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Elsevier

Books in Statistics and probability

Exploring the fundamentals and advanced techniques of statistical analysis and probabilistic modeling, this selection supports data scientists, researchers, and decision-makers. It features cutting-edge methods, applications in industry, and case studies addressing uncertainty, risk assessment, and data-driven decision-making. These resources foster accurate interpretation, predictive analytics, and evidence-based insights essential for innovation in research, healthcare, finance, and policy development.

  • Disease Modelling and Public Health, Part B

    • 1st Edition
    • Volume 37
    • English
    Handbook of Statistics: Disease Modelling and Public Health, Part B, Volume 37 addresses new challenges in existing and emerging diseases. As a two part volume, this title covers an extensive range of techniques in the field, with this book including chapters on Reaction diffusion equations and their application on bacterial communication, Spike and slab methods in disease modeling, Mathematical modeling of mass screening and parameter estimation, Individual-based and agent-based models for infectious disease transmission and evolution: an overview, and a section on Visual Clustering of Static and Dynamic High Dimensional Data. This volume covers the lack of availability of complete data relating to disease symptoms and disease epidemiology, one of the biggest challenges facing vaccine developers, public health planners, epidemiologists and health sector researchers.

  • Disease Modelling and Public Health, Part A

    • 1st Edition
    • Volume 36
    • English
    Disease Modelling and Public Health, Part A, Volume 36 addresses new challenges in existing and emerging diseases with a variety of comprehensive chapters that cover Infectious Disease Modeling, Bayesian Disease Mapping for Public Health, Real time estimation of the case fatality ratio and risk factor of death, Alternative Sampling Designs for Time-To-Event Data with Applications to Biomarker Discovery in Alzheimer's Disease, Dynamic risk prediction for cardiovascular disease: An illustration using the ARIC Study, Theoretical advances in type 2 diabetes, Finite Mixture Models in Biostatistics, and Models of Individual and Collective Behavior for Public Health Epidemiology. As a two part volume, the series covers an extensive range of techniques in the field. It present a vital resource for statisticians who need to access a number of different methods for assessing epidemic spread in population, or in formulating public health policy.

  • Biostatistics and Computer-based Analysis of Health Data Using SAS

    • 1st Edition
    • Christophe Lalanne + 1 more
    • English
    This volume of the Biostatistics and Health Sciences Set focuses on statistics applied to clinical research.The use of SAS for data management and statistical modeling is illustrated using various examples. Many aspects of data processing and statistical analysis of cross-sectional and experimental medical data are covered, including regression models commonly found in medical statistics. This practical book is primarily intended for health researchers with a basic knowledge of statistical methodology. Assuming basic concepts, the authors focus on the practice of biostatistical methods essential to clinical research, epidemiology and analysis of biomedical data (including comparison of two groups, analysis of categorical data, ANOVA, linear and logistic regression, and survival analysis). The use of examples from clinical trials and epidemiological studies provide the basis for a series of practical exercises, which provide instruction and familiarize the reader with essential SAS commands.

  • Inequalities and Extremal Problems in Probability and Statistics

    Selected Topics
    • 1st Edition
    • Iosif Pinelis + 4 more
    • English
    Inequalities and Extremal Problems in Probability and Statistics: Selected Topics presents various kinds of useful inequalities that are applicable in many areas of mathematics, the sciences, and engineering. The book enables the reader to grasp the importance of inequalities and how they relate to probability and statistics. This will be an extremely useful book for researchers and graduate students in probability, statistics, and econometrics, as well as specialists working across sciences, engineering, financial mathematics, insurance, and mathematical modeling of large risks.

  • Optimal Sports Math, Statistics, and Fantasy

    • 1st Edition
    • Robert Kissell + 1 more
    • English
    Optimal Sports Math, Statistics, and Fantasy provides the sports community—students, professionals, and casual sports fans—with the essential mathematics and statistics required to objectively analyze sports teams, evaluate player performance, and predict game outcomes. These techniques can also be applied to fantasy sports competitions. Readers will learn how to: Accurately rank sports teams Compute winning probability Calculate expected victory margin Determine the set of factors that are most predictive of team and player performance Optimal Sports Math, Statistics, and Fantasy also illustrates modeling techniques that can be used to decode and demystify the mysterious computer ranking schemes that are often employed by post-season tournament selection committees in college and professional sports. These methods offer readers a verifiable and unbiased approach to evaluate and rank teams, and the proper statistical procedures to test and evaluate the accuracy of different models. Optimal Sports Math, Statistics, and Fantasy delivers a proven best-in-class quantitative modeling framework with numerous applications throughout the sports world.

  • Survey Sampling Theory and Applications

    • 1st Edition
    • Raghunath Arnab
    • English
    Survey Sampling Theory and Applications offers a comprehensive overview of survey sampling, including the basics of sampling theory and practice, as well as research-based topics and examples of emerging trends. The text is useful for basic and advanced survey sampling courses. Many other books available for graduate students do not contain material on recent developments in the area of survey sampling. The book covers a wide spectrum of topics on the subject, including repetitive sampling over two occasions with varying probabilities, ranked set sampling, Fays method for balanced repeated replications, mirror-match bootstrap, and controlled sampling procedures. Many topics discussed here are not available in other text books. In each section, theories are illustrated with numerical examples. At the end of each chapter theoretical as well as numerical exercises are given which can help graduate students.

  • Introductory Statistics

    • 4th Edition
    • Sheldon M. Ross
    • English
    Introductory Statistics, Fourth Edition, reviews statistical concepts and techniques in a manner that will teach students not only how and when to utilize the statistical procedures developed, but also how to understand why these procedures should be used. The text's main merits are the clarity of presentation, contemporary examples and applications from diverse areas, an explanation of intuition, and the ideas behind the statistical methods. Concepts are motivated, illustrated, and explained in a way that attempts to increase one's intuition. To quote from the preface, it is only when a student develops a feel or intuition for statistics that she or he is really on the path toward making sense of data. Ross achieves this goal through a coherent mix of mathematical analysis, intuitive discussions, and examples. Applications and examples refer to real-world issues, such as gun control, stock price models, health issues, driving age limits, school admission ages, use of helmets, sports, scientific fraud, and many others. Examples relating to data mining techniques using the number of Google queries or Twitter tweets are also considered. For this fourth edition, new topical coverage includes sections on Pareto distribution and the 80-20 rule, Benford's law, added material on odds and joint distributions and correlation, logistic regression, A-B testing, and more modern (big data) examples and exercises.

  • Simulation of Stochastic Processes with Given Accuracy and Reliability

    • 1st Edition
    • Yuriy V. Kozachenko + 3 more
    • English
    Simulation has now become an integral part of research and development across many fields of study. Despite the large amounts of literature in the field of simulation and modeling, one recurring problem is the issue of accuracy and confidence level of constructed models. By outlining the new approaches and modern methods of simulation of stochastic processes, this book provides methods and tools in measuring accuracy and reliability in functional spaces.The authors explore analysis of the theory of Sub-Gaussian (including Gaussian one) and Square Gaussian random variables and processes and Cox processes. Methods of simulation of stochastic processes and fields with given accuracy and reliability in some Banach spaces are also considered.

  • Stochastic Models of Financial Mathematics

    • 1st Edition
    • Vigirdas Mackevicius
    • English
    This book presents a short introduction to continuous-time financial models. An overview of the basics of stochastic analysis precedes a focus on the Black–Scholes and interest rate models. Other topics covered include self-financing strategies, option pricing, exotic options and risk-neutral probabilities. Vasicek, Cox−Ingersoll−Ross, and Heath–Jarrow–Morton interest rate models are also explored.The author presents practitioners with a basic introduction, with more rigorous information provided for mathematicians. The reader is assumed to be familiar with the basics of probability theory. Some basic knowledge of stochastic integration and differential equations theory is preferable, although all preliminary information is given in the first part of the book. Some relatively simple theoretical exercises are also provided.

  • Ruin Probabilities

    Smoothness, Bounds, Supermartingale Approach
    • 1st Edition
    • Yuliya Mishura + 1 more
    • English
    Ruin Probabilities: Smoothness, Bounds, Supermartingale Approach deals with continuous-time risk models and covers several aspects of risk theory. The first of them is the smoothness of the survival probabilities. In particular, the book provides a detailed investigation of the continuity and differentiability of the infinite-horizon and finite-horizon survival probabilities for different risk models. Next, it gives some possible applications of the results concerning the smoothness of the survival probabilities. Additionally, the book introduces the supermartingale approach, which generalizes the martingale one introduced by Gerber, to get upper exponential bounds for the infinite-horizon ruin probabilities in some generalizations of the classical risk model with risky investments.

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