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Books > Science & Mathematics > Mathematics > Probability & statistics

Implementing Reproducible Research (Paperback): Victoria Stodden, Friedrich Leisch, Roger D. Peng Implementing Reproducible Research (Paperback)
Victoria Stodden, Friedrich Leisch, Roger D. Peng
R1,502 Discovery Miles 15 020 Ships in 12 - 17 working days

In computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must be available for widely distributing the data and code. In addition, both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden. Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science. It describes: Computational tools, such as Sweave, knitr, VisTrails, Sumatra, CDE, and the Declaratron system Open source practices, good programming practices, trends in open science, and the role of cloud computing in reproducible research Software and methodological platforms, including open source software packages, RunMyCode platform, and open access journals Each part presents contributions from leaders who have developed software and other products that have advanced the field. Supplementary material is available at www.ImplementingRR.org.

Methods in Medical Informatics - Fundamentals of Healthcare Programming in Perl, Python, and Ruby (Hardcover, New): Jules J.... Methods in Medical Informatics - Fundamentals of Healthcare Programming in Perl, Python, and Ruby (Hardcover, New)
Jules J. Berman
R5,368 Discovery Miles 53 680 Ships in 12 - 17 working days

Too often, healthcare workers are led to believe that medical informatics is a complex field that can only be mastered by teams of professional programmers. This is simply not the case. With just a few dozen simple algorithms, easily implemented with open source programming languages, you can fully utilize the medical information contained in clinical and research datasets. The common computational tasks of medical informatics are accessible to anyone willing to learn the basics. Methods in Medical Informatics: Fundamentals of Healthcare Programming in Perl, Python, and Ruby demonstrates that biomedical professionals with fundamental programming knowledge can master any kind of data collection. Providing you with access to data, nomenclatures, and programming scripts and languages that are all free and publicly available, this book - Describes the structure of data sources used, with instructions for downloading Includes a clearly written explanation of each algorithm Offers equivalent scripts in Perl, Python, and Ruby, for each algorithm Shows how to write short, quickly learned scripts, using a minimal selection of commands Teaches basic informatics methods for retrieving, organizing, merging, and analyzing data sources Provides case studies that detail the kinds of questions that biomedical scientists can ask and answer with public data and an open source programming language Requiring no more than a working knowledge of Perl, Python, or Ruby, Methods in Medical Informatics will have you writing powerful programs in just a few minutes. Within its chapters, you will find descriptions of the basic methods and implementations needed to complete many of the projects you will encounter in your biomedical career.

Statistical Methods (Paperback, 4th edition): Donna L. Mohr, William J. Wilson, Rudolf J. Freund Statistical Methods (Paperback, 4th edition)
Donna L. Mohr, William J. Wilson, Rudolf J. Freund
R1,919 Discovery Miles 19 190 Ships in 12 - 17 working days

Statistical Methods, Fourth Edition, is designed to introduce students to a wide-range of popular and practical statistical techniques. Requiring a minimum of advanced mathematics, it is suitable for undergraduates in statistics, or graduate students in the physical, life, and social sciences. By providing an overview of statistical reasoning, this text equips readers with the insight needed to summarize data, recognize good experimental designs, implement appropriate analyses, and arrive at sound interpretations of statistical results.

Handbook of Design and Analysis of Experiments (Paperback): Angela Dean, Max Morris, John Stufken, Derek B. Ingham Handbook of Design and Analysis of Experiments (Paperback)
Angela Dean, Max Morris, John Stufken, Derek B. Ingham
R2,264 Discovery Miles 22 640 Ships in 12 - 17 working days

Handbook of Design and Analysis of Experiments provides a detailed overview of the tools required for the optimal design of experiments and their analyses. The handbook gives a unified treatment of a wide range of topics, covering the latest developments. This carefully edited collection of 25 chapters in seven sections synthesizes the state of the art in the theory and applications of designed experiments and their analyses. Written by leading researchers in the field, the chapters offer a balanced blend of methodology and applications. The first section presents a historical look at experimental design and the fundamental theory of parameter estimation in linear models. The second section deals with settings such as response surfaces and block designs in which the response is modeled by a linear model, the third section covers designs with multiple factors (both treatment and blocking factors), and the fourth section presents optimal designs for generalized linear models, other nonlinear models, and spatial models. The fifth section addresses issues involved in designing various computer experiments. The sixth section explores "cross-cutting" issues relevant to all experimental designs, including robustness and algorithms. The final section illustrates the application of experimental design in recently developed areas. This comprehensive handbook equips new researchers with a broad understanding of the field's numerous techniques and applications. The book is also a valuable reference for more experienced research statisticians working in engineering and manufacturing, the basic sciences, and any discipline that depends on controlled experimental investigation.

Noninferiority Testing in Clinical Trials - Issues and Challenges (Paperback): Tie-Hua Ng Noninferiority Testing in Clinical Trials - Issues and Challenges (Paperback)
Tie-Hua Ng
R1,467 Discovery Miles 14 670 Ships in 12 - 17 working days

Take Your NI Trial to the Next Level Reflecting the vast research on noninferiority (NI) designs from the past 15 years, Noninferiority Testing in Clinical Trials: Issues and Challenges explains how to choose the NI margin as a small fraction of the therapeutic effect of the active control in a clinical trial. Requiring no prior knowledge of NI testing, the book is easily accessible to both statisticians and nonstatisticians involved in drug development. With over 20 years of experience in this area, the author introduces the basic elements of the NI trials one at a time in a logical order. He discusses issues with estimating the effect size based on historical placebo control trials of the active control. The book covers fundamental concepts related to NI trials, such as assay sensitivity, constancy assumption, discounting, and preservation. It also describes patient populations, three-arm trials, and the equivalence of three or more groups.

Age-Period-Cohort Models - Approaches and Analyses with Aggregate Data (Paperback): Robert O'Brien Age-Period-Cohort Models - Approaches and Analyses with Aggregate Data (Paperback)
Robert O'Brien
R1,468 Discovery Miles 14 680 Ships in 12 - 17 working days

Develop a Deep Understanding of the Statistical Issues of APC Analysis Age-Period-Cohort Models: Approaches and Analyses with Aggregate Data presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. These strategies include constrained estimation, the use of age and/or period and/or cohort characteristics, estimable functions, variance decomposition, and a new technique called the s-constraint approach. See How Common Methods Are Related to Each Other After a general and wide-ranging introductory chapter, the book explains the identification problem from algebraic and geometric perspectives and discusses constrained regression. It then covers important strategies that provide information that does not directly depend on the constraints used to identify the APC model. The final chapter presents a specific empirical example showing that a combination of the approaches can make a compelling case for particular APC effects. Get Answers to Questions about the Relationships of Ages, Periods, and Cohorts to Important Substantive Variables This book incorporates several APC approaches into one resource, emphasizing both their geometry and algebra. This integrated presentation helps researchers effectively judge the strengths and weaknesses of the methods, which should lead to better future research and better interpretation of existing research.

Statistical Detection and Surveillance of Geographic Clusters (Paperback): Peter Rogerson, Ikuho Yamada Statistical Detection and Surveillance of Geographic Clusters (Paperback)
Peter Rogerson, Ikuho Yamada
R1,487 Discovery Miles 14 870 Ships in 12 - 17 working days

The widespread popularity of geographic information systems (GIS) has led to new insights in countless areas of application. It has facilitated not only the collection and storage of geographic data, but also the display of such data. Building on this progress by using an integrated approach, Statistical Detection and Monitoring of Geographic Clusters provides the statistical tools to identify whether data on a given map deviates significantly from expectations and to determine quickly whether new point patterns are emerging over time. The book begins with a review of statistical methods for cluster detection, organized according to the different types of hypotheses and questions about clustering that can be investigated. It then delineates methods that allow for the quick detection of emergent geographic clusters. The book delivers a cohesive presentation unlike that of most edited volumes. Drawing on the authors' extensive work in the field, the book delineates methods in such a way that they can be applied, almost instantly, to an array of disciplines. The readily applicable methods the book describes are useful for a multitude of problems in a variety of fields, particularly disease surveillance in the public health industry. Statistical Detection and Monitoring of Geographic Clusters is an essential volume for your reference shelf.

Fuzzy Analytic Hierarchy Process (Paperback): Ali Emrouznejad, William Ho Fuzzy Analytic Hierarchy Process (Paperback)
Ali Emrouznejad, William Ho
R1,499 Discovery Miles 14 990 Ships in 12 - 17 working days

This book is the first in the literature to present the state of the art and some interesting and relevant applications of the Fuzzy Analytic Hierarchy Process (FAHP). The AHP is a conceptually and mathematically simple, easily implementable, yet extremely powerful tool for group decision making and is used around the world in a wide variety of decision situations, in fields such as government, business, industry, healthcare, and education. The aim of this book is to study various fuzzy methods for dealing with the imprecise and ambiguous data in AHP. Features: First book available on FAHP. Showcases state-of-the-art developments. Contains several novel real-life applications. Provides useful insights to both academics and practitioners in making group decisions under uncertainty This book provides the necessary background to work with existing fuzzy AHP models. Once the material in this book has been mastered, the reader will be able to apply fuzzy AHP models to his or her problems for making decisions with imprecise data.

Uncertainty Analysis of Experimental Data with R (Paperback): Benjamin David Shaw Uncertainty Analysis of Experimental Data with R (Paperback)
Benjamin David Shaw
R1,466 Discovery Miles 14 660 Ships in 12 - 17 working days

"This would be an excellent book for undergraduate, graduate and beyond....The style of writing is easy to read and the author does a good job of adding humor in places. The integration of basic programming in R with the data that is collected for any experiment provides a powerful platform for analysis of data.... having the understanding of data analysis that this book offers will really help researchers examine their data and consider its value from multiple perspectives - and this applies to people who have small AND large data sets alike! This book also helps people use a free and basic software system for processing and plotting simple to complex functions." Michelle Pantoya, Texas Tech University Measurements of quantities that vary in a continuous fashion, e.g., the pressure of a gas, cannot be measured exactly and there will always be some uncertainty with these measured values, so it is vital for researchers to be able to quantify this data. Uncertainty Analysis of Experimental Data with R covers methods for evaluation of uncertainties in experimental data, as well as predictions made using these data, with implementation in R. The books discusses both basic and more complex methods including linear regression, nonlinear regression, and kernel smoothing curve fits, as well as Taylor Series, Monte Carlo and Bayesian approaches. Features: 1. Extensive use of modern open source software (R). 2. Many code examples are provided. 3. The uncertainty analyses conform to accepted professional standards (ASME). 4. The book is self-contained and includes all necessary material including chapters on statistics and programming in R. Benjamin D. Shaw is a professor in the Mechanical and Aerospace Engineering Department at the University of California, Davis. His research interests are primarily in experimental and theoretical aspects of combustion. Along with other courses, he has taught undergraduate and graduate courses on engineering experimentation and uncertainty analysis. He has published widely in archival journals and became an ASME Fellow in 2003.

Understanding Statistics (Paperback): Bruce J. Chalmer Understanding Statistics (Paperback)
Bruce J. Chalmer
R1,502 Discovery Miles 15 020 Ships in 12 - 17 working days

Introducing undergraduates to the vital concepts of statistics, this superb textbook allows instructors to include as much mathematical detail as may be suitable for their students. Featuring Statpal statistical software for the IBM PC (R), the book contains study questions that help solidify students' understanding.

What Makes Variables Random - Probability for the Applied Researcher (Paperback): Peter J. Veazie What Makes Variables Random - Probability for the Applied Researcher (Paperback)
Peter J. Veazie
R1,459 Discovery Miles 14 590 Ships in 12 - 17 working days

What Makes Variables Random: Probability for the Applied Researcher provides an introduction to the foundations of probability that underlie the statistical analyses used in applied research. By explaining probability in terms of measure theory, it gives the applied researchers a conceptual framework to guide statistical modeling and analysis, and to better understand and interpret results. The book provides a conceptual understanding of probability and its structure. It is intended to augment existing calculus-based textbooks on probability and statistics and is specifically targeted to researchers and advanced undergraduate and graduate students in the applied research fields of the social sciences, psychology, and health and healthcare sciences. Materials are presented in three sections. The first section provides an overall introduction and presents some mathematical concepts used throughout the rest of the text. The second section presents the basic structure of measure theory and its special case of probability theory. The third section provides the connection between a conceptual understanding of measure-theoretic probability and applied research. This section starts with a chapter on its use in understanding basic models and finishes with a chapter that focuses on more complicated problems, particularly those related to various types and definitions of analyses related to hierarchical modeling.

Decision Sciences - Theory and Practice (Paperback): Raghu Nandan Sengupta, Aparna Gupta, Joydeep Dutta Decision Sciences - Theory and Practice (Paperback)
Raghu Nandan Sengupta, Aparna Gupta, Joydeep Dutta
R1,605 Discovery Miles 16 050 Ships in 12 - 17 working days

This handbook is an endeavour to cover many current, relevant, and essential topics related to decision sciences in a scientific manner. Using this handbook, graduate students, researchers, as well as practitioners from engineering, statistics, sociology, economics, etc. will find a new and refreshing paradigm shift as to how these topics can be put to use beneficially. Starting from the basics to advanced concepts, authors hope to make the readers well aware of the different theoretical and practical ideas, which are the focus of study in decision sciences nowadays. It includes an excellent bibliography/reference/journal list, information about a variety of datasets, illustrated pseudo-codes, and discussion of future trends in research. Covering topics ranging from optimization, networks and games, multi-objective optimization, inventory theory, statistical methods, artificial neural networks, times series analysis, simulation modeling, decision support system, data envelopment analysis, queueing theory, etc., this reference book is an attempt to make this area more meaningful for varied readers. Noteworthy features of this handbook are in-depth coverage of different topics, solved practical examples, unique datasets for a variety of examples in the areas of decision sciences, in-depth analysis of problems through colored charts, 3D diagrams, and discussions about software.

Probability and Statistical Models with Applications (Paperback): Ch. A. Charalambides, M. V. Koutras, N. Balakrishnan Probability and Statistical Models with Applications (Paperback)
Ch. A. Charalambides, M. V. Koutras, N. Balakrishnan
R1,533 Discovery Miles 15 330 Ships in 12 - 17 working days

This monograph of carefully collected articles reviews recent developments in theoretical and applied statistical science, highlights current noteworthy results and illustrates their applications; and points out possible new directions to pursue. With its enlightening account of statistical discoveries and its numerous figures and tables, Probability and Statistical Models with Applications is a must read for probabilists and theoretical and applied statisticians.

Cyclic and Computer Generated Designs (Paperback, 2nd edition): J.A. John, E. R. Williams Cyclic and Computer Generated Designs (Paperback, 2nd edition)
J.A. John, E. R. Williams
R1,476 Discovery Miles 14 760 Ships in 12 - 17 working days

Cyclic and Computer Generated Designs is a much-expanded and updated version of the well-received monograph, Cyclic Designs . The book is primarily concerned with the construction and analysis of designs with a number of different blocking structures, such as revolvable designs, row-column designs, and Latinized designs. It describes how appropriate and efficient designs can be constructed through the use of cyclic methods and recently developed computer algorithms. In this new edition, a greater emphasis is given to the construction and properties of resolvable block and row-column designs. A general theory for single, fractional and multiple replicate factorial designs is presented. Cyclic methods are used to construct most of these designs. Some new work on the use of computer algorithms for setting out factorial experiments in row-column designs is described. All the designs discussed can be analyzed using the generalized least squares theory given in the book. Two experiments, with analyses, are described in detail.

Goodness-of-Fit-Techniques (Paperback): Ralph B. D'Agostino Goodness-of-Fit-Techniques (Paperback)
Ralph B. D'Agostino
R1,582 Discovery Miles 15 820 Ships in 12 - 17 working days

Conveniently grouping methods by techniques, such as chi-squared and empirical distributionfunction, and also collecting methods of testing for specific famous distributions, this useful reference is the first comprehensive review of the extensive literature on the subject. It surveysthe leading methods of testing fit . .. provides tables to make the tests available . .. assessesthe comparative merits of different test procedures . .. and supplies numerical examples to aidin understanding these techniques.Goodness-of-Fit Techniques shows how to apply the techniques . .. emphasizes testing for thethree major distributions, normal, exponential, and uniform . .. discusses the handling of censoreddata .. . and contains over 650 bibliographic citations that cover the field.Illustrated with tables and drawings, this volume is an ideal reference for mathematical andapplied statisticians, and biostatisticians; professionals in applied science fields, including psychologists,biometricians , physicians, and quality control and reliability engineers; advancedundergraduate- and graduate-level courses on goodness-of-fit techniques; and professional seminarsand symposia on applied statistics, quality control, and reliability.

Quantitative Methods for HIV/AIDS Research (Paperback): Cliburn Chan, Michael G. Hudgens, Shein-Chung Chow Quantitative Methods for HIV/AIDS Research (Paperback)
Cliburn Chan, Michael G. Hudgens, Shein-Chung Chow
R1,421 Discovery Miles 14 210 Ships in 12 - 17 working days

Quantitative Methods in HIV/AIDS Research provides a comprehensive discussion of modern statistical approaches for the analysis of HIV/AIDS data. The first section focuses on statistical issues in clinical trials and epidemiology that are unique to or particularly challenging in HIV/AIDS research; the second section focuses on the analysis of laboratory data used for immune monitoring, biomarker discovery and vaccine development; the final section focuses on statistical issues in the mathematical modeling of HIV/AIDS pathogenesis, treatment and epidemiology. This book brings together a broad perspective of new quantitative methods in HIV/AIDS research, contributed by statisticians and mathematicians immersed in HIV research, many of whom are current or previous leaders of CFAR quantitative cores. It is the editors' hope that the work will inspire more statisticians, mathematicians and computer scientists to collaborate and contribute to the interdisciplinary challenges of understanding and addressing the AIDS pandemic.

Clinical Trial Biostatistics and Biopharmaceutical Applications (Paperback): Walter R. Young, Ding-Geng (Din) Chen Clinical Trial Biostatistics and Biopharmaceutical Applications (Paperback)
Walter R. Young, Ding-Geng (Din) Chen
R1,521 Discovery Miles 15 210 Ships in 12 - 17 working days

Since 1945, "The Annual Deming Conference on Applied Statistics" has been an important event in the statistics profession. In Clinical Trial Biostatistics and Biopharmaceutical Applications, prominent speakers from past Deming conferences present novel biostatistical methodologies in clinical trials as well as up-to-date biostatistical applications from the pharmaceutical industry. Divided into five sections, the book begins with emerging issues in clinical trial design and analysis, including the roles of modeling and simulation, the pros and cons of randomization procedures, the design of Phase II dose-ranging trials, thorough QT/QTc clinical trials, and assay sensitivity and the constancy assumption in noninferiority trials. The second section examines adaptive designs in drug development, discusses the consequences of group-sequential and adaptive designs, and illustrates group sequential design in R. The third section focuses on oncology clinical trials, covering competing risks, escalation with overdose control (EWOC) dose finding, and interval-censored time-to-event data. In the fourth section, the book describes multiple test problems with applications to adaptive designs, graphical approaches to multiple testing, the estimation of simultaneous confidence intervals for multiple comparisons, and weighted parametric multiple testing methods. The final section discusses the statistical analysis of biomarkers from omics technologies, biomarker strategies applicable to clinical development, and the statistical evaluation of surrogate endpoints. This book clarifies important issues when designing and analyzing clinical trials, including several misunderstood and unresolved challenges. It will help readers choose the right method for their biostatistical application. Each chapter is self-contained with references.

Data Science Foundations - Geometry and Topology of Complex Hierarchic Systems and Big Data Analytics (Paperback): Fionn Murtagh Data Science Foundations - Geometry and Topology of Complex Hierarchic Systems and Big Data Analytics (Paperback)
Fionn Murtagh
R1,468 Discovery Miles 14 680 Ships in 12 - 17 working days

"Data Science Foundations is most welcome and, indeed, a piece of literature that the field is very much in need of...quite different from most data analytics texts which largely ignore foundational concepts and simply present a cookbook of methods...a very useful text and I would certainly use it in my teaching." - Mark Girolami, Warwick University Data Science encompasses the traditional disciplines of mathematics, statistics, data analysis, machine learning, and pattern recognition. This book is designed to provide a new framework for Data Science, based on a solid foundation in mathematics and computational science. It is written in an accessible style, for readers who are engaged with the subject but not necessarily experts in all aspects. It includes a wide range of case studies from diverse fields, and seeks to inspire and motivate the reader with respect to data, associated information, and derived knowledge.

Applied Probability and Stochastic Processes (Paperback, 2nd edition): Frank Beichelt Applied Probability and Stochastic Processes (Paperback, 2nd edition)
Frank Beichelt
R1,506 Discovery Miles 15 060 Ships in 12 - 17 working days

Applied Probability and Stochastic Processes, Second Edition presents a self-contained introduction to elementary probability theory and stochastic processes with a special emphasis on their applications in science, engineering, finance, computer science, and operations research. It covers the theoretical foundations for modeling time-dependent random phenomena in these areas and illustrates applications through the analysis of numerous practical examples. The author draws on his 50 years of experience in the field to give your students a better understanding of probability theory and stochastic processes and enable them to use stochastic modeling in their work. New to the Second Edition Completely rewritten part on probability theory-now more than double in size New sections on time series analysis, random walks, branching processes, and spectral analysis of stationary stochastic processes Comprehensive numerical discussions of examples, which replace the more theoretically challenging sections Additional examples, exercises, and figures Presenting the material in a student-friendly, application-oriented manner, this non-measure theoretic text only assumes a mathematical maturity that applied science students acquire during their undergraduate studies in mathematics. Many exercises allow students to assess their understanding of the topics. In addition, the book occasionally describes connections between probabilistic concepts and corresponding statistical approaches to facilitate comprehension. Some important proofs and challenging examples and exercises are also included for more theoretically interested readers.

Computational Exome and Genome Analysis (Paperback): Peter N Robinson, Rosario Michael Piro, Marten Jager Computational Exome and Genome Analysis (Paperback)
Peter N Robinson, Rosario Michael Piro, Marten Jager
R1,550 Discovery Miles 15 500 Ships in 12 - 17 working days

Exome and genome sequencing are revolutionizing medical research and diagnostics, but the computational analysis of the data has become an extremely heterogeneous and often challenging area of bioinformatics. Computational Exome and Genome Analysis provides a practical introduction to all of the major areas in the field, enabling readers to develop a comprehensive understanding of the sequencing process and the entire computational analysis pipeline.

Multiple Comparisons Using R (Hardcover): Frank Bretz, Torsten Hothorn, Peter Westfall Multiple Comparisons Using R (Hardcover)
Frank Bretz, Torsten Hothorn, Peter Westfall
R2,855 Discovery Miles 28 550 Ships in 12 - 17 working days

Adopting a unifying theme based on maximum statistics, Multiple Comparisons Using R describes the common underlying theory of multiple comparison procedures through numerous examples. It also presents a detailed description of available software implementations in R. The R packages and source code for the analyses are available at http: //CRAN.R-project.org

After giving examples of multiplicity problems, the book covers general concepts and basic multiple comparisons procedures, including the Bonferroni method and Simes' test. It then shows how to perform parametric multiple comparisons in standard linear models and general parametric models. It also introduces the multcomp package in R, which offers a convenient interface to perform multiple comparisons in a general context. Following this theoretical framework, the book explores applications involving the Dunnett test, Tukey's all pairwise comparisons, and general multiple contrast tests for standard regression models, mixed-effects models, and parametric survival models. The last chapter reviews other multiple comparison procedures, such as resampling-based procedures, methods for group sequential or adaptive designs, and the combination of multiple comparison procedures with modeling techniques.

Controlling multiplicity in experiments ensures better decision making and safeguards against false claims. A self-contained introduction to multiple comparison procedures, this book offers strategies for constructing the procedures and illustrates the framework for multiple hypotheses testing in general parametric models. It is suitable for readers with R experience but limited knowledge of multiple comparison procedures and vice versa.

See Dr. Bretz discuss the book.

Time Series - Modeling, Computation, and Inference, Second Edition (Hardcover, 2nd edition): Raquel Prado, Marco A. R.... Time Series - Modeling, Computation, and Inference, Second Edition (Hardcover, 2nd edition)
Raquel Prado, Marco A. R. Ferreira, Mike West
R2,621 Discovery Miles 26 210 Ships in 9 - 15 working days

Focusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, Time Series: Modeling, Computation, and Inference, Second Edition integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling, analysis and forecasting, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and contacts research frontiers in multivariate time series modeling and forecasting. It presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. It explores the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian formulations and computation, including use of computations based on Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. It illustrates the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, environmental science, and finance. Along with core models and methods, the book represents state-of-the art approaches to analysis and forecasting in challenging time series problems. It also demonstrates the growth of time series analysis into new application areas in recent years, and contacts recent and relevant modeling developments and research challenges. New in the second edition: Expanded on aspects of core model theory and methodology. Multiple new examples and exercises. Detailed development of dynamic factor models. Updated discussion and connections with recent and current research frontiers.

Bayesian Adaptive Methods for Clinical Trials (Hardcover): Scott M. Berry, Bradley P. Carlin, J. Jack Lee, Peter Muller Bayesian Adaptive Methods for Clinical Trials (Hardcover)
Scott M. Berry, Bradley P. Carlin, J. Jack Lee, Peter Muller
R3,262 Discovery Miles 32 620 Ships in 12 - 17 working days

Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer's disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis. The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials. With a focus on Bayesian designs that achieve good power and Type I error, the next chapters present Bayesian tools useful in early (Phase I) and middle (Phase II) clinical trials as well as two recent Bayesian adaptive Phase II studies: the BATTLE and ISPY-2 trials. In the following chapter on late (Phase III) studies, the authors emphasize modern adaptive methods and seamless Phase II-III trials for maximizing information usage and minimizing trial duration. They also describe a case study of a recently approved medical device to treat atrial fibrillation. The concluding chapter covers key special topics, such as the proper use of historical data, equivalence studies, and subgroup analysis. For readers involved in clinical trials research, this book significantly updates and expands their statistical toolkits. The authors provide many detailed examples drawing on real data sets. The R and WinBUGS codes used throughout are available on supporting websites. Scott Berry talks about the book on the CRC Press YouTube Channel.

Operational Risk Modelling and Management (Hardcover): Claudio Franzetti Operational Risk Modelling and Management (Hardcover)
Claudio Franzetti
R5,966 Discovery Miles 59 660 Ships in 12 - 17 working days

Taking into account the standards of the Basel Accord, Operational Risk Modelling and Management presents a simulation model for generating the loss distribution of operational risk. It also examines a multitude of management issues that must be considered when adjusting the quantitative results of a comprehensive model. The book emphasizes techniques that can be understood and applied by practitioners. In the quantitative portions of the text, the author supplies key concepts and definitions without stating theorems or delving into mathematical proofs. He also offers references for readers looking for further background information. In addition, the book includes a Monte Carlo simulation of risk capital in the form of a run-through example of risk calculations based on data from a quantitative impact study. Since the computations are too complicated for a scripting language, a prototypical software program can be downloaded from www.garrulus.com Helping you navigate the tricky world of risk calculation and management, this book presents two main building blocks for determining how much capital needs to be reserved for operational risk. It employs the loss distribution approach as a model for calculating the risk capital figure and explains risk mitigation through management and management's actuations.

Parameter Redundancy and Identifiability (Hardcover): Diana Cole Parameter Redundancy and Identifiability (Hardcover)
Diana Cole
R4,748 Discovery Miles 47 480 Ships in 12 - 17 working days

Statistical and mathematical models are defined by parameters that describe different characteristics of those models. Ideally it would be possible to find parameter estimates for every parameter in that model, but, in some cases, this is not possible. For example, two parameters that only ever appear in the model as a product could not be estimated individually; only the product can be estimated. Such a model is said to be parameter redundant, or the parameters are described as non-identifiable. This book explains why parameter redundancy and non-identifiability is a problem and the different methods that can be used for detection, including in a Bayesian context. Key features of this book: Detailed discussion of the problems caused by parameter redundancy and non-identifiability Explanation of the different general methods for detecting parameter redundancy and non-identifiability, including symbolic algebra and numerical methods Chapter on Bayesian identifiability Throughout illustrative examples are used to clearly demonstrate each problem and method. Maple and R code are available for these examples More in-depth focus on the areas of discrete and continuous state-space models and ecological statistics, including methods that have been specifically developed for each of these areas This book is designed to make parameter redundancy and non-identifiability accessible and understandable to a wide audience from masters and PhD students to researchers, from mathematicians and statisticians to practitioners using mathematical or statistical models.

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