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Handbook of Survival Analysis

Handbook of Survival Analysis

Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field the handbook focuses on advances in survival analysis techniques covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians

GBP 66.99
1

Real and Complex Analysis

Real and Complex Analysis

Presents Real & Complex Analysis Together Using a Unified ApproachA two-semester course in analysis at the advanced undergraduate or first-year graduate levelUnlike other undergraduate-level texts Real and Complex Analysis develops both the real and complex theory together. It takes a unified elegant approach to the theory that is consistent with the recommendations of the MAA’s 2004 Curriculum Guide. By presenting real and complex analysis together the authors illustrate the connections and differences between these two branches of analysis right from the beginning. This combined development also allows for a more streamlined approach to real and complex function theory. Enhanced by more than 1 000 exercises the text covers all the essential topics usually found in separate treatments of real analysis and complex analysis. Ancillary materials are available on the book’s website. This book offers a unique comprehensive presentation of both real and complex analysis. Consequently students will no longer have to use two separate textbooks—one for real function theory and one for complex function theory.

GBP 59.99
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Handbook of Cluster Analysis

Handbook of Cluster Analysis

Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active distinguished researchers in this area the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools. The book is organized according to the traditional core approaches to cluster analysis from the origins to recent developments. After an overview of approaches and a quick journey through the history of cluster analysis the book focuses on the four major approaches to cluster analysis. These approaches include methods for optimizing an objective function that describes how well data is grouped around centroids dissimilarity-based methods mixture models and partitioning models and clustering methods inspired by nonparametric density estimation. The book also describes additional approaches to cluster analysis including constrained and semi-supervised clustering and explores other relevant issues such as evaluating the quality of a cluster. This handbook is accessible to readers from various disciplines reflecting the interdisciplinary nature of cluster analysis. For those already experienced with cluster analysis the book offers a broad and structured overview. For newcomers to the field it presents an introduction to key issues. For researchers who are temporarily or marginally involved with cluster analysis problems the book gives enough algorithmic and practical details to facilitate working knowledge of specific clustering areas.

GBP 66.99
1

Computational Exome and Genome Analysis

Computational Exome and Genome Analysis

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.

GBP 46.99
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Applied Survey Data Analysis

Applied Survey Data Analysis

Highly recommended by the Journal of Official Statistics The American Statistician and other journals Applied Survey Data Analysis Second Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first edition this second edition expands the topics covered and presents more step-by-step examples of modern approaches to the analysis of survey data using the newest statistical software. Designed for readers working in a wide array of disciplines who use survey data in their work this book continues to provide a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. An example-driven guide to the applied statistical analysis and interpretation of survey data the second edition contains many new examples and practical exercises based on recent versions of real-world survey data sets. Although the authors continue to use Stata for most examples in the text they also continue to offer SAS SPSS SUDAAN R WesVar IVEware and Mplus software code for replicating the examples on the book’s updated website.

GBP 44.99
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Compositional Data Analysis in Practice

Compositional Data Analysis in Practice

Compositional data are quantitative descriptions of the parts of some whole conveying exclusively relative information. Examples are found in various fields including geology medicine chemistry agriculture economics social science etc. This concise book presents a very applied introduction to compositional data analysis focussing on the use of R for analysis. It includes lots of real examples code snippets and colour figures to illustrate the methods. | Compositional Data Analysis in Practice

GBP 46.99
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Accelerated Life Models Modeling and Statistical Analysis

Accelerated Life Models Modeling and Statistical Analysis

The authors of this monograph have developed a large and important class of survival analysis models that generalize most of the existing models. In a unified systematic presentation this monograph fully details those models and explores areas of accelerated life testing usually only touched upon in the literature. Accelerated Life Models: Modeling and Statistical Analysis presents models methods of data collection and statistical analysis for failure-time regression data in accelerated life testing and for degradation data with explanatory variables. In addition to the classical results the authors devote considerable attention to models with time-varying explanatory variables and to methods of semiparametric estimation. They also examine the simultaneous analysis of degradation and failure-time data when the intensities of failure in different modes depend on the level of degradation and the values of explanatory variables. The authors avoid technical details by explaining the ideas and referring to resources where thorough analysis can be found. Whether used for teaching research or general reference Accelerated Life Models: Modeling and Statistical Analysis provides new and known models and modern methods of accelerated life data analysis. | Accelerated Life Models Modeling and Statistical Analysis

GBP 59.99
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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
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Handbook of Meta-Analysis

Handbook of Meta-Analysis

Meta-analysis is the application of statistics to combine results from multiple studies and draw appropriate inferences. Its use and importance have exploded over the last 25 years as the need for a robust evidence base has become clear in many scientific areas including medicine and health social sciences education psychology ecology and economics. Recent years have seen an explosion of methods for handling complexities in meta-analysis including explained and unexplained heterogeneity between studies publication bias and sparse data. At the same time meta-analysis has been extended beyond simple two-group comparisons of continuous and binary outcomes to comparing and ranking the outcomes from multiple groups to complex observational studies to assessing heterogeneity of effects and to survival and multivariate outcomes. Many of these methods are statistically complex and are tailored to specific types of data. Key features Rigorous coverage of the full range of current statistical methodology used in meta-analysis Comprehensive coherent and unified overview of the statistical foundations behind meta-analysis Detailed description of the primary methods for both univariate and multivariate data Computer code to reproduce examples in chapters Thorough review of the literature with thousands of references Applications to specific types of biomedical and social science data Supplementary website with code data sample chapters and errata This book is for a broad audience of graduate students researchers and practitioners interested in the theory and application of statistical methods for meta-analysis. It is written at the level of graduate courses in statistics but will be of interest to and readable for quantitative scientists from a range of disciplines. The book can be used as a graduate level textbook as a general reference for methods or as an introduction to specialized topics using state-of-the art methods. | Handbook of Meta-Analysis

GBP 59.99
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A First Course in Functional Analysis

A First Course in Functional Analysis

Written as a textbook A First Course in Functional Analysis is an introduction to basic functional analysis and operator theory with an emphasis on Hilbert space methods. The aim of this book is to introduce the basic notions of functional analysis and operator theory without requiring the student to have taken a course in measure theory as a prerequisite. It is written and structured the way a course would be designed with an emphasis on clarity and logical development alongside real applications in analysis. The background required for a student taking this course is minimal; basic linear algebra calculus up to Riemann integration and some acquaintance with topological and metric spaces.

GBP 46.99
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Practical Multivariate Analysis

Practical Multivariate Analysis

This is the sixth edition of a popular textbook on multivariate analysis. Well-regarded for its practical and accessible approach with excellent examples and good guidance on computing the book is particularly popular for teaching outside statistics i. e. in epidemiology social science business etc. The sixth edition has been updated with a new chapter on data visualization a distinction made between exploratory and confirmatory analyses and a new section on generalized estimating equations and many new updates throughout. This new edition will enable the book to continue as one of the leading textbooks in the area particularly for non-statisticians. Key Features:Provides a comprehensive practical and accessible introduction to multivariate analysis. Keeps mathematical details to a minimum so particularly geared toward a non-statistical audience. Includes lots of detailed worked examples guidance on computing and exercises. Updated with a new chapter on data visualization. | Practical Multivariate Analysis

GBP 44.99
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Handbook of Infectious Disease Data Analysis

Handbook of Infectious Disease Data Analysis

Recent years have seen an explosion in new kinds of data on infectious diseases including data on social contacts whole genome sequences of pathogens biomarkers for susceptibility to infection serological panel data and surveillance data. The Handbook of Infectious Disease Data Analysis provides an overview of many key statistical methods that have been developed in response to such new data streams and the associated ability to address key scientific and epidemiological questions. A unique feature of the Handbook is the wide range of topics covered. Key featuresContributors include many leading researchers in the fieldDivided into four main sections: Basic concepts Analysis of Outbreak Data Analysis of Seroprevalence Data Analysis of Surveillance DataNumerous case studies and examples throughoutProvides both introductory material and key reference material

GBP 56.99
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Exploratory Multivariate Analysis by Example Using R

Exploratory Multivariate Analysis by Example Using R

Full of real-world case studies and practical advice Exploratory Multivariate Analysis by Example Using R Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical and hierarchical cluster analysis. The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework they present the principles indicators and ways of representing and visualising objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative how to handle more than two categorical variables in a CA context in which there are originally two variables and how to add quantitative variables in an MCA context in which variables are categorical. They also illustrate the methods using examples from various fields with related R code accessible in the FactoMineR package developed by the authors.

GBP 46.99
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Crime Mapping and Spatial Data Analysis using R

Crime Mapping and Spatial Data Analysis using R

Crime mapping and analysis sit at the intersection of geocomputation data visualisation and cartography spatial statistics environmental criminology and crime analysis. This book brings together relevant knowledge from these fields into a practical hands-on guide providing a useful introduction and reference material for topics in crime mapping the geography of crime environmental criminology and crime analysis. It can be used by students practitioners and academics alike whether to develop a university course to support further training and development or to hone skills in self-teaching R and crime mapping and spatial data analysis. It is not an advanced statistics textbook but rather an applied guide and later useful reference books intended to be read and for readers to practice the learnings from each chapter in sequence. In the first part of this volume we introduce key concepts for geographic analysis and representation and provide the reader with the foundations needed to visualise spatial crime data. We then introduce a series of tools to study spatial homogeneity and dependence. A key focus in this section is how to visualise and detect local clusters of crime and repeat victimisation. The final chapters introduce the use of basic spatial models which account for the distribution of crime across space. In terms of spatial data analysis the focus of the book is on spatial point pattern analysis and lattice or area data analysis.   | Crime Mapping and Spatial Data Analysis using R

GBP 74.99
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A Theoretical Introduction to Numerical Analysis

A Theoretical Introduction to Numerical Analysis

A Theoretical Introduction to Numerical Analysis presents the general methodology and principles of numerical analysis illustrating these concepts using numerical methods from real analysis linear algebra and differential equations. The book focuses on how to efficiently represent mathematical models for computer-based study. An accessible yet rigorous mathematical introduction this book provides a pedagogical account of the fundamentals of numerical analysis. The authors thoroughly explain basic concepts such as discretization error efficiency complexity numerical stability consistency and convergence. The text also addresses more complex topics like intrinsic error limits and the effect of smoothness on the accuracy of approximation in the context of Chebyshev interpolation Gaussian quadratures and spectral methods for differential equations. Another advanced subject discussed the method of difference potentials employs discrete analogues of Calderon’s potentials and boundary projection operators. The authors often delineate various techniques through exercises that require further theoretical study or computer implementation. By lucidly presenting the central mathematical concepts of numerical methods A Theoretical Introduction to Numerical Analysis provides a foundational link to more specialized computational work in fluid dynamics acoustics and electromagnetism.

GBP 59.99
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Statistical Techniques for Data Analysis

Statistical Techniques for Data Analysis

Since the first edition of this book appeared computers have come to the aid of modern experimenters and data analysts bringing with them data analysis techniques that were once beyond the calculational reach of even professional statisticians. Today scientists in every field have access to the techniques and technology they need to analyze statistical data. All they need is practical guidance on how to use them. Valuable to everyone who produces uses or evaluates scientific data Statistical Techniques for Data Analysis Second Edition provides straightforward discussion of basic statistical techniques and computer analysis. The purpose structure and general principles of the book remain the same as the first edition but the treatment now includes updates in every chapter additional topics and most importantly an introduction to use of the MINITAB Statistical Software. The presentation of each technique includes motivation and discussion of the statistical analysis a hand-calculated example the same example calculated using MINITAB and discussion of the MINITAB output and conclusions. Highlights of the Second Edition:Detailed discussion and use of MINITAB in examples complete with code and outputA new chapter addressing proportions time to event data and time series data in the metrology settingAdditional material on hypothesis testingDiscussion of critical valuesA look at mistakes commonly made in data analysis

GBP 44.99
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Wavelet Analysis Basic Concepts and Applications

Wavelet Analysis Basic Concepts and Applications

Wavelet Analysis: Basic Concepts and Applications provides a basic and self-contained introduction to the ideas underpinning wavelet theory and its diverse applications. This book is suitable for master’s or PhD students senior researchers or scientists working in industrial settings where wavelets are used to model real-world phenomena and data needs (such as finance medicine engineering transport images signals etc. ). Features: Offers a self-contained discussion of wavelet theory Suitable for a wide audience of post-graduate students researchers practitioners and theorists Provides researchers with detailed proofs Provides guides for readers to help them understand and practice wavelet analysis in different areas | Wavelet Analysis Basic Concepts and Applications

GBP 44.99
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The Analysis of Time Series An Introduction with R

The Analysis of Time Series An Introduction with R

This new edition of this classic title now in its seventh edition presents a balanced and comprehensive introduction to the theory implementation and practice of time series analysis. The book covers a wide range of topics including ARIMA models forecasting methods spectral analysis linear systems state-space models the Kalman filters nonlinear models volatility models and multivariate models. | The Analysis of Time Series An Introduction with R

GBP 69.99
1

Applied Meta-Analysis with R and Stata

Applied Meta-Analysis with R and Stata

Review of the First Edition: The authors strive to reduce theory to a minimum which makes it a self-learning text that is comprehensible for biologists physicians etc. who lack an advanced mathematics background. Unlike in many other textbooks R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses graphics and simulations directly relating to meta-analysis… A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs. —Journal of Applied Statistics Statistical Meta-Analysis with R and Stata Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions. What’s New in the Second Edition: Adds Stata programs along with the R programs for meta-analysis Updates all the statistical meta-analyses with R/Stata programs Covers fixed-effects and random-effects MA meta-regression MA with rare-event and MA-IPD vs MA-SS Adds five new chapters on multivariate MA publication bias missing data in MA MA in evaluating diagnostic accuracy and network MA Suitable as a graduate-level text for a meta-data analysis course the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health medical research governmental agencies and the pharmaceutical industry. | Applied Meta-Analysis with R and Stata

GBP 44.99
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Design and Analysis of Experiments Classical and Regression Approaches with SAS

Design and Analysis of Experiments Classical and Regression Approaches with SAS

Unlike other books on the modeling and analysis of experimental data Design and Analysis of Experiments: Classical and Regression Approaches with SAS not only covers classical experimental design theory it also explores regression approaches. Capitalizing on the availability of cutting-edge software the author uses both manual methods and SAS programs to carry out analyses. The book presents most of the different designs covered in a typical experimental design course. It discusses the requirements for good experimentation the completely randomized design the use of orthogonal contrast to test hypotheses and the model adequacy check. With an emphasis on two-factor factorial experiments the author analyzes repeated measures as well as fixed random and mixed effects models. He also describes designs with randomization restrictions before delving into the special cases of the 2k and 3k factorial designs including fractional replication and confounding. In addition the book covers response surfaces balanced incomplete block and hierarchical designs ANOVA ANCOVA and MANOVA. Fortifying the theory and computations with practical exercises and supplemental material this distinctive text provides a modern comprehensive treatment of experimental design and analysis. | Design and Analysis of Experiments Classical and Regression Approaches with SAS

GBP 59.99
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Handbook of Mixture Analysis

Handbook of Mixture Analysis

Mixture models have been around for over 150 years and they are found in many branches of statistical modelling as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate continuous or categorical cross-sectional time series networks and much more. Mixture analysis is a very active research topic in statistics and machine learning with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics including the EM algorithm Bayesian mixture models model-based clustering high-dimensional data hidden Markov models and applications in finance genomics and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data together with computational implementation to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field whether they are developing new methodology or applying the models to real scientific problems.

GBP 56.99
1

Bioinformatics A Practical Guide to Next Generation Sequencing Data Analysis

Bioinformatics A Practical Guide to Next Generation Sequencing Data Analysis

This book contains the latest material in the subject covering next generation sequencing (NGS) applications and meeting the requirements of a complete semester course. This book digs deep into analysis providing both concept and practice to satisfy the exact need of researchers seeking to understand and use NGS data reprocessing genome assembly variant discovery gene profiling epigenetics and metagenomics. The book does not introduce the analysis pipelines in a black box but with detailed analysis steps to provide readers with the scientific and technical backgrounds required to enable them to conduct analysis with confidence and understanding. The book is primarily designed as a companion for researchers and graduate students using sequencing data analysis but will also serve as a textbook for teachers and students in biology and bioscience. | Bioinformatics A Practical Guide to Next Generation Sequencing Data Analysis

GBP 74.99
1

Handbook of Neuroimaging Data Analysis

Handbook of Neuroimaging Data Analysis

This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development of novel statistical approaches to model brain data. Designed for researchers in statistics biostatistics computer science cognitive science computer engineering biomedical engineering applied mathematics physics and radiology the book can also be used as a textbook for graduate-level courses in statistics and biostatistics or as a self-study reference for Ph. D. students in statistics biostatistics psychology neuroscience and computer science. | Handbook of Neuroimaging Data Analysis

GBP 66.99
1

Exploratory Data Analysis Using R

Exploratory Data Analysis Using R

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of interesting – good bad and ugly – features that can be found in data and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data exploratory analysis and R as well as graphics in R. It then explores working with external data linear regression models and crafting data stories. The second part of the book focuses on developing R programs including good programming practices and examples working with text data and general predictive models. The book ends with a chapter on keeping it all together that includes managing the R installation managing files documenting and an introduction to reproducible computing. The book is designed for both advanced undergraduate entry-level graduate students and working professionals with little to no prior exposure to data analysis modeling statistics or programming. it keeps the treatment relatively non-mathematical even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters and an instructor's solution manual is available. About the Author:Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera a property insurance company in Fairfield California and he has previously held similar positions in a variety of application areas including software development drug safety data analysis and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering the Sciences and Medicine (Oxford University Press 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).

GBP 44.99
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Doing Meta-Analysis with R A Hands-On Guide

Doing Meta-Analysis with R A Hands-On Guide

Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered including calculation and pooling of outcome measures forest plots heterogeneity diagnostics subgroup analyses meta-regression methods to control for publication bias risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis multi-three-level meta-analyses Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package dmetar is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level making the book widely accessible. Features• Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data including exercises• Describes statistical concepts clearly and concisely before applying them in R• Includes step-by-step guidance through the coding required to perform meta-analyses and a companion R package for the book | Doing Meta-Analysis with R A Hands-On Guide

GBP 74.99
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