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Sequential Analysis Hypothesis Testing and Changepoint Detection

Sequential Analysis Hypothesis Testing and Changepoint Detection

Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently. The book reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. The authors not only emphasize traditional binary hypotheses but also substantially more difficult multiple decision problems. They address scenarios with simple hypotheses and more realistic cases of two and finitely many composite hypotheses. The book primarily focuses on practical discrete-time models with certain continuous-time models also examined when general results can be obtained very similarly in both cases. It treats both conventional i. i. d. and general non-i. i. d. stochastic models in detail including Markov hidden Markov state-space regression and autoregression models. Rigorous proofs are given for the most important results. Written by leading authorities in the field this book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains. It explains how the theoretical aspects influence the hypothesis testing and changepoint detection problems as well as the design of algorithms. | Sequential Analysis Hypothesis Testing and Changepoint Detection

GBP 44.99
1

Statistical Testing Strategies in the Health Sciences

Statistical Testing Strategies in the Health Sciences

Statistical Testing Strategies in the Health Sciences provides a compendium of statistical approaches for decision making ranging from graphical methods and classical procedures through computationally intensive bootstrap strategies to advanced empirical likelihood techniques. It bridges the gap between theoretical statistical methods and practical procedures applied to the planning and analysis of health-related experiments. The book is organized primarily based on the type of questions to be answered by inference procedures or according to the general type of mathematical derivation. It establishes the theoretical framework for each method with a substantial amount of chapter notes included for additional reference. It then focuses on the practical application for each concept providing real-world examples that can be easily implemented using corresponding statistical software code in R and SAS. The book also explains the basic elements and methods for constructing correct and powerful statistical decision-making processes to be adapted for complex statistical applications. With techniques spanning robust statistical methods to more computationally intensive approaches this book shows how to apply correct and efficient testing mechanisms to various problems encountered in medical and epidemiological studies including clinical trials. Theoretical statisticians medical researchers and other practitioners in epidemiology and clinical research will appreciate the book’s novel theoretical and applied results. The book is also suitable for graduate students in biostatistics epidemiology health-related sciences and areas pertaining to formal decision-making mechanisms.

GBP 44.99
1

The Weibull Distribution A Handbook

The Weibull Distribution A Handbook

The Most Comprehensive Book on the SubjectChronicles the Development of the Weibull Distribution in Statistical Theory and Applied StatisticsExploring one of the most important distributions in statistics The Weibull Distribution: A Handbook focuses on its origin statistical properties and related distributions. The book also presents various approaches to estimate the parameters of the Weibull distribution under all possible situations of sampling data as well as approaches to parameter and goodness-of-fit testing. Describes the Statistical Methods Concepts Theories and Applications of This DistributionCompiling findings from dozens of scientific journals and hundreds of research papers the author first gives a careful and thorough mathematical description of the Weibull distribution and all of its features. He then deals with Weibull analysis using classical and Bayesian approaches along with graphical and linear maximum likelihood techniques to estimate the three Weibull parameters. The author also explores the inference of Weibull processes Weibull parameter testing and different types of goodness-of-fit tests and methods. Successfully Apply the Weibull ModelBy using inferential procedures for estimating testing forecasting and simulating data this self-contained detailed handbook shows how to solve statistical life science and engineering problems. | The Weibull Distribution A Handbook

GBP 44.99
1

An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. Rank-based and resampling techniques are heavily represented but robust techniques are considered as well. These techniques include one-sample testing and estimation multi-sample testing and estimation and regression. Attention is paid to the intellectual development of the field with a thorough review of bibliographical references. Computational tools in R and SAS are developed and illustrated via examples. Exercises designed to reinforce examples are included. Features Rank-based techniques including sign Kruskal-Wallis Friedman Mann-Whitney and Wilcoxon tests are presented Tests are inverted to produce estimates and confidence intervals Multivariate tests are explored Techniques reflecting the dependence of a response variable on explanatory variables are presented Density estimation is explored The bootstrap and jackknife are discussed This text is intended for a graduate student in applied statistics. The course is best taken after an introductory course in statistical methodology elementary probability and regression. Mathematical prerequisites include calculus through multivariate differentiation and integration and ideally a course in matrix algebra.

GBP 84.99
1

Component-Based Software Engineering Methods and Metrics

Component-Based Software Engineering Methods and Metrics

This book focuses on a specialized branch of the vast domain of software engineering: component-based software engineering (CBSE). Component-Based Software Engineering: Methods and Metrics enhances the basic understanding of components by defining categories characteristics repository interaction complexity and composition. It divides the research domain of CBSE into three major sub-domains: (1) reusability issues (2) interaction and integration issues and (3) testing and reliability issues. This book covers the state-of-the-art literature survey of at least 20 years in the domain of reusability interaction and integration complexities and testing and reliability issues of component-based software engineering. The aim of this book is not only to review and analyze the previous works conducted by eminent researchers academicians and organizations in the context of CBSE but also suggests innovative efficient and better solutions. A rigorous and critical survey of traditional and advanced paradigms of software engineering is provided in the book. Features: In-interactions and Out-Interactions both are covered to assess the complexity. In the context of CBSE both white-box and black-box testing methods and their metrics are described. This work covers reliability estimation using reusability which is an innovative method. Case studies and real-life software examples are used to explore the problems and their solutions. Students research scholars software developers and software designers or individuals interested in software engineering especially in component-based software engineering can refer to this book to understand the concepts from scratch. These measures and metrics can be used to estimate the software before the actual coding commences. | Component-Based Software Engineering Methods and Metrics

GBP 105.00
1

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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Theoretical Statistics

Theoretical Statistics

A text that stresses the general concepts of the theory of statistics Theoretical Statistics provides a systematic statement of the theory of statistics emphasizing general concepts rather than mathematical rigor. Chapters 1 through 3 provide an overview of statistics and discuss some of the basic philosophical ideas and problems behind statistical procedures. Chapters 4 and 5 cover hypothesis testing with simple and null hypotheses respectively. Subsequent chapters discuss non-parametrics interval estimation point estimation asymptotics Bayesian procedure and deviation theory. Student familiarity with standard statistical techniques is assumed.

GBP 180.00
1

A Course in the Large Sample Theory of Statistical Inference

A Course in the Large Sample Theory of Statistical Inference

This book provides an accessible but rigorous introduction to asymptotic theory in parametric statistical models. Asymptotic results for estimation and testing are derived using the “moving alternative” formulation due to R. A. Fisher and L. Le Cam. Later chapters include discussions of linear rank statistics and of chi-squared tests for contingency table analysis including situations where parameters are estimated from the complete ungrouped data. This book is based on lecture notes prepared by the first author subsequently edited expanded and updated by the second author. Key features: Succinct account of the concept of “asymptotic linearity” and its uses Simplified derivations of the major results under an assumption of joint asymptotic normality Inclusion of numerical illustrations practical examples and advice Highlighting some unexpected consequences of the theory Large number of exercises many with hints to solutions Some facility with linear algebra and with real analysis including ‘epsilon-delta’ arguments is required. Concepts and results from measure theory are explained when used. Familiarity with undergraduate probability and statistics including basic concepts of estimation and hypothesis testing is necessary and experience with applying these concepts to data analysis would be very helpful. | A Course in the Large Sample Theory of Statistical Inference

GBP 84.99
1

Handbook of Statistical Methods for Randomized Controlled Trials

Handbook of Statistical Methods for Randomized Controlled Trials

Statistical concepts provide scientific framework in experimental studies including randomized controlled trials. In order to design monitor analyze and draw conclusions scientifically from such clinical trials clinical investigators and statisticians should have a firm grasp of the requisite statistical concepts. The Handbook of Statistical Methods for Randomized Controlled Trials presents these statistical concepts in a logical sequence from beginning to end and can be used as a textbook in a course or as a reference on statistical methods for randomized controlled trials. Part I provides a brief historical background on modern randomized controlled trials and introduces statistical concepts central to planning monitoring and analysis of randomized controlled trials. Part II describes statistical methods for analysis of different types of outcomes and the associated statistical distributions used in testing the statistical hypotheses regarding the clinical questions. Part III describes some of the most used experimental designs for randomized controlled trials including the sample size estimation necessary in planning. Part IV describe statistical methods used in interim analysis for monitoring of efficacy and safety data. Part V describe important issues in statistical analyses such as multiple testing subgroup analysis competing risks and joint models for longitudinal markers and clinical outcomes. Part VI addresses selected miscellaneous topics in design and analysis including multiple assignment randomization trials analysis of safety outcomes non-inferiority trials incorporating historical data and validation of surrogate outcomes.

GBP 59.99
1

Statistical Design and Analysis of Stability Studies

Statistical Design and Analysis of Stability Studies

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

GBP 44.99
1

Statistical Inference Based on Divergence Measures

Statistical Inference Based on Divergence Measures

The idea of using functionals of Information Theory such as entropies or divergences in statistical inference is not new. However in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models many statisticians remain unaware of this powerful approach. Statistical Inference Based on Divergence Measures explores classical problems of statistical inference such as estimation and hypothesis testing on the basis of measures of entropy and divergence. The first two chapters form an overview from a statistical perspective of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations presenting the interesting possibility of introducing alternative test statistics to classical ones like Wald Rao and likelihood ratio. Each chapter concludes with exercises that clarify the theoretical results and present additional results that complement the main discussions. Clear comprehensive and logically developed this book offers a unique opportunity to gain not only a new perspective on some standard statistics problems but the tools to put it into practice.

GBP 44.99
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Project-Based R Companion to Introductory Statistics

Project-Based R Companion to Introductory Statistics

Project-Based R Companion to Introductory Statistics is envisioned as a companion to a traditional statistics or biostatistics textbook with each chapter covering traditional topics such as descriptive statistics regression and hypothesis testing. However unlike a traditional textbook each chapter will present its material using a complete step-by-step analysis of a real publicly available dataset with an emphasis on the practical skills of testing assumptions data exploration and forming conclusions. The chapters in the main body of the book include a worked example showing the R code used at each step followed by a multi-part project for students to complete. These projects which could serve as alternatives to traditional discrete homework problems will illustrate how to put the pieces together and conduct a complete start-to-finish data analysis using the R statistical software package. At the end of the book there are several projects that require the use of multiple statistical techniques that could be used as a take-home final exam or final project for a class. Key features of the text: Organized in chapters focusing on the same topics found in typical introductory statistics textbooks (descriptive statistics regression two-way tables hypothesis testing for means and proportions etc. ) so instructors can easily pair this supplementary material with course plans Includes student projects for each chapter which can be assigned as laboratory exercises or homework assignments to supplement traditional homework Features real-world datasets from scientific publications in the fields of history pop culture business medicine and forensics for students to analyze Allows students to gain experience working through a variety of statistical analyses from start to finish The book is written at the undergraduate level to be used in an introductory statistical methods course or subject-specific research methods course such as biostatistics or research methods for psychology or business analytics. Author After a 10-year career as a research biostatistician in the Department of Ophthalmology and Visual Sciences at the University of Wisconsin-Madison Chelsea Myers teaches statistics and biostatistics at Rollins College and Valencia College in Central Florida. She has authored or co-authored more than 30 scientific papers and presentations and is the creator of the MCAT preparation website MCATMath. com.

GBP 48.99
1

A Concise Introduction to Robot Programming with ROS2

A Concise Introduction to Robot Programming with ROS2

A Concise Introduction to Robot Programming with ROS2 provides the reader with the concepts and tools necessary to bring a robot to life through programming. It will equip the reader with the skills necessary to undertake projects with ROS2 the new version of ROS. It is not necessary to have previous experience with ROS2 as it will describe its concepts tools and methodologies from the beginning. Key Features Uses the two programming languages officially supported in ROS2 (C++ mainly and Python) Approaches ROS2 from three different but complementary dimensions: the Community Computation Graph and the Workspace Includes a complete simulated robot development and testing strategies Behavior Trees and Nav2 description setup and use A GitHub repository with code to assist readers It will appeal to motivated engineering students engineers and professionals working with robot programming.

GBP 44.99
1

Software Engineering for Science

Software Engineering for Science

Software Engineering for Science provides an in-depth collection of peer-reviewed chapters that describe experiences with applying software engineering practices to the development of scientific software. It provides a better understanding of how software engineering is and should be practiced and which software engineering practices are effective for scientific software. The book starts with a detailed overview of the Scientific Software Lifecycle and a general overview of the scientific software development process. It highlights key issues commonly arising during scientific software development as well as solutions to these problems. The second part of the book provides examples of the use of testing in scientific software development including key issues and challenges. The chapters then describe solutions and case studies aimed at applying testing to scientific software development efforts. The final part of the book provides examples of applying software engineering techniques to scientific software including not only computational modeling but also software for data management and analysis. The authors describe their experiences and lessons learned from developing complex scientific software in different domains. About the EditorsJeffrey Carver is an Associate Professor in the Department of Computer Science at the University of Alabama. He is one of the primary organizers of the workshop series on Software Engineering for Science (http://www. SE4Science. org/workshops). Neil P. Chue Hong is Director of the Software Sustainability Institute at the University of Edinburgh. His research interests include barriers and incentives in research software ecosystems and the role of software as a research object. George K. Thiruvathukal is Professor of Computer Science at Loyola University Chicago and Visiting Faculty at Argonne National Laboratory. His current research is focused on software metrics in open source mathematical and scientific software.

GBP 44.99
1

Data Analysis Using Hierarchical Generalized Linear Models with R

Data Analysis Using Hierarchical Generalized Linear Models with R

Since their introduction hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown and various practical analytical tools have been developed. This book summarizes developments within the field and using data examples illustrates how to analyse various kinds of data using R. It provides a likelihood approach to advanced statistical modelling including generalized linear models with random effects survival analysis and frailty models multivariate HGLMs factor and structural equation models robust modelling of random effects models including penalty and variable selection and hypothesis testing. This example-driven book is aimed primarily at researchers and graduate students who wish to perform data modelling beyond the frequentist framework and especially for those searching for a bridge between Bayesian and frequentist statistics. | Data Analysis Using Hierarchical Generalized Linear Models with R

GBP 44.99
1

Statistics for Technology A Course in Applied Statistics Third Edition

Statistics for Technology A Course in Applied Statistics Third Edition

One of the most popular introductory texts in its field Statistics for Technology: A Course in Applied Studies presents the range of statistical methods commonly used in science social science and engineering. The mathematics are simple and straightforward; statistical concepts are explained carefully; and real-life (rather than contrived) examples are used throughout the chapters. Divided into three parts the Introduction describes some simple methods of summarizing data. Theory examines the basic concepts and theory of statistics. Applications covers the planning and procedures of experiments quality control and life testing. Revised throughout this Third Edition places a higher priority on the role of computers in analysis and many new references have been incorporated. A new appendix describes general methods of tackling statistical problems including guidance on literature searching and report writing. | Statistics for Technology A Course in Applied Statistics Third Edition

GBP 180.00
1

Computational Aspects of Psychometric Methods With R

Computational Aspects of Psychometric Methods With R

This book covers the computational aspects of psychometric methods involved in developing measurement instruments and analyzing measurement data in social sciences. It covers the main topics of psychometrics such as validity reliability item analysis item response theory models and computerized adaptive testing. The computational aspects comprise the statistical theory and models comparison of estimation methods and algorithms as well as an implementation with practical data examples in R and also in an interactive ShinyItemAnalysis application. Key Features: Statistical models and estimation methods involved in psychometric research Includes reproducible R code and examples with real datasets Interactive implementation in ShinyItemAnalysis application The book is targeted toward a wide range of researchers in the field of educational psychological and health-related measurements. It is also intended for those developing measurement instruments and for those collecting and analyzing data from behavioral measurements who are searching for a deeper understanding of underlying models and further development of their analytical skills. | Computational Aspects of Psychometric Methods With R

GBP 54.99
1

Handbook of Item Response Theory Three Volume Set

Handbook of Item Response Theory Three Volume Set

Drawing on the work of 75 internationally acclaimed experts in the field Handbook of Item Response Theory Three-Volume Set presents all major item response models classical and modern statistical tools used in item response theory (IRT) and major areas of applications of IRT in educational and psychological testing medical diagnosis of patient-reported outcomes and marketing research. It also covers CRAN packages WinBUGS Bilog MG Multilog Parscale IRTPRO Mplus GLLAMM Latent Gold and numerous other software tools. A full update of editor Wim J. van der Linden and Ronald K. Hambleton’s classic Handbook of Modern Item Response Theory this handbook has been expanded from 28 chapters to 85 chapters in three volumes. The three volumes are thoroughly edited and cross-referenced with uniform notation format and pedagogical principles across all chapters. Each chapter is self-contained and deals with the latest developments in IRT. | Handbook of Item Response Theory Three Volume Set

GBP 140.00
1

Design and Analysis of Experiments and Observational Studies using R

Design and Analysis of Experiments and Observational Studies using R

Introduction to Design and Analysis of Scientific Studies exposes undergraduate and graduate students to the foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model. A causal inference framework is important in design data collection and analysis since it provides a framework for investigators to readily evaluate study limitations and draw appropriate conclusions. R is used to implement designs and analyse the data collected. Features: Classical experimental design with an emphasis on computation using tidyverse packages in R. Applications of experimental design to clinical trials A/B testing and other modern examples. Discussion of the link between classical experimental design and causal inference. The role of randomization in experimental design and sampling in the big data era. Exercises with solutions. Instructor slides in RMarkdown a new R package will be developed to be used with book and a bookdown version of the book will be freely available. The proposed book will emphasize ethics communication and decision making as part of design data analysis and statistical thinking. | Design and Analysis of Experiments and Observational Studies using R

GBP 82.99
1

Applied Stochastic Modelling

Applied Stochastic Modelling

Highlighting modern computational methods Applied Stochastic Modelling Second Edition provides students with the practical experience of scientific computing in applied statistics through a range of interesting real-world applications. It also successfully revises standard probability and statistical theory. Along with an updated bibliography and improved figures this edition offers numerous updates throughout. New to the Second Edition An extended discussion on Bayesian methods A large number of new exercises A new appendix on computational methods The book covers both contemporary and classical aspects of statistics including survival analysis Kernel density estimation Markov chain Monte Carlo hypothesis testing regression bootstrap and generalised linear models. Although the book can be used without reference to computational programs the author provides the option of using powerful computational tools for stochastic modelling. All of the data sets and MATLAB and R programs found in the text as well as lecture slides and other ancillary material are available for download at www. crcpress. com Continuing in the bestselling tradition of its predecessor this textbook remains an excellent resource for teaching students how to fit stochastic models to data.

GBP 175.00
1

Fundamentals of Mathematical Statistics

Fundamentals of Mathematical Statistics

Fundamentals of Mathematical Statistics is meant for a standard one-semester advanced undergraduate or graduate-level course in Mathematical Statistics. It covers all the key topics—statistical models linear normal models exponential families estimation asymptotics of maximum likelihood significance testing and models for tables of counts. It assumes a good background in mathematical analysis linear algebra and probability but includes an appendix with basic results from these areas. Throughout the text there are numerous examples and graduated exercises that illustrate the topics covered rendering the book suitable for teaching or self-study. Features A concise yet rigorous introduction to a one-semester course in Mathematical Statistics Covers all the key topics Assumes a solid background in Mathematics and Probability Numerous examples illustrate the topics Many exercises enhance understanding of the material and enable course use This textbook will be a perfect fit for an advanced course in Mathematical Statistics or Statistical Theory. The concise and lucid approach means it could also serve as a good alternative or supplement to existing texts.

GBP 74.99
1

Basic Statistics and Pharmaceutical Statistical Applications

Basic Statistics and Pharmaceutical Statistical Applications

Building on its best-selling predecessors Basic Statistics and Pharmaceutical Statistical Applications Third Edition covers statistical topics most relevant to those in the pharmaceutical industry and pharmacy practice. It focuses on the fundamentals required to understand descriptive and inferential statistics for problem solving. Incorporating new material in virtually every chapter this third edition now provides information on software applications to assist with evaluating data. New to the Third EditionUse of Excel® and Minitab® for performing statistical analysisDiscussions of nonprobability sampling procedures determining if data is normally distributed evaluation of covariances and testing for precision equivalenceExpanded sections on regression analysis chi square tests tests for trends with ordinal data and tests related to survival statistics Additional nonparametric procedures including the one-sided sign test Wilcoxon signed-ranks test and Mood’s median testWith the help of flow charts and tables the author dispels some of the anxiety associated with using basic statistical tests in the pharmacy profession and helps readers correctly interpret their results using statistical software. Through the text’s worked-out examples readers better understand how the mathematics works the logic behind many of the equations and the tests’ outcomes.

GBP 44.99
1

Statistics in Toxicology Using R

Statistics in Toxicology Using R

The apparent contradiction between statistical significance and biological relevance has diminished the value of statistical methods as a whole in toxicology. Moreover recommendations for statistical analysis are imprecise in most toxicological guidelines. Addressing these dilemmas Statistics in Toxicology Using R explains the statistical analysis of selected experimental data in toxicology and presents assay-specific suggestions such as for the in vitro micronucleus assay. Mostly focusing on hypothesis testing the book covers standardized bioassays for chemicals drugs and environmental pollutants. It is organized according to selected toxicological assays including: Short-term repeated toxicity studiesLong-term carcinogenicity assays Studies on reproductive toxicity Mutagenicity assays Toxicokinetic studies The book also discusses proof of safety (particularly in ecotoxicological assays) toxicogenomics the analysis of interlaboratory studies and the modeling of dose-response relationships for risk assessment. For each toxicological problem the author describes the statistics involved matching data example R code and outcomes and their interpretation. This approach allows you to select a certain bioassay identify the specific data structure run the R code with the data example understand the test outcome and interpretation and replace the data set with your own data and run again.

GBP 44.99
1

Cure Models Methods Applications and Implementation

Cure Models Methods Applications and Implementation

Cure Models: Methods Applications and Implementation is the first book in the last 25 years that provides a comprehensive and systematic introduction to the basics of modern cure models including estimation inference and software. This book is useful for statistical researchers and graduate students and practitioners in other disciplines to have a thorough review of modern cure model methodology and to seek appropriate cure models in applications. The prerequisites of this book include some basic knowledge of statistical modeling survival models and R and SAS for data analysis. The book features real-world examples from clinical trials and population-based studies and a detailed introduction to R packages SAS macros and WinBUGS programs to fit some cure models. The main topics covered include the foundation of statistical estimation and inference of cure models for independent and right-censored survival data cure modeling for multivariate recurrent-event and competing-risks survival data and joint modeling with longitudinal data statistical testing for the existence and difference of cure rates and sufficient follow-up new developments in Bayesian cure models applications of cure models in public health research and clinical trials. | Cure Models Methods Applications and Implementation

GBP 35.99
1

Handbook of Spatial Epidemiology

Handbook of Spatial Epidemiology

Handbook of Spatial Epidemiology explains how to model epidemiological problems and improve inference about disease etiology from a geographical perspective. Top epidemiologists geographers and statisticians share interdisciplinary viewpoints on analyzing spatial data and space–time variations in disease incidences. These analyses can provide important information that leads to better decision making in public health. The first part of the book addresses general issues related to epidemiology GIS environmental studies clustering and ecological analysis. The second part presents basic statistical methods used in spatial epidemiology including fundamental likelihood principles Bayesian methods and testing and nonparametric approaches. With a focus on special methods the third part describes geostatistical models splines quantile regression focused clustering mixtures multivariate methods and much more. The final part examines special problems and application areas such as residential history analysis segregation health services research health surveys infectious disease veterinary topics and health surveillance and clustering. Spatial epidemiology also known as disease mapping studies the geographical or spatial distribution of health outcomes. This handbook offers a wide-ranging overview of state-of-the-art approaches to determine the relationships between health and various risk factors empowering researchers and policy makers to tackle public health problems.

GBP 66.99
1