statistics libraries or open database on the internet | Hands-On Data Analysis in R for Finance

GBP 74.99

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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

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Statistical Design and Analysis of Stability Studies

The US Food and Drug Administration's Report to the Nation in 2004 and 2005 indicated that one of the top reasons for drug recall was that stability data did not support existing expiration dates. Pharmaceutical companies conduct stability studies to characterize the degradation of drug products and to estimate drug shelf life. Illustrating how stability studies play an important role in drug safety and quality assurance Statistical Design and Analysis of Stability Studies presents the principles and methodologies in the design and analysis of stability studies. After introducing the basic concepts of stability testing the book focuses on short-term stability studies and reviews several methods for estimating drug expiration dating periods. It then compares some commonly employed study designs and discusses both fixed and random batch statistical analyses. Following a chapter on the statistical methods for stability analysis under a linear mixed effects model the book examines stability analyses with discrete responses multiple components and frozen drug products. In addition the author provides statistical methods for dissolution testing and explores current issues and recent developments in stability studies. To ensure the safety of consumers professionals in the field must carry out stability studies to determine the reliability of drug products during their expiration period. This book provides the material necessary for you to perform stability designs and analyses in pharmaceutical research and development.

GBP 44.99

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Monomial Algebras

Monomial Algebras Second Edition presents algebraic combinatorial and computational methods for studying monomial algebras and their ideals including Stanley–Reisner rings monomial subrings Ehrhart rings and blowup algebras. It emphasizes square-free monomials and the corresponding graphs clutters or hypergraphs. New to the Second Edition Four new chapters that focus on the algebraic properties of blowup algebras in combinatorial optimization problems of clutters and hypergraphs Two new chapters that explore the algebraic and combinatorial properties of the edge ideal of clutters and hypergraphs Full revisions of existing chapters to provide an up-to-date account of the subject Bringing together several areas of pure and applied mathematics this book shows how monomial algebras are related to polyhedral geometry combinatorial optimization and combinatorics of hypergraphs. It directly links the algebraic properties of monomial algebras to combinatorial structures (such as simplicial complexes posets digraphs graphs and clutters) and linear optimization problems.

GBP 74.99

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Bioequivalence and Statistics in Clinical Pharmacology

Maintaining a practical perspective Bioequivalence and Statistics in Clinical Pharmacology Second Edition explores statistics used in day-to-day clinical pharmacology work. The book is a starting point for those involved in such research and covers the methods needed to design analyze and interpret bioequivalence trials; explores when how and why these studies are performed as part of drug development; and demonstrates the methods using real world examples. Drawing on knowledge gained directly from working in the pharmaceutical industry the authors set the stage by describing the general role of statistics. Once the foundation of clinical pharmacology drug development regulatory applications and the design and analysis of bioequivalence trials are established including recent regulatory changes in design and analysis and in particular sample-size adaptation they move on to related topics in clinical pharmacology involving the use of cross-over designs. These include but are not limited to safety studies in Phase I dose-response trials drug interaction trials food-effect and combination trials QTc and other pharmacodynamic equivalence trials proof-of-concept trials dose-proportionality trials and vaccines trials. This second edition addresses several recent developments in the field including new chapters on adaptive bioequivalence studies scaled average bioequivalence testing and vaccine trials. Purposefully designed to be instantly applicable Bioequivalence and Statistics in Clinical Pharmacology Second Edition provides examples of SAS and R code so that the analyses described can be immediately implemented. The authors have made extensive use of the proc mixed procedures available in SAS.

GBP 44.99

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Monte Carlo Simulation with Applications to Finance

Developed from the author’s course on Monte Carlo simulation at Brown University Monte Carlo Simulation with Applications to Finance provides a self-contained introduction to Monte Carlo methods in financial engineering. It is suitable for advanced undergraduate and graduate students taking a one-semester course or for practitioners in the financial industry. The author first presents the necessary mathematical tools for simulation arbitrary free option pricing and the basic implementation of Monte Carlo schemes. He then describes variance reduction techniques including control variates stratification conditioning importance sampling and cross-entropy. The text concludes with stochastic calculus and the simulation of diffusion processes. Only requiring some familiarity with probability and statistics the book keeps much of the mathematics at an informal level and avoids technical measure-theoretic jargon to provide a practical understanding of the basics. It includes a large number of examples as well as MATLAB® coding exercises that are designed in a progressive manner so that no prior experience with MATLAB is needed.

GBP 59.99

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The Essentials of Data Science: Knowledge Discovery Using R

The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years’ experience in teaching and practising data science the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.

GBP 145.00

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Quantitative Finance with Python A Practical Guide to Investment Management Trading and Financial Engineering

Quantitative Finance with Python: A Practical Guide to Investment Management Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on rigorous introduction to foundational topics in quant finance such as options pricing portfolio optimization and machine learning. Simultaneously the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors. Features Useful as both a teaching resource and as a practical tool for professional investors. Ideal textbook for first year graduate students in quantitative finance programs such as those in master’s programs in Mathematical Finance Quant Finance or Financial Engineering. Includes a perspective on the future of quant finance techniques and in particular covers some introductory concepts of Machine Learning. Free-to-access repository with Python codes available at www. routledge. com/ 9781032014432 and on https://github. com/lingyixu/Quant-Finance-With-Python-Code. | Quantitative Finance with Python A Practical Guide to Investment Management Trading and Financial Engineering

GBP 99.99

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Inferential Models Reasoning with Uncertainty

A New Approach to Sound Statistical ReasoningInferential Models: Reasoning with Uncertainty introduces the authors’ recently developed approach to inference: the inferential model (IM) framework. This logical framework for exact probabilistic inference does not require the user to input prior information. The authors show how an IM produces meaningful prior-free probabilistic inference at a high level. The book covers the foundational motivations for this new IM approach the basic theory behind its calibration properties a number of important applications and new directions for research. It discusses alternative meaningful probabilistic interpretations of some common inferential summaries such as p-values. It also constructs posterior probabilistic inferential summaries without a prior and Bayes’ formula and offers insight on the interesting and challenging problems of conditional and marginal inference. This book delves into statistical inference at a foundational level addressing what the goals of statistical inference should be. It explores a new way of thinking compared to existing schools of thought on statistical inference and encourages you to think carefully about the correct approach to scientific inference. | Inferential Models Reasoning with Uncertainty

GBP 44.99

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New Centrality Measures in Networks How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes

Over the last number of years there has been a growing interest in the analysis of complex networks which describe a wide range of real-world systems in nature and society. Identification of the central elements in such networks is one of the key research areas. Solutions to this problem are important for making strategic decisions and studying the behavior of dynamic processes e. g. epidemic spread. The importance of nodes has been studied using various centrality measures. Generally it should be considered that most real systems are not homogeneous: nodes may have individual attributes and influence each other in groups while connections between nodes may describe different types of relations. Thus critical nodes detection is not a straightforward process. New Centrality Measures in Networks presents a class of new centrality measures which take into account individual attributes of nodes the possibility of group influence and long-range interactions and discusses all their new features. The book provides a wide range of applications of network analysis in several fields – financial networks international migration global trade global food network arms transfers networks of terrorist groups and networks of international journals in economics. Real-world studies of networks indicate that the proposed centrality measures can identify important nodes in different applications. Starting from the basic ideas the development of the indices and their advantages compared to existing centrality measures are presented. Features Built around real-world case studies in a variety of different areas (finance migration trade etc. ) Suitable for students and professional researchers with an interest in complex network analysis Paired with a software package for readers who wish to apply the proposed models of centrality (in Python) available at https://github. com/SergSHV/slric. | New Centrality Measures in Networks How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes

GBP 48.99

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Deep and Shallow Machine Learning in Music and Audio

Providing an essential and unique bridge between the theories of signal processing machine learning and artificial intelligence (AI) in music this book provides a holistic overview of foundational ideas in music from the physical and mathematical properties of sound to symbolic representations. Combining signals and language models in one place this book explores how sound may be represented and manipulated by computer systems and how our devices may come to recognize particular sonic patterns as musically meaningful or creative through the lens of information theory. Introducing popular fundamental ideas in AI at a comfortable pace more complex discussions around implementations and implications in musical creativity are gradually incorporated as the book progresses. Each chapter is accompanied by guided programming activities designed to familiarize readers with practical implications of discussed theory without the frustrations of free-form coding. Surveying state-of-the art methods in applications of deep neural networks to audio and sound computing as well as offering a research perspective that suggests future challenges in music and AI research this book appeals to both students of AI and music as well as industry professionals in the fields of machine learning music and AI. | Deep and Shallow Machine Learning in Music and Audio

GBP 44.99

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Finite Automata

Interest in finite automata theory continues to grow not only because of its applications in computer science but also because of more recent applications in mathematics particularly group theory and symbolic dynamics. The subject itself lies on the boundaries of mathematics and computer science and with a balanced approach that does justice to both aspects this book provides a well-motivated introduction to the mathematical theory of finite automata. The first half of Finite Automata focuses on the computer science side of the theory and culminates in Kleene's Theorem which the author proves in a variety of ways to suit both computer scientists and mathematicians. In the second half the focus shifts to the mathematical side of the theory and constructing an algebraic approach to languages. Here the author proves two main results: Schützenberger's Theorem on star-free languages and the variety theorem of Eilenberg and Schützenberger. Accessible even to students with only a basic knowledge of discrete mathematics this treatment develops the underlying algebra gently but rigorously and nearly 200 exercises reinforce the concepts. Whether your students' interests lie in computer science or mathematics the well organized and flexible presentation of Finite Automata provides a route to understanding that you can tailor to their particular tastes and abilities.

GBP 59.99

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Ratio of Momentum Diffusivity to Thermal Diffusivity Introduction Meta-analysis and Scrutinization

This book presents a systematic introduction practical meaning and measurement of thermo-physical properties (i. e. viscosity density thermal conductivity specific heat capacity and thermal diffusivity) associated with the Prandtl number. The method of slope linear regression through the data points is presented in this textbook as a methodology for a deeper and insightful scrutinization. The book serves as a reference book for scientific investigators Teachers of Fluid Mechanics Experts on Heat and Mass Transfer Researchers on Boundary layer flows Mechanical and Chemical Engineers Physicists and Postgraduate Students working on transport phenomena who need theoretical and empirical reviews on the impact of increasing the ratio of momentum diffusivity to thermal diffusivity. Features: A systematic overview of the state-of-the-art in statistical methodology for understanding changes between dependent and independent variables. Pointers to some theoretical and empirical reviews on Prandtl number. Presents in-depth analysis of various self-similar flows emphasizing stretching induced flows nanofluid dynamics suction injection free convection mixed convection and forced convection. Insightful study on thermal radiation heat sour heat sink energy flux due to concentration gradient mass flux due to temperature gradient thermo-capillary convection flow Joule heating viscous dissipation thermal stratification thermophoresis and Brownian motion of particles. | Ratio of Momentum Diffusivity to Thermal Diffusivity Introduction Meta-analysis and Scrutinization

GBP 150.00

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Multi-State Survival Models for Interval-Censored Data

Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states over time. It is targeted at researchers in medical statistics epidemiology demography and social statistics. One of the applications in the book is a three-state process for dementia and survival in the older population. This process is described by an illness-death model with a dementia-free state a dementia state and a dead state. Statistical modelling of a multi-state process can investigate potential associations between the risk of moving to the next state and variables such as age gender or education. A model can also be used to predict the multi-state process. The methods are for longitudinal data subject to interval censoring. Depending on the definition of a state it is possible that the time of the transition into a state is not observed exactly. However when longitudinal data are available the transition time may be known to lie in the time interval defined by two successive observations. Such an interval-censored observation scheme can be taken into account in the statistical inference. Multi-state modelling is an elegant combination of statistical inference and the theory of stochastic processes. Multi-State Survival Models for Interval-Censored Data shows that the statistical modelling is versatile and allows for a wide range of applications.

GBP 48.99

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Meaningful Futures with Robots Designing a New Coexistence

Soon robots will leave the factories and make their way into living rooms supermarkets and care facilities. They will cooperate with humans in everyday life taking on more than just practical tasks. How should they communicate with us? Do they need eyes a screen or arms? Should they resemble humans? Or may they enrich social situations precisely because they act so differently from humans? Meaningful Futures with Robots: Designing a New Coexistence provides insight into the opportunities and risks that arise from living with robots in the future anchored in current research projects on everyday robotics. As well as generating ideas for robot developers and designers it also critically discusses existing theories and methods for social robotics from different perspectives - ethical design artistical and technological – and presents new approaches to meaningful human-robot interaction design. Key Features: Provides insights into current research on robots from different disciplinary angles with a particular focus on a value-driven design. Includes contributions from designers psychologists engineers philosophers artists and legal scholars among others. Licence line: Chapters 1 3 12 and 15 of this book are available for free in PDF format as Open Access from the individual product page at www. crcpress. com. They have been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4. 0 license. | Meaningful Futures with Robots Designing a New Coexistence

GBP 44.99

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Statistical Reinforcement Learning Modern Machine Learning Approaches

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence plant control and gaming the RL framework is ideal for decision making in unknown environments with large amounts of data. Supplying an up-to-date and accessible introduction to the field Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches including model-based and model-free approaches policy iteration and policy search methods. Covers the range of reinforcement learning algorithms from a modern perspectiveLays out the associated optimization problems for each reinforcement learning scenario coveredProvides thought-provoking statistical treatment of reinforcement learning algorithmsThe book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques. This book is an ideal resource for graduate-level students in computer science and applied statistics programs as well as researchers and engineers in related fields. | Statistical Reinforcement Learning Modern Machine Learning Approaches

GBP 44.99

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Model-Assisted Bayesian Designs for Dose Finding and Optimization Methods and Applications

Bayesian adaptive designs provide a critical approach to improve the efficiency and success of drug development that has been embraced by the US Food and Drug Administration (FDA). This is particularly important for early phase trials as they form the basis for the development and success of subsequent phase II and III trials. The objective of this book is to describe the state-of-the-art model-assisted designs to facilitate and accelerate the use of novel adaptive designs for early phase clinical trials. Model-assisted designs possess avant-garde features where superiority meets simplicity. Model-assisted designs enjoy exceptional performance comparable to more complicated model-based adaptive designs yet their decision rules often can be pre-tabulated and included in the protocol—making implementation as simple as conventional algorithm-based designs. An example is the Bayesian optimal interval (BOIN) design the first dose-finding design to receive the fit-for-purpose designation from the FDA. This designation underscores the regulatory agency's support of the use of the novel adaptive design to improve drug development. Features Represents the first book to provide comprehensive coverage of model-assisted designs for various types of dose-finding and optimization clinical trials Describes the up-to-date theory and practice for model-assisted designs Presents many practical challenges issues and solutions arising from early-phase clinical trials Illustrates with many real trial applications Offers numerous tips and guidance on designing dose finding and optimization trials Provides step-by-step illustrations of using software to design trials Develops a companion website (www. trialdesign. org) to provide freely available easy-to-use software to assist learning and implementing model-assisted designs Written by internationally recognized research leaders who pioneered model-assisted designs from the University of Texas MD Anderson Cancer Center this book shows how model-assisted designs can greatly improve the efficiency and simplify the design conduct and optimization of early-phase dose-finding trials. It should therefore be a very useful practical reference for biostatisticians clinicians working in clinical trials and drug regulatory professionals as well as graduate students of biostatistics. Novel model-assisted designs showcase the new KISS principle: Keep it simple and smart! | Model-Assisted Bayesian Designs for Dose Finding and Optimization Methods and Applications

GBP 84.99

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Merchant

Price (EUR)

Model-free Hedging A Martingale Optimal Transport Viewpoint

Direct Sum Decompositions of Torsion-Free Finite Rank Groups

Introduction To The Calculus of Variations And Its Applications

Computational Fluid Dynamics

Introduction to Statistical Modelling and Inference

Real World AI Ethics for Data Scientists Practical Case Studies

Applications of Regression for Categorical Outcomes Using R

Metabolomics Practical Guide to Design and Analysis

Hands-On Data Analysis in R for Finance

Measuring Society

Statistical Design and Analysis of Stability Studies

Monomial Algebras

Bioequivalence and Statistics in Clinical Pharmacology

Monte Carlo Simulation with Applications to Finance

The Essentials of Data Science: Knowledge Discovery Using R

Quantitative Finance with Python A Practical Guide to Investment Management Trading and Financial Engineering

Inferential Models Reasoning with Uncertainty

New Centrality Measures in Networks How to Take into Account the Parameters of the Nodes and Group Influence of Nodes to Nodes

Deep and Shallow Machine Learning in Music and Audio

Finite Automata

Ratio of Momentum Diffusivity to Thermal Diffusivity Introduction Meta-analysis and Scrutinization

Multi-State Survival Models for Interval-Censored Data

Meaningful Futures with Robots Designing a New Coexistence

Statistical Reinforcement Learning Modern Machine Learning Approaches

Model-Assisted Bayesian Designs for Dose Finding and Optimization Methods and Applications

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