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53140 results in Statistics and Probability

Page 1 of 2657

Statistics without Calculations
  • A Guide for Qualitative Researchers in the Social Sciences
  • Colin Foster
  • Coming soon
  • Expected online publication date:
    March 2027
    Print publication:
    31 March 2027

Analysing Experiments with Mixed-Effects Models
  • Johannes Forkman
  • Coming soon
  • Expected online publication date:
    February 2027
    Print publication:
    28 February 2027
  • Focusing on mixed-effects models, this book offers a comprehensive guide to analysing experiments across diverse fields, including behavioural, agricultural, and medical sciences. The text opens with a traditional analysis of variance and then ranges from linear fixed-effects models to generalised linear mixed-effects models. It covers the most common experimental designs, such as factorial, hierarchical, between-subject, within-subject, cross-over, two-factor mixed, and split-plot designs, before studying analysis of covariance, models with group-specific error variances and models for repeated-measures analysis. Frequently drawing on real-life experiments, the book offers 69 examples and 134 exercises. Readers are supported with digital supplements, comprising the solutions to exercises, the datasets and R code and SAS code for all examples requiring software computation. This is an essential resource for students, practitioners conducting experiments and applied statisticians wishing to use mixed-effects models for the analysis of experiments.

A Guide to First-Passage Processes: A Second Look
  • 2nd edition
  • Sidney Redner
  • Coming soon
  • Expected online publication date:
    January 2027
    Print publication:
    31 January 2027
  • This second edition provides a comprehensive and unified presentation of the theory and applications of first-passage processes. It begins by developing the phenomenology from basic principles and illustrating it via the first-passage properties of the interval, the semi-infinite line, spherical geometries, unbounded domains, and bounded two-dimensional domains. Dedicated sections describe first-passage processes in many types of systems, including complete and nearly complete graphs, the Erdős-Renyi graph, the Cayley tree, and hierarchical networks. The new edition has been substantially revised and expanded to include new sections on the martingale method, heterogeneity, and other important concepts. A wide range of new applications are discussed, such as queueing, birth-death reactions, the Polya urn, resetting, chemoreception, search processes, and the 'hot hand' paradox. The book's accessible style makes it a valuable resource for researchers and students in statistical and mathematical physics, chemistry, engineering, and all other fields in which first-passage problems arise.

Machine Learning for Medical and Epidemiological Research

Practical Time Series Data Analysis
  • Lessons from Time-Domain Astronomy
  • Jeffrey Scargle
  • Coming soon
  • Expected online publication date:
    December 2026
    Print publication:
    31 December 2026
  • This practical guide demonstrates the use of methods to analyze sequential data, from basic standard methods to advanced novel techniques, all in the setting of an accessible, intuitive view of underlying statistical theory. The book reveals the unappreciated limitations of standard methods and shows how simple new viewpoints can overcome these obstacles and open up novel opportunities for discovery. Readers, from beginning students of astronomy, physics, statistics, and other technical subjects, to seasoned practitioners in these fields, are invited to use thought-provoking exercises to delve deeper into important topics without resorting to mindless calculations. Several case studies are included – not only to point out the end results, but to illustrate how the scientific process is actually carried out in practice. Scargle, a well-known pioneer in the field, shares his decades of experience to demonstrate improvements and extensions of classical techniques and discourage uncritical use of 'black box' analyses.

Inverse Theory
  • The Art of Scientific Inference
  • Andreas Fichtner
  • Coming soon
  • Expected online publication date:
    December 2026
    Print publication:
    31 December 2026
  • How can we draw reliable conclusions from limited and imperfect data? This textbook offers a clear and accessible guide to the principles behind scientific inference, showing how a unifying framework connects fields as diverse as Earth science, medical imaging, non-destructive testing, meteorology, climate research, and machine learning. It presents both classical and modern methods for solving real-world inference problems, with practical guidance on evaluating the reliability of results and understanding their uncertainties. Designed as both a learning resource and a long-term reference, the book balances depth with clarity. Hands-on computational exercises throughout help readers translate ideas into practice, strengthen their intuition and build confidence in tackling their own data challenges. It is ideal for advanced undergraduate and postgraduate students, as well as researchers and professionals, across many disciplines, from environmental science and medical imaging to climate research, machine learning, and economics.

Emergence and Phase Transitions in Uniform Random Graphs
  • Charles Radin, Lorenzo Sadun
  • Coming soon
  • Expected online publication date:
    November 2026
    Print publication:
    30 November 2026
  • This book studies the emergence of large-scale structure from small structures in the context of random graphs. Typical large graphs with fixed edge density e and triangle density t are described by a 'graphon' that solves a constrained optimization problem. Proofs are provided of the existence of infinitely many open sets ('phases') in the (e,t) plane where the optimal graphon is unique and varies analytically with (e,t). The optimal graphons take a simple form, with symmetries that vary from phase to phase, indicating an emergent self-organization of the corresponding graphs. Besides being of independent interest in the theory of random graphs, extremal combinatorics and the calculus of variations, this provides a rigorous framework for studying ideas from statistical physics that have never been proven in their original setting. The techniques presented in this book can serve as a guide for optimization problems in other fields.

Game-Theoretic Models of International Crisis Bargaining
  • Ahmer Tarar
  • Coming soon
  • Expected online publication date:
    October 2026
    Print publication:
    31 October 2026
  • In recent decades, game theory has been extensively used in academic research throughout the social sciences, including international relations. The typical format of applied game theoretic journal articles is theorem-proof, but while the proof demonstrates that the theorem is true, it doesn't typically show how the researcher actually “discovered” the theorem. Ahmer Tarar's Game-Theoretic Models of International Crisis Bargaining explains how to derive the equilibria of (sometimes complicated) game-theoretic models. Examining central results on international crisis bargaining, using a unified modeling framework, he presents simplified versions of important published game-theoretic models in international conflict to demonstrate how to construct and solve game-theoretic models for academic research. He provides detailed derivations for each result, presenting a proposition summarizing the entire equilibrium strategy profile. With over 300 exercises, ranging from easy to difficult, Tarar provides readers with extensive practice for honing their skills and becoming skilled modelers.

The Evolutionary Foundations of Preferences
  • Arthur J. Robson, Larry Samuelson
  • Coming soon
  • Expected online publication date:
    September 2026
    Print publication:
    30 September 2026
  • Preferences are the point of departure for economic analysis. Despite myriad experiments designed to characterize preferences, no consensus has been reached. In The Evolutionary Foundation of Preferences, Arthur J. Robson and Larry Samuelson examine how economic preferences might be shaped by biological evolution. They theorize that each of us is descended from a line of ancestors who were able to survive and reproduce, and they analyze how this may have affected modern preferences. Drawing on demographic models, they explain how different preferences induce different behaviors which lead to different growth rates among respective subpopulations. People whose preferences induce the highest growth rate eventually comprise the overwhelming proportion of the population. Examining neuroscientific evidence that points to a cardinal, or hedonic, interpretation of utility, the authors discuss the implications of these interpretations and the challenges raised for welfare economics.

Markov Decision Processes and Reinforcement Learning
  • Martin L. Puterman, Timothy C. Y. Chan
  • Coming soon
  • Expected online publication date:
    September 2026
    Print publication:
    30 September 2026
  • This book offers a comprehensive introduction to Markov decision process and reinforcement learning fundamentals using common mathematical notation and language. Its goal is to provide a solid foundation that enables readers to engage meaningfully with these rapidly evolving fields. Topics covered include finite and infinite horizon models, partially observable models, value function approximation, simulation-based methods, Monte Carlo methods, and Q-learning. Rigorous mathematical concepts and algorithmic developments are supported by numerous worked examples. As an up-to-date successor to Martin L. Puterman's influential 1994 textbook, this volume assumes familiarity with probability, mathematical notation, and proof techniques. It is ideally suited for students, researchers, and professionals in operations research, computer science, engineering, and economics.

Advanced Analytics for Finance
  • Theory and Empirics Using Big Data
  • Rajeev R. Bhattacharya
  • Coming soon
  • Expected online publication date:
    September 2026
    Print publication:
    30 September 2026
  • Businesses are increasingly leveraging big data in financial analysis to improve decision-making, risk management, and market competitiveness, and professionals who know how to apply this data are in high demand. Designed for graduate programs and advanced undergraduate studies, this text synthesizes traditional statistics and econometrics with contemporary artificial intelligence and machine learning methods, preparing readers for the realities of modern-day financial data analysis. It studies known unknowns versus unknown unknowns and provides a systematic and objective characterization of statistical versus actual significance. Applying advanced theoretical and empirical methods to massive high-frequency databases, the book explores market microstructure, risk, market efficiency, equities, fixed income securities, and options. Grounded in over three decades of research, consulting, management, and teaching experience, it serves as a comprehensive and practical resource for students, practitioners, and scholars in capital markets, advanced analytics, and litigation.

Healthcare Quality Measurement
  • Volume 1, History and Practice of Healthcare Quality Measurement
  • Sharon-Lise T. Normand, David M. Shahian
  • Coming soon
  • Expected online publication date:
    August 2026
    Print publication:
    31 August 2026
  • This comprehensive guide presents a data science approach to healthcare quality measurement and provider profiling for policymakers, regulators, hospital quality leaders, clinicians, and researchers. Two volumes encompass basic and advanced statistical techniques and diverse practical applications. Volume 1 begins with a historical review followed by core concepts including measure types and attributes (bias, validity, reliability, power, sample size); data sources; target conditions and procedures; patient and provider observation periods; attribution level; risk modeling; social risk factors; outlier classification; data presentation; public reporting; and graphical approaches. Volume 2 introduces causal inference for provider profiling, focusing on hierarchical regression models. These models appropriately partition systematic and random variation in observations, accounting for within-provider clustering. Item Response Theory models are introduced for linking multiple categorical quality metrics to underlying quality constructs. Computational strategies are discussed, followed by various approaches to inference. Finally, methods to assess and compare model fit are presented.

Healthcare Quality Measurement
  • Volume 2, Statistical Methodology
  • Sharon-Lise T. Normand, David M. Shahian
  • Coming soon
  • Expected online publication date:
    August 2026
    Print publication:
    31 August 2026
  • This comprehensive guide presents a data science approach to healthcare quality measurement and provider profiling for policymakers, regulators, hospital quality leaders, clinicians, and researchers. Two volumes encompass basic and advanced statistical techniques and diverse practical applications. Volume 1 begins with a historical review followed by core concepts including measure types and attributes (bias, validity, reliability, power, sample size); data sources; target conditions and procedures; patient and provider observation periods; attribution level; risk modeling; social risk factors; outlier classification; data presentation; public reporting; and graphical approaches. Volume 2 introduces causal inference for provider profiling, focusing on hierarchical regression models. These models appropriately partition systematic and random variation in observations, accounting for within-provider clustering. Item Response Theory models are introduced for linking multiple categorical quality metrics to underlying quality constructs. Computational strategies are discussed, followed by various approaches to inference. Finally, methods to assess and compare model fit are presented.

  • 22 - Solutions to Questions

  • Paul Sweeting, University of Kent, Canterbury
  • Book:
    Financial Enterprise Risk Management
    Print publication:
    13 August 2026, pp 639-659

  • Summary

    This comprehensive yet accessible guide to enterprise risk management (ERM) for financial institutions contains all the tools needed to build and maintain an ERM framework. It discusses the internal and external contexts within which risk management must be carried out, and it covers a range of qualitative and quantitative techniques that can be used to identify, model and measure risks. This third edition has been thoroughly revised and updated to reflect new regulations and legislation. It includes additional detail on machine learning, a new section on vine copulas and significantly expanded information on sustainability. A range of new case studies includes Theranos and FTX.

    Suitable as a course book or for self-study, this book forms part of the core reading for the Institute and Faculty of Actuaries’ examination in ERM.

Page 1 of 2657