This book discusses various statistical models and their implications for developing landslide susceptibility and risk zonation maps. It also presents a range of statistical techniques, i.e. bivariate and multivariate statistical models and machine learning models, as well as multi-criteria evaluation, pseudo-quantitative and probabilistic approaches. As such, it provides methods and techniques for RS & GIS-based models in spatial distribution for all those engaged in the preparation and development of projects, research, training courses and postgraduate studies. Further, the book offers a valuable resource for students using RS & GIS techniques in their studies.

Take your first steps into learning statistics, and understand the fascinating science of analysing data. Statistics: The Art and Science of Learning from Data, Global Edition, 5th edition by Agresti, Franklin, and Klingenberg is the ideal introduction to the discipline that will familiarise you with the world of statistics and data analysis. Ideal for students who study introductory courses in statistics, this text takes a conceptual approach and will encourage you to learn how to analyse data the right way by enquiring and searching for the right questions and information rather than just memorising procedures. Enjoyable and accessible, yet informative and without compromising the necessary rigour, this edition will help you engage with the science in modern life, delivering a learning experience that is effective in statistical thinking and practice. Key features include: Greater attention to the analysis of proportions compared to other introductory statistics texts. Introduction to key concepts, presenting the categorical data first, and quantitative data after. A wide variety of real-world data in the examples and exercises New sections and updated content will enhance your learning and understanding. Pearson MyLab (R) Students, if Pearson Pearson MyLab Statistics is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. Pearson MyLab Statistics should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information. This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content which is especially relevant to students outside the United States.

Functional Gaussian Approximation for Dependent Structures develops and analyses mathematical models for phenomena that evolve in time and influence each another. It provides a better understanding of the structure and asymptotic behaviour of stochastic processes. Two approaches are taken. Firstly, the authors present tools for dealing with the dependent structures used to obtain normal approximations. Secondly, they apply normal approximations to various examples. The main tools consist of inequalities for dependent sequences of random variables, leading to limit theorems, including the functional central limit theorem and functional moderate deviation principle. The results point out large classes of dependent random variables which satisfy invariance principles, making possible the statistical study of data coming from stochastic processes both with short and long memory. The dependence structures considered throughout the book include the traditional mixing structures, martingale-like structures, and weakly negatively dependent structures, which link the notion of mixing to the notions of association and negative dependence. Several applications are carefully selected to exhibit the importance of the theoretical results. They include random walks in random scenery and determinantal processes. In addition, due to their importance in analysing new data in economics, linear processes with dependent innovations will also be considered and analysed.

This sequel to volume 19 of Handbook on Statistics on Stochastic Processes: Modelling and Simulation is concerned mainly with the theme of reviewing and, in some cases, unifying with new ideas the different lines of research and developments in stochastic processes of applied flavour. This volume consists of 23 chapters addressing various topics in stochastic processes. These include, among others, those on manufacturing systems, random graphs, reliability, epidemic modelling, self-similar processes, empirical processes, time series models, extreme value therapy, applications of Markov chains, modelling with Monte Carlo techniques, and stochastic processes in subjects such as engineering, telecommunications, biology, astronomy and chemistry. particular with modelling, simulation techniques and numerical methods concerned with stochastic processes. The scope of the project involving this volume as well as volume 19 is already clarified in the preface of volume 19. The present volume completes the aim of the project and should serve as an aid to students, teachers, researchers and practitioners interested in applied stochastic processes.

This book focuses on the spatial distribution of landslide hazards of the Darjeeling Himalayas. Knowledge driven methods and statistical techniques such as frequency ratio model (FRM), information value model (IVM), logistic regression model (LRM), index overlay model (IOM), certainty factor model (CFM), analytical hierarchy process (AHP), artificial neural network model (ANN), and fuzzy logic have been adopted to identify landslide susceptibility. In addition, a comparison between various statistical models were made using success rate cure (SRC) and it was found that artificial neural network model (ANN), certainty factor model (CFM) and frequency ratio based fuzzy logic approach are the most reliable statistical techniques in the assessment and prediction of landslide susceptibility in the Darjeeling Himalayas. The study identified very high, high, moderate, low and very low landslide susceptibility locations to take site-specific management options as well as to ensure developmental activities in theDarjeeling Himalayas. Particular attention is given to the assessment of various geomorphic, geotectonic and geohydrologic attributes that help to understand the role of different factors and corresponding classes in landslides, to apply different models, and to monitor and predict landslides. The use of various statistical and physical models to estimate landslide susceptibility is also discussed. The causes, mechanisms and types of landslides and their destructive character are elaborated in the book. Researchers interested in applying statistical tools for hazard zonation purposes will find the book appealing.

Quantum mechanics is arguably one of the most successful scientific theories ever and its applications to chemistry, optics, and information theory are innumerable. This book provides the reader with a rigorous treatment of the main mathematical tools from harmonic analysis which play an essential role in the modern formulation of quantum mechanics. This allows us at the same time to suggest some new ideas and methods, with a special focus on topics such as the Wigner phase space formalism and its applications to the theory of the density operator and its entanglement properties. This book can be used with profit by advanced undergraduate students in mathematics and physics, as well as by confirmed researchers.

This open access book demonstrates how data quality issues affect all surveys and proposes methods that can be utilised to deal with the observable components of survey error in a statistically sound manner. This book begins by profiling the post-Apartheid period in South Africa's history when the sampling frame and survey methodology for household surveys was undergoing periodic changes due to the changing geopolitical landscape in the country. This book profiles how different components of error had disproportionate magnitudes in different survey years, including coverage error, sampling error, nonresponse error, measurement error, processing error and adjustment error. The parameters of interest concern the earnings distribution, but despite this outcome of interest, the discussion is generalizable to any question in a random sample survey of households or firms. This book then investigates questionnaire design and item nonresponse by building a response propensity model for the employee income question in two South African labour market surveys: the October Household Survey (OHS, 1997-1999) and the Labour Force Survey (LFS, 2000-2003). This time period isolates a period of changing questionnaire design for the income question. Finally, this book is concerned with how to employee income data with a mixture of continuous data, bounded response data and nonresponse. A variable with this mixture of data types is called coarse data. Because the income question consists of two parts -- an initial, exact income question and a bounded income follow-up question -- the resulting statistical distribution of employee income is both continuous and discrete. The book shows researchers how to appropriately deal with coarse income data using multiple imputation. The take-home message from this book is that researchers have a responsibility to treat data quality concerns in a statistically sound manner, rather than making adjustments to public-use data in arbitrary ways, often underpinned by undefensible assumptions about an implicit unobservable loss function in the data. The demonstration of how this can be done provides a replicable concept map with applicable methods that can be utilised in any sample survey.

Companion Website materials: https://tzkeith.com/ Multiple Regression and Beyond offers a conceptually-oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. This book: * Covers both MR and SEM, while explaining their relevance to one another * Includes path analysis, confirmatory factor analysis, and latent growth modeling * Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises * Extensive use of figures and tables providing examples and illustrating key concepts and techniques New to this edition: * New chapter on mediation, moderation, and common cause * New chapter on the analysis of interactions with latent variables and multilevel SEM * Expanded coverage of advanced SEM techniques in chapters 18 through 22 * International case studies and examples * Updated instructor and student online resources

This book is a first course in statistics for students of biology. Most of the examples have an ecological bias, but illustrate principles which have direct relevance for biologists doing laboratory work. The structured approach begins with basic concepts, and progresses towards an appreciation of the needs and use of analysis of variance and regression, and includes the use of computer statistical packages. The work is clearly explained with worked examples of real-life biological problems, and should be suitable for undergraduate students engaged in quantitative biological work. Biostatistics should give students a sound grasp of the key principles of biological statistics without overwhelming detail, and should allow students to quickly apply techniques to their own work and data.

For courses in Probability and Random Processes. "Probability, Statistics, and Random Processes for Engineers, 4e "is a useful text for electrical and computer engineers. This book is a comprehensive treatment of probability and random processes that, more than any other available source, combines "rigor" with "accessibility." Beginning with the fundamentals of probability theory and requiring only college-level calculus, the book develops all the tools needed to understand more advanced topics such as random sequences, continuous-time random processes, and statistical signal processing. The book progresses at a leisurely pace, never assuming more knowledge than contained in the material already covered. Rigor is established by developing all results from the basic axioms and carefully defining and discussing such advanced notions as stochastic convergence, stochastic integrals and resolution of stochastic processes.

Expert practical and theoretical coverage of runs and scans

This volume presents both theoretical and applied aspects of runs and scans, and illustrates their important role in reliability analysis through various applications from science and engineering. Runs and Scans with Applications presents new and exciting content in a systematic and cohesive way in a single comprehensive volume, complete with relevant approximations and explanations of some limit theorems.

The authors provide detailed discussions of both classical and current problems, such as:

• Sooner and later waiting time
• Consecutive systems
• Start-up demonstration testing in life-testing experiments
• Learning and memory models
• "Match" in genetic codes

Runs and Scans with Applications offers broad coverage of the subject in the context of reliability and life-testing settings and serves as an authoritative reference for students and professionals alike.

This is the second of a two-volume series on sampling theory. The mathematical foundations were laid in the first volume, and this book surveys the many applications of sampling theory both within mathematics and in other areas of science. Many of the topics covered here are not found in other books, and all are given an up to date treatment bringing the reader's knowledge up to research level. This book consists of ten chapters, written by ten different teams of authors, and the contents range over a wide variety of topics including combinatorial analysis, number theory, neural networks, derivative sampling, wavelets, stochastic signals, random fields, and abstract harmonic analysis. There is a comprehensive, up to date bibliography.

This book provides a coherent framework for understanding shrinkage estimation in statistics. The term refers to modifying a classical estimator by moving it closer to a target which could be known a priori or arise from a model. The goal is to construct estimators with improved statistical properties. The book focuses primarily on point and loss estimation of the mean vector of multivariate normal and spherically symmetric distributions. Chapter 1 reviews the statistical and decision theoretic terminology and results that will be used throughout the book. Chapter 2 is concerned with estimating the mean vector of a multivariate normal distribution under quadratic loss from a frequentist perspective. In Chapter 3 the authors take a Bayesian view of shrinkage estimation in the normal setting. Chapter 4 introduces the general classes of spherically and elliptically symmetric distributions. Point and loss estimation for these broad classes are studied in subsequent chapters. In particular, Chapter 5 extends many of the results from Chapters 2 and 3 to spherically and elliptically symmetric distributions. Chapter 6 considers the general linear model with spherically symmetric error distributions when a residual vector is available. Chapter 7 then considers the problem of estimating a location vector which is constrained to lie in a convex set. Much of the chapter is devoted to one of two types of constraint sets, balls and polyhedral cones. In Chapter 8 the authors focus on loss estimation and data-dependent evidence reports. Appendices cover a number of technical topics including weakly differentiable functions; examples where Stein's identity doesn't hold; Stein's lemma and Stokes' theorem for smooth boundaries; harmonic, superharmonic and subharmonic functions; and modified Bessel functions.

This book discusses risk management, product pricing, capital management and Return on Equity comprehensively and seamlessly. Strategic planning, including the required quantitative methods, is an essential part of bank management and control. A thorough introduction to the advanced methods of risk management for Credit Risk, Counterparty Credit Risk, Market Risk, Operational Risk and Risk Aggregation is provided. In addition, directly applicable concepts and data such as macroeconomic scenarios for strategic planning and stress testing as well as detailed scenarios for Operational Risk and advanced concepts for Credit Risk are presented in straightforward language. The book highlights the implications and chances of the Basel III and Basel IV implementations (2022 onwards), especially in terms of capital management and Return on Equity. A wealth of essential background information from practice, international observations and comparisons, along with numerous illustrative examples, make this book a useful resource for established and future professionals in bank management, risk management, capital management, controlling and accounting.

Providing a clear explanation of the fundamental theory of time series analysis and forecasting, this book couples theory with applications of two popular statistical packages--SAS and SPSS. The text examines moving average, exponential smoothing, Census X-11 deseasonalization, ARIMA, intervention, transfer function, and autoregressive error models and has brief discussions of ARCH and GARCH models. The book features treatments of forecast improvement with regression and autoregression combination models and model and forecast evaluation, along with a sample size analysis for common time series models to attain adequate statistical power. To enhance the book's value as a teaching tool, the data sets and programs used in the book are made available on the Academic Press Web site. The careful linkage of the theoretical constructs with the practical considerations involved in utilizing the statistical packages makes it easy for the user to properly apply these techniques.
Key Features
* Describes principal approaches to time series analysis and forecasting
* Presents examples from public opinion research, policy analysis, political science, economics, and sociology
* Free Web site contains the data used in most chapters, facilitating learning
* Math level pitched to general social science usage
* Glossary makes the material accessible for readers at all levels

This book brings together carefully selected, peer-reviewed works on mathematical biology presented at the BIOMAT International Symposium on Mathematical and Computational Biology, which was held at the Institute of Numerical Mathematics, Russian Academy of Sciences, in October 2017, in Moscow. Topics covered include, but are not limited to, the evolution of spatial patterns on metapopulations, problems related to cardiovascular diseases and modeled by boundary control techniques in hemodynamics, algebraic modeling of the genetic code, and multi-step biochemical pathways. Also, new results are presented on topics like pattern recognition of probability distribution of amino acids, somitogenesis through reaction-diffusion models, mathematical modeling of infectious diseases, and many others. Experts, scientific practitioners, graduate students and professionals working in various interdisciplinary fields will find this book a rich resource for research and applications alike.

Recently, the use of statistical tools, methodologies, and models in human resource management (HRM) has increased because of human resources (HR) analytics and predictive HR decision making. To utilize these technological tools, HR managers and students must increase their knowledge of the resources' optimum application. Statistical Tools and Analysis in Human Resources Management is a critical scholarly resource that presents in-depth details on the application of statistics in every sphere of HR functions for optimal decision-making and analytical solutions. Featuring coverage on a broad range of topics such as leadership, industrial relations, training and development, and diversity management, this book is geared towards managers, professionals, upper-level students, administrators, and researchers seeking current information on the integration of HRM technologies.

The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data. Probability & Statistics, Fourth Edition, was written for a one- or two-semester probability and statistics course. This course is offered primarily at four-year institutions and taken mostly by sophomore and junior level students majoring in mathematics or statistics. Calculus is a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus.

Molecular-Genetic and Statistical Techniques for Behavioral and Neural Research presents the most exciting molecular and recombinant DNA techniques used in the analysis of brain function and behavior, a critical piece of the puzzle for clinicians, scientists, course instructors and advanced undergraduate and graduate students. Chapters examine neuroinformatics, genetic and neurobehavioral databases and data mining, also providing an analysis of natural genetic variation and principles and applications of forward (mutagenesis) and reverse genetics (gene targeting). In addition, the book discusses gene expression and its role in brain function and behavior, along with ethical issues in the use of animals in genetics testing. Written and edited by leading international experts, this book provides a clear presentation of the frontiers of basic research as well as translationally relevant techniques that are used by neurobehavioral geneticists.

This book is the leader among the new generation of text books on quality that follow the systems approach to creating quality in products and services; the earlier generations focused solely on parts of the system such as statistical methods, process control, and management philosophy. It follows the premise that the body of knowledge and tools documented by quality professionals and researchers, when employed in designing, creating and delivering the product will lead to product quality, customer satisfaction and reduced waste. The tools employed at the different stages of the product creation cycle are covered in this book using real world examples along with their theoretical bases, strengths and weaknesses. This textbook can be used for training - from shop floor personnel to college majors in business and engineering to practicing professionals. Graduate students training as researchers in the quality field will also find useful material. The book has been used as the text for a Professional Series Massive Open Online Course offered by the Technical University of Munich on edX.org, through which tens of thousands of participants from all over the world have received training in quality methods. According to Professor Dr. Holly Ott, who chose the book for the course, the text is one of the main factors contributing to success of this MOOC. The Third Edition has been fully revised to be friendly for self-study, reflects changes in the standards referenced such as ISO 9000, and includes new examples of application of statistical tools in health care industry. Features: Reviews the history of quality movement in the U.S. and abroad Discusses Quality Cost analysis and quality's impact on a company's bottom line Explains finding customer needs and designing the product using House of Quality Covers selection of product parameters using DOE and reliability principles Includes control charts to control processes to make the product right-the-first-time Describes use of capability indices Cp and Cpk to meet customer needs Presents problem solving methodology and tools for continuous improvement Offers ISO 9000, Baldrige and Six Sigma as templates for creating a quality system