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18 results in Statistics and Probability

Bibliography and Modern Book Production
  • Notes and Sources for Student Librarians, Printers, Booksellers, Stationers, Book-collectors
  • Percy Freer
  • Introduction by Veronica Klipp
  • Published by:
    Wits University Press
    Published online:
    17 April 2025
    Print publication:
    01 August 2024
  • Covering key developments in bibliography and publishing, from the history of writing and paper manufacture to the origins of typefaces and printing up to the 1940s.

    Bibliography and Modern Book Production is a fascinating historic journey through the fields of print history, librarianship and publishing. It covers key developments from 1494 to 1949 in bibliography and book production from the history of scripts and paper manufacture to the origins of typefaces and printing. Although not a textbook, the book was a guide for library students in the 1950s on the essential literature of librarianship.

    As the first librarian appointed to Wits University in 1929, Percy Freer's near encyclopaedic knowledge of the subject of bibliography enabled him to develop a key resource for relevant library examinations in South Africa and abroad.

    Due to its immense value as a historic record, and to acknowledge Freer's contributions as scholar, librarian and publisher, it is being reissued as part of the Wits University Press Re/Presents series to make it accessible to scholars in book histories, publishing studies and information science.

Quantitative Research Methods in Corporate Finance
  • Exemplified by Stata, Python, and R
  • Taylan Mavruk
  • Published online:
    20 March 2025
    Print publication:
    20 March 2025
  • Focused on empirical methods and their applications to corporate finance, this innovative text equips students with the knowledge to analyse and critically evaluate quantitative research methods in corporate finance, and conduct computer-aided statistical analyses on various types of datasets. Chapters demonstrate the application of basic econometric models in corporate finance (as opposed to derivations or theorems), backed up by relevant research. Alongside practical examples and mini case studies, computer lab exercises enable students to apply the theories of corporate finance and make stronger connections between theory and practice, while developing their programming skills. All of the Stata code is provided (with corresponding Python and R code available online), so students of all programming abilities can focus on understanding and interpreting the analyses.

Dependence Models via Hierarchical Structures
  • Luis E. Nieto-Barajas
  • Published online:
    20 March 2025
    Print publication:
    27 March 2025
  • Bringing together years of research into one useful resource, this text empowers the reader to creatively construct their own dependence models. Intended for senior undergraduate and postgraduate students, it takes a step-by-step look at the construction of specific dependence models, including exchangeable, Markov, moving average and, in general, spatio-temporal models. All constructions maintain a desired property of pre-specifying the marginal distribution and keeping it invariant. They do not separate the dependence from the marginals and the mechanisms followed to induce dependence are so general that they can be applied to a very large class of parametric distributions. All the constructions are based on appropriate definitions of three building blocks: prior distribution, likelihood function and posterior distribution, in a Bayesian analysis context. All results are illustrated with examples and graphical representations. Applications with data and code are interspersed throughout the book, covering fields including insurance and epidemiology.

Information Theory
  • From Coding to Learning
  • Yury Polyanskiy, Yihong Wu
  • Published online:
    09 January 2025
    Print publication:
    02 January 2025
  • This enthusiastic introduction to the fundamentals of information theory builds from classical Shannon theory through to modern applications in statistical learning, equipping students with a uniquely well-rounded and rigorous foundation for further study. Introduces core topics such as data compression, channel coding, and rate-distortion theory using a unique finite block-length approach. With over 210 end-of-part exercises and numerous examples, students are introduced to contemporary applications in statistics, machine learning and modern communication theory. This textbook presents information-theoretic methods with applications in statistical learning and computer science, such as f-divergences, PAC Bayes and variational principle, Kolmogorov's metric entropy, strong data processing inequalities, and entropic upper bounds for statistical estimation. Accompanied by a solutions manual for instructors, and additional standalone chapters on more specialized topics in information theory, this is the ideal introductory textbook for senior undergraduate and graduate students in electrical engineering, statistics, and computer science.

Time Series for Economics and Finance
  • Oliver Linton
  • Published online:
    19 December 2024
    Print publication:
    19 December 2024
  • Focusing on methods for data that are ordered in time, this textbook provides a comprehensive guide to analyzing time series data using modern techniques from data science. It is specifically tailored to economics and finance applications, aiming to provide students with rigorous training. Chapters cover Bayesian approaches, nonparametric smoothing methods, machine learning, and continuous time econometrics. Theoretical and empirical exercises, concise summaries, bolded key terms, and illustrative examples are included throughout to reinforce key concepts and bolster understanding. Ancillary materials include an instructor's manual with solutions and additional exercises, PowerPoint lecture slides, and datasets. With its clear and accessible style, this textbook is an essential tool for advanced undergraduate and graduate students in economics, finance, and statistics.

Brownian Motion, the Fredholm Determinant, and Time Series Analysis
  • Katsuto Tanaka
  • Published online:
    19 December 2024
    Print publication:
    02 January 2025
  • Brownian motion is an important topic in various applied fields where the analysis of random events is necessary. Introducing Brownian motion from a statistical viewpoint, this detailed text examines the distribution of quadratic plus linear or bilinear functionals of Brownian motion and demonstrates the utility of this approach for time series analysis. It also offers the first comprehensive guide on deriving the Fredholm determinant and the resolvent associated with such statistics. Presuming only a familiarity with standard statistical theory and the basics of stochastic processes, this book brings together a set of important statistical tools in one accessible resource for researchers and graduate students. Readers also benefit from online appendices, which provide probability density graphs and solutions to the chapter problems.

Introduction to Probability and Statistics for Data Science
  • with R
  • Steven E. Rigdon, Ronald D. Fricker, Jr, Douglas C. Montgomery
  • Published online:
    13 December 2024
    Print publication:
    14 November 2024
  • Introduction to Probability and Statistics for Data Science provides a solid course in the fundamental concepts, methods and theory of statistics for students in statistics, data science, biostatistics, engineering, and physical science programs. It teaches students to understand, use, and build on modern statistical techniques for complex problems. The authors develop the methods from both an intuitive and mathematical angle, illustrating with simple examples how and why the methods work. More complicated examples, many of which incorporate data and code in R, show how the method is used in practice. Through this guidance, students get the big picture about how statistics works and can be applied. This text covers more modern topics such as regression trees, large scale hypothesis testing, bootstrapping, MCMC, time series, and fewer theoretical topics like the Cramer-Rao lower bound and the Rao-Blackwell theorem. It features more than 250 high-quality figures, 180 of which involve actual data. Data and R are code available on our website so that students can reproduce the examples and do hands-on exercises.

Experimental Sociology
  • Outline of a Scientific Field
  • Davide Barrera, Klarita Gërxhani, Bernhard Kittel, Luis Miller, Tobias Wolbring
  • Published online:
    23 November 2024
    Print publication:
    21 November 2024
  • Experiments have gained prominence in sociology in recent years. Increased interest in testing causal theories through experimental designs has ignited a debate about which experimental designs can facilitate scientific progress in sociology. This book discusses the implications of research interests for the design of experiments, identifies points of commonality and disagreement among the different perspectives within sociology, and elaborates on the rationales of each. It helps experimental sociologists find appropriate designs for answering specific research questions while alerting them to the challenges. Offering more than just a guide, this book explores both the historical roots of experimental sociology and the cutting-edge techniques of rigorous sociology. It concludes with a tantalizing peek into the future and provides a roadmap to the exciting prospects and uncharted territories of experimental sociology.

Ethics in Econometrics
  • A Guide to Research Practice
  • Philip Hans Franses
  • Published online:
    14 November 2024
    Print publication:
    28 November 2024
  • Applied econometrics uses the tools of theoretical econometrics and real-word data to develop predictive models and assess economic theories. Due to the complex nature of such analysis, various assumptions are often not understood by those people who rely on it. The danger of this is that economic policies can be assessed favourably to suit a particular political agenda and forecasts can be generated to match the needs of a particular customer. Ethics in Econometrics argues that econometricians need to be aware of potential ethical pitfalls when carrying out their analysis and that they need to be encouraged to avoid them. Using a range of empirical examples and detailed discussions of real cases, this book provides a guide for research practices in econometrics, illustrating why it is imperative that econometricians act ethically in terms of the way they conduct their analysis and treat their data.

Introduction to Catastrophe Risk Modelling
  • A Physics-based Approach
  • Arnaud Mignan
  • Published online:
    08 November 2024
    Print publication:
    21 November 2024
  • Focusing on the physics of the catastrophe process and addressed directly to advanced students, this innovative textbook quantifies dozens of perils, both natural and man-made, and covers the latest developments in catastrophe modelling. Combining basic statistics, applied physics, natural and environmental sciences, civil engineering, and psychology, the text remains at an introductory level, focusing on fundamental concepts for a comprehensive understanding of catastrophe phenomenology and risk quantification. A broad spectrum of perils are covered, including geophysical, hydrological, meteorological, climatological, biological, extraterrestrial, technological and socio-economic, as well as events caused by domino effects and global warming. Following industry standards, the text provides the necessary tools to develop a CAT model from hazard to loss assessment. Online resources include a CAT risk model starter-kit and a CAT risk modelling 'sandbox' with Python Jupyter tutorial. Every process, described by equations, (pseudo)codes and illustrations, is fully reproducible, allowing students to solidify knowledge through practice.

Machine Learning Evaluation
  • Towards Reliable and Responsible AI
  • Nathalie Japkowicz, Zois Boukouvalas
  • Published online:
    07 November 2024
    Print publication:
    21 November 2024
  • As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers and blogs to provide a centralized resource that is accessible to students, practitioners, and researchers across the sciences. The book examines meaningful metrics for diverse types of learning paradigms and applications, unbiased estimation methods, rigorous statistical analysis, fair training sets, and meaningful explainability, all of which are essential to building robust and reliable machine learning products. In addition to standard classification, the book discusses unsupervised learning, regression, image segmentation, and anomaly detection. The book also covers topics such as industry-strength evaluation, fairness, and responsible AI. Implementations using Python and scikit-learn are available on the book's website.

Probability Theory, An Analytic View
  • 3rd edition
  • Daniel W. Stroock
  • Published online:
    07 November 2024
    Print publication:
    21 November 2024
  • The third edition of this highly regarded text provides a rigorous, yet entertaining, introduction to probability theory and the analytic ideas and tools on which the modern theory relies. The main changes are the inclusion of the Gaussian isoperimetric inequality plus many improvements and clarifications throughout the text. With more than 750 exercises, it is ideal for first-year graduate students with a good grasp of undergraduate probability theory and analysis. Starting with results about independent random variables, the author introduces weak convergence of measures and its application to the central limit theorem, and infinitely divisible laws and their associated stochastic processes. Conditional expectation and martingales follow before the context shifts to infinite dimensions, where Gaussian measures and weak convergence of measures are studied. The remainder is devoted to the mutually beneficial connection between probability theory and partial differential equations, culminating in an explanation of the relationship of Brownian motion to classical potential theory.

Polls, Pollsters, and Public Opinion
  • A Guide for Decision-Makers
  • Clifford Young, Kathryn Ziemer
  • Published online:
    01 November 2024
    Print publication:
    04 July 2024
  • A vast literature exists on theories of public opinion - how to measure, analyze, predict, and influence it; however, there is no synthesis of best practices for interpreting public opinion: existing knowledge is disparate and spread across many disciplines. Polls, Pollsters, and Public Opinion presents a systematic analytical approach for understanding, predicting, and engaging public opinion. It tells the story through the eyes of the pollster and draws an analytical road map for examining public opinion, both conceptually and practically. Providing a theoretical and conceptual foundation, as well as debunking popular myths, this book delves into the science of polling, offering tools analysts can use to assess the quality of polls. It also introduces methods that can be used to predict elections and other socio-political outcomes while understanding the nuances of messaging, engaging, and moving public opinion.

Essential Epidemiology
  • An Introduction for Students and Health Professionals
  • 5th edition
  • Penelope Webb, Chris Bain, Andrew Page
  • Published online:
    27 September 2024
    Print publication:
    12 November 2024
  • Epidemiology is fundamental to public health, providing the tools required to detect and quantify health problems and identify and evaluate solutions. Essential Epidemiology is a clear, engaging and methodological introduction to the subject. Now in its fifth edition, the text has been thoroughly updated. Its trademark clear and consistent pedagogical structure makes challenging topics accessible, while the local and international examples, including from the COVID-19 pandemic, encourage students to apply theory to real-world cases. Statistical analysis is explained simply, with more challenging concepts presented in optional advanced boxes. Each chapter includes information boxes, margin notes highlighting supplementary facts and question prompts to enhance learners' understanding. The end-of-chapter questions and accompanying guided solutions promote the consolidation of knowledge. Written by leading Australian academics and researchers, Essential Epidemiology remains a fundamental resource and reference text for students and public health practitioners alike.

Multivariate Biomarker Discovery
  • Data Science Methods for Efficient Analysis of High-Dimensional Biomedical Data
  • Darius M. Dziuda
  • Published online:
    30 May 2024
    Print publication:
    06 June 2024
  • Multivariate biomarker discovery is increasingly important in the realm of biomedical research, and is poised to become a crucial facet of personalized medicine. This will prompt the demand for a myriad of novel biomarkers representing distinct 'omic' biosignatures, allowing selection and tailoring treatments to the various individual characteristics of a particular patient. This concise and self-contained book covers all aspects of predictive modeling for biomarker discovery based on high-dimensional data, as well as modern data science methods for identification of parsimonious and robust multivariate biomarkers for medical diagnosis, prognosis, and personalized medicine. It provides a detailed description of state-of-the-art methods for parallel multivariate feature selection and supervised learning algorithms for regression and classification, as well as methods for proper validation of multivariate biomarkers and predictive models implementing them. This is an invaluable resource for scientists and students interested in bioinformatics, data science, and related areas.

Harmonic Functions and Random Walks on Groups
  • Ariel Yadin
  • Published online:
    16 May 2024
    Print publication:
    23 May 2024
  • Research in recent years has highlighted the deep connections between the algebraic, geometric, and analytic structures of a discrete group. New methods and ideas have resulted in an exciting field, with many opportunities for new researchers. This book is an introduction to the area from a modern vantage point. It incorporates the main basics, such as Kesten's amenability criterion, Coulhon and Saloff-Coste inequality, random walk entropy and bounded harmonic functions, the Choquet–Deny Theorem, the Milnor–Wolf Theorem, and a complete proof of Gromov's Theorem on polynomial growth groups. The book is especially appropriate for young researchers, and those new to the field, accessible even to graduate students. An abundance of examples, exercises, and solutions encourage self-reflection and the internalization of the concepts introduced. The author also points to open problems and possibilities for further research.

A Practical Guide to Data Analysis Using R
  • An Example-Based Approach
  • John H. Maindonald, W. John Braun, Jeffrey L. Andrews
  • Published online:
    11 May 2024
    Print publication:
    30 May 2024
  • Using diverse real-world examples, this text examines what models used for data analysis mean in a specific research context. What assumptions underlie analyses, and how can you check them? Building on the successful 'Data Analysis and Graphics Using R,' 3rd edition (Cambridge, 2010), it expands upon topics including cluster analysis, exponential time series, matching, seasonality, and resampling approaches. An extended look at p-values leads to an exploration of replicability issues and of contexts where numerous p-values exist, including gene expression.Developing practical intuition, this book assists scientists in the analysis of their own data, and familiarizes students in statistical theory with practical data analysis. The worked examples and accompanying commentary teach readers to recognize when a method works and, more importantly, when it doesn't. Each chapter contains copious exercises. Selected solutions, notes, slides, and R code are available online, with extensive references pointing to detailed guides to R.

Groups and Graphs, Designs and Dynamics
  • Edited by R. A. Bailey, Peter J. Cameron, Yaokun Wu
  • Published online:
    11 May 2024
    Print publication:
    30 May 2024
  • This collection of four short courses looks at group representations, graph spectra, statistical optimality, and symbolic dynamics, highlighting their common roots in linear algebra. It leads students from the very beginnings in linear algebra to high-level applications: representations of finite groups, leading to probability models and harmonic analysis; eigenvalues of growing graphs from quantum probability techniques; statistical optimality of designs from Laplacian eigenvalues of graphs; and symbolic dynamics, applying matrix stability and K-theory. An invaluable resource for researchers and beginning Ph.D. students, this book includes copious exercises, notes, and references.