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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

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

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

Theory of Statistical Inference

Theory of Statistical Inference

Theory of Statistical Inference is designed as a reference on statistical inference for researchers and students at the graduate or advanced undergraduate level. It presents a unified treatment of the foundational ideas of modern statistical inference and would be suitable for a core course in a graduate program in statistics or biostatistics. The emphasis is on the application of mathematical theory to the problem of inference leading to an optimization theory allowing the choice of those statistical methods yielding the most efficient use of data. The book shows how a small number of key concepts such as sufficiency invariance stochastic ordering decision theory and vector space algebra play a recurring and unifying role. The volume can be divided into four sections. Part I provides a review of the required distribution theory. Part II introduces the problem of statistical inference. This includes the definitions of the exponential family invariant and Bayesian models. Basic concepts of estimation confidence intervals and hypothesis testing are introduced here. Part III constitutes the core of the volume presenting a formal theory of statistical inference. Beginning with decision theory this section then covers uniformly minimum variance unbiased (UMVU) estimation minimum risk equivariant (MRE) estimation and the Neyman-Pearson test. Finally Part IV introduces large sample theory. This section begins with stochastic limit theorems the δ-method the Bahadur representation theorem for sample quantiles large sample U-estimation the Cramér-Rao lower bound and asymptotic efficiency. A separate chapter is then devoted to estimating equation methods. The volume ends with a detailed development of large sample hypothesis testing based on the likelihood ratio test (LRT) Rao score test and the Wald test. Features This volume includes treatment of linear and nonlinear regression models ANOVA models generalized linear models (GLM) and generalized estimating equations (GEE). An introduction to decision theory (including risk admissibility classification Bayes and minimax decision rules) is presented. The importance of this sometimes overlooked topic to statistical methodology is emphasized. The volume emphasizes throughout the important role that can be played by group theory and invariance in statistical inference. Nonparametric (rank-based) methods are derived by the same principles used for parametric models and are therefore presented as solutions to well-defined mathematical problems rather than as robust heuristic alternatives to parametric methods. Each chapter ends with a set of theoretical and applied exercises integrated with the main text. Problems involving R programming are included. Appendices summarize the necessary background in analysis matrix algebra and group theory.

GBP 99.99
1

Exercises and Solutions in Biostatistical Theory

Exercises and Solutions in Biostatistical Theory

Drawn from nearly four decades of Lawrence L. Kupper‘s teaching experiences as a distinguished professor in the Department of Biostatistics at the University of North Carolina Exercises and Solutions in Biostatistical Theory presents theoretical statistical concepts numerous exercises and detailed solutions that span topics from basic probability to statistical inference. The text links theoretical biostatistical principles to real-world situations including some of the authors own biostatistical work that has addressed complicated design and analysis issues in the health sciences. This classroom-tested material is arranged sequentially starting with a chapter on basic probability theory followed by chapters on univariate distribution theory and multivariate distribution theory. The last two chapters on statistical inference cover estimation theory and hypothesis testing theory. Each chapter begins with an in-depth introduction that summarizes the biostatistical principles needed to help solve the exercises. Exercises range in level of difficulty from fairly basic to more challenging (identified with asterisks). By working through the exercises and detailed solutions in this book students will develop a deep understanding of the principles of biostatistical theory. The text shows how the biostatistical theory is effectively used to address important biostatistical issues in a variety of real-world settings. Mastering the theoretical biostatistical principles described in the book will prepare students for successful study of higher-level statistical theory and will help them become better biostatisticians.

GBP 175.00
1

Survival Analysis

Survival Analysis

Survival analysis generally deals with analysis of data arising from clinical trials. Censoring truncation and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties essentially asymptotic ones of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way. Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis. Features: Classical survival analysis techniques for estimating statistical functional and hypotheses testing Regression methods covering the popular Cox relative risk regression model Aalen’s additive hazards model etc. Information criteria to facilitate model selection including Akaike Bayes and Focused Penalized methods Survival trees and ensemble techniques of bagging boosting and random survival forests A brief exposure of neural networks for survival data R program illustration throughout the book

GBP 99.99
1

Fundamentals of Internet of Things

Fundamentals of Internet of Things

The Internet of Things (IoT) networks have revolutionized the world and have innumerable real-time applications on automation. A few examples include driverless cars remote monitoring of the elderly remote order of tea or coffee of your choice from a vending machine and home/industrial automation amongst others. Fundamentals of Internet of Things build the foundations of IoT networks by leveraging the relevant concepts from signal processing communications net-works and machine learning. The book covers two fundamental components of IoT networks namely the Internet and Things. In particular the book focuses on networking concepts protocols clustering data fusion localization energy harvesting control optimization data analytics fog computing privacy and security including elliptic curve cryptography and blockchain technology. Most of the existing books are theoretical and without many mathematical details and examples. In addition some essential topics of the IoT networks are also missing in the existing books. Features: • The book covers cutting-edge research topics• Provides mathematical understanding of the topics in addition to relevant theory and insights• Includes illustrations with hand-solved numerical examples for visualization of the theory and testing of understanding• Lucid and crisp explanation to lessen the study time of the reader The book is a complete package of the fundamentals of IoT networks and is suitable for graduate-level students and researchers who want to dive into the world of IoT networks.

GBP 145.00
1

Intensive Longitudinal Analysis of Human Processes

Intensive Longitudinal Analysis of Human Processes

This book focuses on a span of statistical topics relevant to researchers who seek to conduct person-specific analysis of human data. Our purpose is to provide one consolidated resource that includes techniques from disciplines such as engineering physics statistics and quantitative psychology and outlines their application to data often seen in human research. The book balances mathematical concepts with information needed for using these statistical approaches in applied settings such as interpretative caveats and issues to consider when selecting an approach. The statistical topics covered here include foundational material as well as state-of-the-art methods. These analytic approaches can be applied to a range of data types such as psychophysiological self-report and passively collected measures such as those obtained from smartphones. We provide examples using varied data sources including functional MRI (fMRI) daily diary and ecological momentary assessment data. Features: Description of time series measurement model building and network methods for person-specific analysis Discussion of the statistical methods in the context of human research Empirical and simulated data examples used throughout the book R code for analyses and recorded lectures for each chapter available at the book website: https://www. personspecific. com/ Across various disciplines of human study researchers are increasingly seeking to conduct person-specific analysis. This book provides comprehensive information so no prior knowledge of these methods is required. We aim to reach active researchers who already have some understanding of basic statistical testing. Our book provides a comprehensive resource for those who are just beginning to learn about person-specific analysis as well as those who already conduct such analysis but seek to further deepen their knowledge and learn new tools. | Intensive Longitudinal Analysis of Human Processes

GBP 89.99
1

Linear Mixed Models A Practical Guide Using Statistical Software

Linear Mixed Models A Practical Guide Using Statistical Software

Highly recommended by JASA Technometrics and other leading statistical journals the first two editions of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software Third Edition continues to lead readers step-by-step through the process of fitting LMMs. The third edition provides a comprehensive update of the available tools for fitting linear mixed-effects models in the newest versions of SAS SPSS R Stata and HLM. All examples have been updated with a focus on new tools for visualization of results and interpretation. New conceptual and theoretical developments in mixed-effects modeling have been included and there is a new chapter on power analysis for mixed-effects models. Features:•Dedicates an entire chapter to the key theories underlying LMMs for clustered longitudinal and repeated measures data•Provides descriptions explanations and examples of software code necessary to fit LMMs in SAS SPSS R Stata and HLM•Contains detailed tables of estimates and results allowing for easy comparisons across software procedures•Presents step-by-step analyses of real-world data sets that arise from a variety of research settings and study designs including hypothesis testing interpretation of results and model diagnostics•Integrates software code in each chapter to compare the relative advantages and disadvantages of each package•Supplemented by a website with software code datasets additional documents and updates Ideal for anyone who uses software for statistical modeling this book eliminates the need to read multiple software-specific texts by covering the most popular software programs for fitting LMMs in one handy guide. The authors illustrate the models and methods through real-world examples that enable comparisons of model-fitting options and results across the software procedures. | Linear Mixed Models A Practical Guide Using Statistical Software

GBP 89.99
1

Probability and Statistics for Computer Scientists

Probability and Statistics for Computer Scientists

Praise for the Second Edition: The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter. [He] has taken great care to select examples that are interesting and practical for computer scientists. . The content is illustrated with numerous figures and concludes with appendices and an index. The book is erudite and … could work well as a required text for an advanced undergraduate or graduate course. Computing Reviews Probability and Statistics for Computer Scientists Third Edition helps students understand fundamental concepts of Probability and Statistics general methods of stochastic modeling simulation queuing and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB this classroom-tested book can be used for one- or two-semester courses. Features: Axiomatic introduction of probability Expanded coverage of statistical inference and data analysis including estimation and testing Bayesian approach multivariate regression chi-square tests for independence and goodness of fit nonparametric statistics and bootstrap Numerous motivating examples and exercises including computer projects Fully annotated R codes in parallel to MATLAB Applications in computer science software engineering telecommunications and related areas In-Depth yet Accessible Treatment of Computer Science-Related TopicsStarting with the fundamentals of probability the text takes students through topics heavily featured in modern computer science computer engineering software engineering and associated fields such as computer simulations Monte Carlo methods stochastic processes Markov chains queuing theory statistical inference and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET). About the Author Michael Baron is David Carroll Professor of Mathematics and Statistics at American University in Washington D. C. He conducts research in sequential analysis and optimal stopping change-point detection Bayesian inference and applications of statistics in epidemiology clinical trials semiconductor manufacturing and other fields. M. Baron is a Fellow of the American Statistical Association and a recipient of the Abraham Wald Prize for the best paper in Sequential Analysis and the Regents Outstanding Teaching Award. M. Baron holds a Ph. D. in statistics from the University of Maryland. In his turn he supervised twelve doctoral students mostly employed on academic and research positions.

GBP 99.99
1