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Financial Mathematics A Comprehensive Treatment in Discrete Time

Financial Mathematics A Comprehensive Treatment in Discrete Time

The book has been tested and refined through years of classroom teaching experience. With an abundance of examples problems and fully worked out solutions the text introduces the financial theory and relevant mathematical methods in a mathematically rigorous yet engaging way. This textbook provides complete coverage of discrete-time financial models that form the cornerstones of financial derivative pricing theory. Unlike similar texts in the field this one presents multiple problem-solving approaches linking related comprehensive techniques for pricing different types of financial derivatives. Key features: In-depth coverage of discrete-time theory and methodology. Numerous fully worked out examples and exercises in every chapter. Mathematically rigorous and consistent yet bridging various basic and more advanced concepts. Judicious balance of financial theory mathematical and computational methods. Guide to Material. This revision contains: Almost 200 pages worth of new material in all chapters. A new chapter on elementary probability theory. An expanded the set of solved problems and additional exercises. Answers to all exercises. This book is a comprehensive self-contained and unified treatment of the main theory and application of mathematical methods behind modern-day financial mathematics. | Financial Mathematics A Comprehensive Treatment in Discrete Time

GBP 89.99
1

Financial Mathematics A Comprehensive Treatment in Continuous Time Volume II

Financial Mathematics A Comprehensive Treatment in Continuous Time Volume II

The book has been tested and refined through years of classroom teaching experience. With an abundance of examples problems and fully worked out solutions the text introduces the financial theory and relevant mathematical methods in a mathematically rigorous yet engaging way. This textbook provides complete coverage of continuous-time financial models that form the cornerstones of financial derivative pricing theory. Unlike similar texts in the field this one presents multiple problem-solving approaches linking related comprehensive techniques for pricing different types of financial derivatives. Key features: In-depth coverage of continuous-time theory and methodology Numerous fully worked out examples and exercises in every chapter Mathematically rigorous and consistent yet bridging various basic and more advanced concepts Judicious balance of financial theory and mathematical methods Guide to Material This revision contains: Almost 150 pages worth of new material in all chapters A appendix on probability theory An expanded set of solved problems and additional exercises Answers to all exercises This book is a comprehensive self-contained and unified treatment of the main theory and application of mathematical methods behind modern-day financial mathematics. The text complements Financial Mathematics: A Comprehensive Treatment in Discrete Time by the same authors also published by CRC Press. | Financial Mathematics A Comprehensive Treatment in Continuous Time Volume II

GBP 84.99
1

Dynamic Treatment Regimes Statistical Methods for Precision Medicine

Dynamic Treatment Regimes Statistical Methods for Precision Medicine

Dynamic Treatment Regimes: Statistical Methods for Precision Medicine provides a comprehensive introduction to statistical methodology for the evaluation and discovery of dynamic treatment regimes from data. Researchers and graduate students in statistics data science and related quantitative disciplines with a background in probability and statistical inference and popular statistical modeling techniques will be prepared for further study of this rapidly evolving field. A dynamic treatment regime is a set of sequential decision rules each corresponding to a key decision point in a disease or disorder process where each rule takes as input patient information and returns the treatment option he or she should receive. Thus a treatment regime formalizes how a clinician synthesizes patient information and selects treatments in practice. Treatment regimes are of obvious relevance to precision medicine which involves tailoring treatment selection to patient characteristics in an evidence-based way. Of critical importance to precision medicine is estimation of an optimal treatment regime one that if used to select treatments for the patient population would lead to the most beneficial outcome on average. Key methods for estimation of an optimal treatment regime from data are motivated and described in detail. A dedicated companion website presents full accounts of application of the methods using a comprehensive R package developed by the authors. The authors’ website www. dtr-book. com includes updates corrections new papers and links to useful websites. | Dynamic Treatment Regimes Statistical Methods for Precision Medicine

GBP 44.99
1

Pricing in General Insurance

Pricing in General Insurance

Based on the syllabus of the actuarial profession courses on general insurance pricing – with additional material inspired by the author’s own experience as a practitioner and lecturer – Pricing in General Insurance Second Edition presents pricing as a formalised process that starts with collecting information about a particular policyholder or risk and ends with a commercially informed rate. The first edition of the book proved very popular among students and practitioners with its pragmatic approach informal style and wide-ranging selection of topics including: Background and context for pricing Process of experience rating ranging from traditional approaches (burning cost analysis) to more modern approaches (stochastic modelling) Exposure rating for both property and casualty products Specialised techniques for personal lines (e. g. GLMs) reinsurance and specific products such as credit risk and weather derivatives General-purpose techniques such as credibility multi-line pricing and insurance optimisation The second edition is a substantial update on the first edition including: New chapter on pricing models: their structure development calibration and maintenance New chapter on rate change calculations and the pricing cycle Substantially enhanced treatment of exposure rating increased limit factors burning cost analysis Expanded treatment of triangle-free techniques for claim count development Improved treatment of premium building and capital allocation Expanded treatment of machine learning Enriched treatment of rating factor selection and the inclusion of generalised additive models The book delivers a practical introduction to all aspects of general insurance pricing and is aimed at students of general insurance and actuarial science as well as practitioners in the field. It is complemented by online material such as spreadsheets which implement the techniques described in the book solutions to problems a glossary and other appendices – increasing the practical value of the book.

GBP 74.99
1

Handbook of Statistical Methods and Analyses in Sports

Handbook of Statistical Methods and Analyses in Sports

This handbook will provide both overviews of statistical methods in sports and in-depth treatment of critical problems and challenges confronting statistical research in sports. The material in the handbook will be organized by major sport (baseball football hockey basketball and soccer) followed by a section on other sports and general statistical design and analysis issues that are common to all sports. This handbook has the potential to become the standard reference for obtaining the necessary background to conduct serious statistical analyses for sports applications and to appreciate scholarly work in this expanding area. | Handbook of Statistical Methods and Analyses in Sports

GBP 58.99
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An Advanced Course in Probability and Stochastic Processes

An Advanced Course in Probability and Stochastic Processes

An Advanced Course in Probability and Stochastic Processes provides a modern and rigorous treatment of probability theory and stochastic processes at an upper undergraduate and graduate level. Starting with the foundations of measure theory this book introduces the key concepts of probability theory in an accessible way providing full proofs and extensive examples and illustrations. Fundamental stochastic processes such as Gaussian processes Poisson random measures Lévy processes Markov processes and Itô processes are presented and explored in considerable depth showcasing their many interconnections. Special attention is paid to martingales and the Wiener process and their central role in the treatment of stochastic integrals and stochastic calculus. This book includes many exercises designed to test and challenge the reader and expand their skillset. An Advanced Course in Probability and Stochastic Processes is meant for students and researchers who have a solid mathematical background and who have had prior exposure to elementary probability and stochastic processes. Key Features: Focus on mathematical understanding Rigorous and self-contained Accessible and comprehensive High-quality illustrations Includes essential simulation algorithms Extensive list of exercises and worked-out examples Elegant and consistent notation

GBP 89.99
1

A First Course in Machine Learning

A First Course in Machine Learning

A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings and goes all the way to the frontiers of the subject such as infinite mixture models GPs and MCMC. —Devdatt Dubhashi Professor Department of Computer Science and Engineering Chalmers University Sweden This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade. —Daniel Barbara George Mason University Fairfax Virginia USA The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling inference and prediction providing ‘just in time’ the essential background on linear algebra calculus and probability theory that the reader needs to understand these concepts. —Daniel Ortiz-Arroyo Associate Professor Aalborg University Esbjerg Denmark I was impressed by how closely the material aligns with the needs of an introductory course on machine learning which is its greatest strength…Overall this is a pragmatic and helpful book which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months. —David Clifton University of Oxford UK The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process MCMC and mixture modeling provide an ideal basis for practical projects without disturbing the very clear and readable exposition of the basics contained in the first part of the book. —Gavin Cawley Senior Lecturer School of Computing Sciences University of East Anglia UK This book could be used for junior/senior undergraduate students or first-year graduate students as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective. —Guangzhi Qu Oakland University Rochester Michigan USA

GBP 39.99
1

Canonical Problems in Scattering and Potential Theory Part 1 Canonical Structures in Potential Theory

Canonical Problems in Scattering and Potential Theory Part 1 Canonical Structures in Potential Theory

Although the analysis of scattering for closed bodies of simple geometric shape is well developed structures with edges cavities or inclusions have seemed until now intractable to analytical methods. This two-volume set describes a breakthrough in analytical techniques for accurately determining diffraction from classes of canonical scatterers with comprising edges and other complex cavity features. It is an authoritative account of mathematical developments over the last two decades that provides benchmarks against which solutions obtained by numerical methods can be verified. The first volume Canonical Structures in Potential Theory develops the mathematics solving mixed boundary potential problems for structures with cavities and edges. The second volume Acoustic and Electromagnetic Diffraction by Canonical Structures examines the diffraction of acoustic and electromagnetic waves from several classes of open structures with edges or cavities. Together these volumes present an authoritative and unified treatment of potential theory and diffraction-the first complete description quantifying the scattering mechanisms in complex structures. | Canonical Problems in Scattering and Potential Theory Part 1 Canonical Structures in Potential Theory

GBP 56.99
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Hybrid Frequentist/Bayesian Power and Bayesian Power in Planning Clinical Trials

Hybrid Frequentist/Bayesian Power and Bayesian Power in Planning Clinical Trials

Hybrid Frequentist/Bayesian Power and Bayesian Power in Planning Clinical Trials provides a practical introduction to unconditional approaches to planning randomised clinical trials particularly aimed at drug development in the pharmaceutical industry. This book is aimed at providing guidance to practitioners in using average power assurance and related concepts. This book brings together recent research and sets them in a consistent framework and provides a fresh insight into how such methods can be used. Features: A focus on normal theory linking average power expected power predictive power assurance conditional Bayesian power and Bayesian power. Extensions of the concepts to binomial and time-to-event outcomes and non-inferiority trials An investigation into the upper bound on average power assurance and Bayesian power based on the prior probability of a positive treatment effect Application of assurance to a series of trials in a development program and an introduction of the assurance of an individual trial conditional on the positive outcome of an earlier trial in the program or to the successful outcome of an interim analysis Prior distribution of power and sample size Extension of the basic approach to proof-of-concept trials with dual success criteria Investigation of the connection between conditional and predictive power at an interim analysis and power and assurance Introduction of the idea of surety in sample sizing of clinical trials based on the width of the confidence intervals for the treatment effect and an unconditional version.

GBP 99.99
1

Data Driven Science for Clinically Actionable Knowledge in Diseases

Data Driven Science for Clinically Actionable Knowledge in Diseases

Data-driven science has become a major decision-making aid for the diagnosis and treatment of disease. Computational and visual analytics enables effective exploration and sense making of large and complex data through the deployment of appropriate data science methods meaningful visualisation and human-information interaction. This edited volume covers state-of-the-art theory method models design evaluation and applications in computational and visual analytics in desktop mobile and immersive environments for analysing biomedical and health data. The book is focused on data-driven integral analysis including computational methods and visual analytics practices and solutions for discovering actionable knowledge in support of clinical actions in real environments. By studying how data and visual analytics have been implemented into the healthcare domain the book demonstrates how analytics influences the domain through improving decision making specifying diagnostics selecting the best treatments and generating clinical certainty. | Data Driven Science for Clinically Actionable Knowledge in Diseases

GBP 44.99
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Biomarker Analysis in Clinical Trials with R

Biomarker Analysis in Clinical Trials with R

The world is awash in data. This volume of data will continue to increase. In the pharmaceutical industry much of this data explosion has happened around biomarker data. Great statisticians are needed to derive understanding from these data. This book will guide you as you begin the journey into communicating understanding and synthesizing biomarker data. From the Foreword Jared Christensen Vice President Biostatistics Early Clinical Development Pfizer Inc. Biomarker Analysis in Clinical Trials with R offers practical guidance to statisticians in the pharmaceutical industry on how to incorporate biomarker data analysis in clinical trial studies. The book discusses the appropriate statistical methods for evaluating pharmacodynamic predictive and surrogate biomarkers for delivering increased value in the drug development process. The topic of combining multiple biomarkers to predict drug response using machine learning is covered. Featuring copious reproducible code and examples in R the book helps students researchers and biostatisticians get started in tackling the hard problems of designing and analyzing trials with biomarkers. Features:Analysis of pharmacodynamic biomarkers for lending evidence target modulation. Design and analysis of trials with a predictive biomarker. Framework for analyzing surrogate biomarkers. Methods for combining multiple biomarkers to predict treatment response. Offers a biomarker statistical analysis plan. R code data and models are given for each part: including regression models for survival and longitudinal data as well as statistical learning models such as graphical models and penalized regression models.

GBP 39.99
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Statistical Topics in Health Economics and Outcomes Research

Statistical Topics in Health Economics and Outcomes Research

With ever-rising healthcare costs evidence generation through Health Economics and Outcomes Research (HEOR) plays an increasingly important role in decision-making about the allocation of resources. Accordingly it is now customary for health technology assessment and reimbursement agencies to request for HEOR evidence in addition to data from clinical trials to inform decisions about patient access to new treatment options. While there is a great deal of literature on HEOR there is a need for a volume that presents a coherent and unified review of the major issues that arise in application especially from a statistical perspective. Statistical Topics in Health Economics and Outcomes Research fulfils that need by presenting an overview of the key analytical issues and best practice. Special attention is paid to key assumptions and other salient features of statistical methods customarily used in the area and appropriate and relatively comprehensive references are made to emerging trends. The content of the book is purposefully designed to be accessible to readers with basic quantitative backgrounds while providing an in-depth coverage of relatively complex statistical issues. The book will make a very useful reference for researchers in the pharmaceutical industry academia and research institutions involved with HEOR studies. The targeted readers may include statisticians data scientists epidemiologists outcomes researchers health economists and healthcare policy and decision-makers.

GBP 44.99
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Bayesian Networks With Examples in R

Bayesian Networks With Examples in R

Bayesian Networks: With Examples in R Second Edition introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each step of the modelling process and discuss side by side the underlying theory and its application using R code. The examples start from the simplest notions and gradually increase in complexity. In particular this new edition contains significant new material on topics from modern machine-learning practice: dynamic networks networks with heterogeneous variables and model validation. The first three chapters explain the whole process of Bayesian network modelling from structure learning to parameter learning to inference. These chapters cover discrete Gaussian and conditional Gaussian Bayesian networks. The following two chapters delve into dynamic networks (to model temporal data) and into networks including arbitrary random variables (using Stan). The book then gives a concise but rigorous treatment of the fundamentals of Bayesian networks and offers an introduction to causal Bayesian networks. It also presents an overview of R packages and other software implementing Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein-signalling network published in Science and a probabilistic graphical model for predicting the composition of different body parts. Covering theoretical and practical aspects of Bayesian networks this book provides you with an introductory overview of the field. It gives you a clear practical understanding of the key points behind this modelling approach and at the same time it makes you familiar with the most relevant packages used to implement real-world analyses in R. The examples covered in the book span several application fields data-driven models and expert systems probabilistic and causal perspectives thus giving you a starting point to work in a variety of scenarios. Online supplementary materials include the data sets and the code used in the book which will all be made available from https://www. bnlearn. com/book-crc-2ed/ | Bayesian Networks With Examples in R

GBP 82.99
1

A First Course in Ordinary Differential Equations

A First Course in Ordinary Differential Equations

A First course in Ordinary Differential Equations provides a detailed introduction to the subject focusing on analytical methods to solve ODEs and theoretical aspects of analyzing them when it is difficult/not possible to find their solutions explicitly. This two-fold treatment of the subject is quite handy not only for undergraduate students in mathematics but also for physicists engineers who are interested in understanding how various methods to solve ODEs work. More than 300 end-of-chapter problems with varying difficulty are provided so that the reader can self examine their understanding of the topics covered in the text. Most of the definitions and results used from subjects like real analysis linear algebra are stated clearly in the book. This enables the book to be accessible to physics and engineering students also. Moreover sufficient number of worked out examples are presented to illustrate every new technique introduced in this book. Moreover the author elucidates the importance of various hypotheses in the results by providing counter examples. Features Offers comprehensive coverage of all essential topics required for an introductory course in ODE. Emphasizes on both computation of solutions to ODEs as well as the theoretical concepts like well-posedness comparison results stability etc. Systematic presentation of insights of the nature of the solutions to linear/non-linear ODEs. Special attention on the study of asymptotic behavior of solutions to autonomous ODEs (both for scalar case and 2✕2 systems). Sufficient number of examples are provided wherever a notion is introduced. Contains a rich collection of problems. This book serves as a text book for undergraduate students and a reference book for scientists and engineers. Broad coverage and clear presentation of the material indeed appeals to the readers. Dr. Suman K. Tumuluri has been working in University of Hyderabad India for 11 years and at present he is an associate professor. His research interests include applications of partial differential equations in population dynamics and fluid dynamics.

GBP 82.99
1

Linear Regression Models Applications in R

Linear Regression Models Applications in R

Research in social and behavioral sciences has benefited from linear regression models (LRMs) for decades to identify and understand the associations among a set of explanatory variables and an outcome variable. Linear Regression Models: Applications in R provides you with a comprehensive treatment of these models and indispensable guidance about how to estimate them using the R software environment. After furnishing some background material the author explains how to estimate simple and multiple LRMs in R including how to interpret their coefficients and understand their assumptions. Several chapters thoroughly describe these assumptions and explain how to determine whether they are satisfied and how to modify the regression model if they are not. The book also includes chapters on specifying the correct model adjusting for measurement error understanding the effects of influential observations and using the model with multilevel data. The concluding chapter presents an alternative model—logistic regression—designed for binary or two-category outcome variables. The book includes appendices that discuss data management and missing data and provides simulations in R to test model assumptions. Features Furnishes a thorough introduction and detailed information about the linear regression model including how to understand and interpret its results test assumptions and adapt the model when assumptions are not satisfied. Uses numerous graphs in R to illustrate the model’s results assumptions and other features. Does not assume a background in calculus or linear algebra rather an introductory statistics course and familiarity with elementary algebra are sufficient. Provides many examples using real-world datasets relevant to various academic disciplines. Fully integrates the R software environment in its numerous examples. The book is aimed primarily at advanced undergraduate and graduate students in social behavioral health sciences and related disciplines taking a first course in linear regression. It could also be used for self-study and would make an excellent reference for any researcher in these fields. The R code and detailed examples provided throughout the book equip the reader with an excellent set of tools for conducting research on numerous social and behavioral phenomena. John P. Hoffmann is a professor of sociology at Brigham Young University where he teaches research methods and applied statistics courses and conducts research on substance use and criminal behavior. | Linear Regression Models Applications in R

GBP 66.99
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Case Studies in Innovative Clinical Trials

Case Studies in Innovative Clinical Trials

Drug development is a strictly regulated area. As such marketing approval of a new drug depends heavily if not exclusively on evidence generated from clinical trials. Drug development has seen tremendous innovation in science and technology that has revolutionized the treatment of some diseases. And yet the statistical design and practical conduct of the clinical trials used to test new therapeutics for safety and efficacy have changed very little over the decades. Our approach to clinical trials is steeped in convention and tradition. The large fixed randomized controlled trial methods that have been the gold standard are well understood and expected by many trial stakeholders. However this approach is not well suited to all aspects of modern drug development and the current competitive landscape. We now see new therapies that target a small fraction of the patient population rare diseases with high unmet medical needs and pediatric populations that must wait for years for new drug approvals from the time that therapies are approved in adults. Large randomized clinical trials are at best inefficient and at worst completely infeasible in many modern clinical settings. Advances in technology and data infrastructure call for innovations in clinical trial design. Despite advances in statistical methods the availability of information and computing power the actual experience with innovative design in clinical trials across industry and academia is limited. This book will be an important showcase of the potential for these innovative designs in modern drug development and will be an important resource to guide those who wish to undertake them for themselves. This book is ideal for professionals in the pharmaceutical industry and regulatory agencies but it will also be useful to academic researchers faculty members and graduate students in statistics biostatistics public health and epidemiology due to its focus on innovation. Key Features: Is written by pharmaceutical industry experts academic researchers and regulatory reviewers; this is the first book providing a comprehensive set of case studies related to statistical methodology implementation regulatory considerations and communication of complex innovative trial design Has a broad appeal to a multitude of readers across academia industry and regulatory agencies Each contribution is a practical case study that can speak to the benefits of an innovative approach but also balance that with the real-life challenges encountered A complete understanding of what is actually being done in modern clinical trials will broaden the reader’s capabilities and provide examples to first mimic and then customize and expand upon when exploring these ideas on their own | Case Studies in Innovative Clinical Trials

GBP 130.00
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Introduction to Mathematical Oncology

Introduction to Mathematical Oncology

Introduction to Mathematical Oncology presents biologically well-motivated and mathematically tractable models that facilitate both a deep understanding of cancer biology and better cancer treatment designs. It covers the medical and biological background of the diseases modeling issues and existing methods and their limitations. The authors introduce mathematical and programming tools along with analytical and numerical studies of the models. They also develop new mathematical tools and look to future improvements on dynamical models. After introducing the general theory of medicine and exploring how mathematics can be essential in its understanding the text describes well-known practical and insightful mathematical models of avascular tumor growth and mathematically tractable treatment models based on ordinary differential equations. It continues the topic of avascular tumor growth in the context of partial differential equation models by incorporating the spatial structure and physiological structure such as cell size. The book then focuses on the recent active multi-scale modeling efforts on prostate cancer growth and treatment dynamics. It also examines more mechanistically formulated models including cell quota-based population growth models with applications to real tumors and validation using clinical data. The remainder of the text presents abundant additional historical biological and medical background materials for advanced and specific treatment modeling efforts. Extensively classroom-tested in undergraduate and graduate courses this self-contained book allows instructors to emphasize specific topics relevant to clinical cancer biology and treatment. It can be used in a variety of ways including a single-semester undergraduate course a more ambitious graduate course or a full-year sequence on mathematical oncology.

GBP 44.99
1

Estimands Estimators and Sensitivity Analysis in Clinical Trials

Estimands Estimators and Sensitivity Analysis in Clinical Trials

The concepts of estimands analyses (estimators) and sensitivity are interrelated. Therefore great need exists for an integrated approach to these topics. This book acts as a practical guide to developing and implementing statistical analysis plans by explaining fundamental concepts using accessible language providing technical details real-world examples and SAS and R code to implement analyses. The updated ICH guideline raises new analytic and cross-functional challenges for statisticians. Gaps between different communities have come to surface such as between causal inference and clinical trialists as well as among clinicians statisticians and regulators when it comes to communicating decision-making objectives assumptions and interpretations of evidence. This book lays out a path toward bridging some of these gaps. It offers¿ A common language and unifying framework along with the technical details and practical guidance to help statisticians meet the challenges¿ A thorough treatment of intercurrent events (ICEs) i. e. postrandomization events that confound interpretation of outcomes and five strategies for ICEs in ICH E9 (R1)¿ Details on how estimands integrated into a principled study development process lay a foundation for coherent specification of trial design conduct and analysis needed to overcome the issues caused by ICEs:¿ A perspective on the role of the intention-to-treat principle¿ Examples and case studies from various areas¿ Example code in SAS and R¿ A connection with causal inference¿ Implications and methods for analysis of longitudinal trials with missing dataTogether the authors have offered the readers their ample expertise in clinical trial design and analysis from an industrial and academic perspective. | Estimands Estimators and Sensitivity Analysis in Clinical Trials

GBP 39.99
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Exposure-Response Modeling Methods and Practical Implementation

Exposure-Response Modeling Methods and Practical Implementation

Discover the Latest Statistical Approaches for Modeling Exposure-Response RelationshipsWritten by an applied statistician with extensive practical experience in drug development Exposure-Response Modeling: Methods and Practical Implementation explores a wide range of topics in exposure-response modeling from traditional pharmacokinetic-pharmacodynamic (PKPD) modeling to other areas in drug development and beyond. It incorporates numerous examples and software programs for implementing novel methods. The book describes using measurement error models to treat sequential modeling fitting models with exposure and response driven by complex dynamics and survival analysis with dynamic exposure history. It also covers Bayesian analysis and model-based Bayesian decision analysis causal inference to eliminate confounding biases and exposure-response modeling with response-dependent dose/treatment adjustments (dynamic treatment regimes) for personalized medicine and treatment adaptation. Many examples illustrate the use of exposure-response modeling in experimental toxicology clinical pharmacology epidemiology and drug safety. Some examples demonstrate how to solve practical problems while others help with understanding concepts and evaluating the performance of new methods. The provided SAS and R codes enable readers to test the approaches in their own scenarios. Although application oriented this book also gives a systematic treatment of concepts and methodology. Applied statisticians and modelers can find details on how to implement new approaches. Researchers can find topics for or applications of their work. In addition students can see how complicated methodology and models are applied to practical situations. | Exposure-Response Modeling Methods and Practical Implementation

GBP 44.99
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Advances in Distance Learning in Times of Pandemic

Advances in Distance Learning in Times of Pandemic

The book Advances in Distance Learning in Times of Pandemic is devoted to the issues and challenges faced by universities in the field of distance learning in COVID-19 times. It covers both the theoretical and practical aspects connected to distance education. It elaborates on issues regarding distance learning its challenges assessment by students and their expectations the use of tools to improve distance learning and the functioning of e-learning in the industry 4. 0 and society 5. 0 eras. The book also devotes a lot of space to the issues of Web 3. 0 in university e-learning quality assurance and knowledge management. The aim and scope of this book is to draw a holistic picture of ongoing online teaching-activities before and during the lockdown period and present the meaning and future of e-learning from students’ points of view taking into consideration their attitudes and expectations as well as industry 4. 0 and society 5. 0 aspects. The book presents the approach to distance learning and how it has changed especially during a pandemic that revolutionized education. It highlights • the function of online education and how that has changed before and during the pandemic. • how e-learning is beneficial in promoting digital citizenship. • distance learning characteristic in the era of industry 4. 0 and society 5. 0. • how the era of industry 4. 0 treats distance learning as a desirable form of education. The book covers both scientific and educational aspects and can be useful for university-level undergraduate postgraduate and research-grade courses and can be referred to by anyone interested in exploring the diverse aspects of distance learning.

GBP 110.00
1

Robust Statistical Methods with R Second Edition

Robust Statistical Methods with R Second Edition

The second edition of Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on new developments and on the computational aspects. There are many numerical examples and notes on the R environment and the updated chapter on the multivariate model contains additional material on visualization of multivariate data in R. A new chapter on robust procedures in measurement error models concentrates mainly on the rank procedures less sensitive to errors than other procedures. This book will be an invaluable resource for researchers and postgraduate students in statistics and mathematics. Features• Provides a systematic practical treatment of robust statistical methods• Offers a rigorous treatment of the whole range of robust methods including the sequential versions of estimators their moment convergence and compares their asymptotic and finite-sample behavior• The extended account of multivariate models includes the admissibility shrinkage effects and unbiasedness of two-sample tests• Illustrates the small sensitivity of the rank procedures in the measurement error model• Emphasizes the computational aspects supplies many examples and illustrations and provides the own procedures of the authors in the R software on the book’s website | Robust Statistical Methods with R Second Edition

GBP 39.99
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Bayesian Designs for Phase I-II Clinical Trials

Bayesian Designs for Phase I-II Clinical Trials

Reliably optimizing a new treatment in humans is a critical first step in clinical evaluation since choosing a suboptimal dose or schedule may lead to failure in later trials. At the same time if promising preclinical results do not translate into a real treatment advance it is important to determine this quickly and terminate the clinical evaluation process to avoid wasting resources. Bayesian Designs for Phase I–II Clinical Trials describes how phase I–II designs can serve as a bridge or protective barrier between preclinical studies and large confirmatory clinical trials. It illustrates many of the severe drawbacks with conventional methods used for early-phase clinical trials and presents numerous Bayesian designs for human clinical trials of new experimental treatment regimes. Written by research leaders from the University of Texas MD Anderson Cancer Center this book shows how Bayesian designs for early-phase clinical trials can explore refine and optimize new experimental treatments. It emphasizes the importance of basing decisions on both efficacy and toxicity. | Bayesian Designs for Phase I-II Clinical Trials

GBP 38.99
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Correspondence Analysis in Practice

Correspondence Analysis in Practice

Drawing on the author’s 45 years of experience in multivariate analysis Correspondence Analysis in Practice Third Edition shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. CA and its variants subset CA multiple CA and joint CA translate two-way and multi-way tables into more readable graphical forms — ideal for applications in the social environmental and health sciences as well as marketing economics linguistics archaeology and more. Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra Barcelona Spain where he teaches a course amongst others on Data Visualization. He has authored and co-edited nine books and 80 journal articles and book chapters mostly on correspondence analysis the latest being Visualization and Verbalization of Data in 2015. He has given short courses in fifteen countries to environmental scientists sociologists data scientists and marketing professionals and has specialized in statistics in ecology and social science.

GBP 48.99
1

Statistical Models in Toxicology

Statistical Models in Toxicology

Statistical Models in Toxicology presents an up-to-date and comprehensive account of statistical theory topics that occur in toxicology. The attention given by statisticians to the problem of health risk estimation for environmental and occupational exposures in the last few decades has created excitement and optimism among both statisticians and toxicologists. The development of modern statistical techniques with solid mathematical foundations in the twentieth century and the advent of modern computers in the latter part of the century gave way to the development of many statistical models and methods to describe toxicological processes and attempts to solve the associated problems. Not only have the models enjoyed a high level of elegance and sophistication mathematically but they are widely used by industry and government regulatory agencies. Features:Focuses on describing the statistical models in environmental toxicology that facilitate the assessment of risk mainly in humans. The properties and shortfalls of each model are discussed and its impact in the process of risk assessment is examined. Discusses models that assess the risk of mixtures of chemicals. Presents statistical models that are developed for risk estimation in different aspects of environmental toxicology including cancer and carcinogenic substances. Includes models for developmental and reproductive toxicity risk assessment risk assessment in continuous outcomes and developmental neurotoxicity. Contains numerous examples and exercises. Statistical Models in Toxicology introduces a wide variety of statistical models that are currently utilized for dose-response modeling and risk analysis. These models are often developed based on design and regulatory guidelines of toxicological experiments. The book is suitable for practitioners or it can be used as a textbook for advanced undergraduate or graduate students of mathematics and statistics.

GBP 44.99
1