Skip to main content
Elsevier
View Cart
My account

Books in Statistics and probability

BooksJournals

    • Fundamentals of Advanced Mathematics V2

      • 1st Edition
      • January 17, 2018
      • Henri Bourles
      • English
      • Hardback
        9 7 8 1 7 8 5 4 8 2 4 9 6
      • eBook
        9 7 8 0 0 8 1 0 2 3 8 5 3
      The three volumes of this series of books, of which this is the second, put forward the mathematical elements that make up the foundations of a number of contemporary scientific methods: modern theory on systems, physics and engineering. Whereas the first volume focused on the formal conditions for systems of linear equations (in particular of linear differential equations) to have solutions, this book presents the approaches to finding solutions to polynomial equations and to systems of linear differential equations with varying coefficients. Fundamentals of Advanced Mathematics, Volume 2: Field Extensions, Topology and Topological Vector Spaces, Functional Spaces, and Sheaves begins with the classical Galois theory and the theory of transcendental field extensions. Next, the differential side of these theories is treated, including the differential Galois theory (Picard-Vessiot theory of systems of linear differential equations with time-varying coefficients) and differentially transcendental field extensions. The treatment of analysis includes topology (using both filters and nets), topological vector spaces (using the notion of disked space, which simplifies the theory of duality), and the radon measure (assuming that the usual theory of measure and integration is known). In addition, the theory of sheaves is developed with application to the theory of distributions and the theory of hyperfunctions (assuming that the usual theory of functions of the complex variable is known). This volume is the prerequisite to the study of linear systems with time-varying coefficients from the point-of-view of algebraic analysis and the algebraic theory of nonlinear systems.

    • Reliability Modelling and Analysis in Discrete Time

      • 1st Edition
      • May 15, 2018
      • Unnikrishnan Nair + 2 more
      • English
      • Paperback
        9 7 8 0 1 2 8 0 1 9 1 3 9
      • eBook
        9 7 8 0 1 2 8 0 2 0 0 6 7
      Reliability Modelling and Analysis in Discrete Time provides an overview of the probabilistic and statistical aspects connected with discrete reliability systems. This engaging book discusses their distributional properties and dependence structures before exploring various orderings associated between different reliability structures. Though clear explanations, multiple examples, and exhaustive coverage of the basic and advanced topics of research in this area, the work gives the reader a thorough understanding of the theory and concepts associated with discrete models and reliability structures. A comprehensive bibliography assists readers who are interested in further research and understanding. Requiring only an introductory understanding of statistics, this book offers valuable insight and coverage for students and researchers in Probability and Statistics, Electrical Engineering, and Reliability/Quality Engineering. The book also includes a comprehensive bibliography to assist readers seeking to delve deeper.

    • Disease Modelling and Public Health, Part B

      • 1st Edition
      • Volume 37
      • October 31, 2017
      • English
      • Hardback
        9 7 8 0 4 4 4 6 3 9 7 5 2
      • eBook
        9 7 8 0 4 4 4 6 3 9 7 6 9
      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.

    • Survey Sampling Theory and Applications

      • 1st Edition
      • March 8, 2017
      • Raghunath Arnab
      • English
      • Paperback
        9 7 8 0 1 2 8 1 1 8 4 8 1
      • eBook
        9 7 8 0 1 2 8 1 1 8 9 7 9
      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.

    • Optimal Sports Math, Statistics, and Fantasy

      • 1st Edition
      • April 6, 2017
      • Robert Kissell + 1 more
      • English
      • Hardback
        9 7 8 0 1 2 8 0 5 1 6 3 4
      • eBook
        9 7 8 0 1 2 8 0 5 2 9 3 8
      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.

    • Inequalities and Extremal Problems in Probability and Statistics

      • 1st Edition
      • May 8, 2017
      • Iosif Pinelis + 4 more
      • English
      • Paperback
        9 7 8 0 1 2 8 0 9 8 1 8 9
      • eBook
        9 7 8 0 1 2 8 0 9 8 9 2 9
      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.

    • Handbook of Statistical Analysis and Data Mining Applications

      • 2nd Edition
      • November 9, 2017
      • Ken Yale + 2 more
      • English
      • Hardback
        9 7 8 0 1 2 4 1 6 6 3 2 5
      • eBook
        9 7 8 0 1 2 4 1 6 6 4 5 5
      Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce.

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

      • 1st Edition
      • June 22, 2017
      • Christophe Lalanne + 1 more
      • English
      • Hardback
        9 7 8 1 7 8 5 4 8 1 1 1 6
      • eBook
        9 7 8 0 0 8 1 0 1 1 7 1 3
      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.

    • Occupancy Estimation and Modeling

      • 2nd Edition
      • November 13, 2017
      • Darryl I. MacKenzie + 5 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 4 6 9 1 0
      • eBook
        9 7 8 0 1 2 4 0 7 2 4 5 9
      Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition, provides a synthesis of model-based approaches for analyzing presence-absence data, allowing for imperfect detection. Beginning from the relatively simple case of estimating the proportion of area or sampling units occupied at the time of surveying, the authors describe a wide variety of extensions that have been developed since the early 2000s. This provides an improved insight about species and community ecology, including, detection heterogeneity; correlated detections; spatial autocorrelation; multiple states or classes of occupancy; changes in occupancy over time; species co-occurrence; community-level modeling, and more. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition has been greatly expanded and detail is provided regarding the estimation methods and examples of their application are given. Important study design recommendations are also covered to give a well rounded view of modeling.

    • Statistical Inference in Financial and Insurance Mathematics with R

      • 1st Edition
      • November 22, 2017
      • Alexandre Brouste
      • English
      • Hardback
        9 7 8 1 7 8 5 4 8 0 8 3 6
      • eBook
        9 7 8 0 0 8 1 0 1 2 6 1 1
      Finance and insurance companies are facing a wide range of parametric statistical problems. Statistical experiments generated by a sample of independent and identically distributed random variables are frequent and well understood, especially those consisting of probability measures of an exponential type. However, the aforementioned applications also offer non-classical experiments implying observation samples of independent but not identically distributed random variables or even dependent random variables. Three examples of such experiments are treated in this book. First, the Generalized Linear Models are studied. They extend the standard regression model to non-Gaussian distributions. Statistical experiments with Markov chains are considered next. Finally, various statistical experiments generated by fractional Gaussian noise are also described. In this book, asymptotic properties of several sequences of estimators are detailed. The notion of asymptotical efficiency is discussed for the different statistical experiments considered in order to give the proper sense of estimation risk. Eighty examples and computations with R software are given throughout the text.

Related subjects

Probability theory and stochastic processes

Statistics