A comprehensive introduction to bootstrap methods in the R programming environment Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the practicality of this approach and successfully utilizes R to illustrate applications for the bootstrap and other resampling methods. This book provides a modern introduction to bootstrap methods for readers who do not have an extensive background in advanced mathematics. Emphasis throughout is on the use of bootstrap methods as an exploratory tool, including its value in variable selection and other modeling environments. The authors begin with a description of bootstrap methods and its relationship to other resampling methods, along with an overview of the wide variety of applications of the approach. Subsequent chapters offer coverage of improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems, including pharmaceutical, genomics, and economics. To inform readers on the limitations of the method, the book also exhibits counterexamples to the consistency of bootstrap methods. An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented throughout the book. A related website houses the book's R subroutines, and an extensive listing of references provides resources for further study. Discussing the topic at a remarkably practical and accessible level, An Introduction to Bootstrap Methods with Applications to R is an excellent book for introductory courses on bootstrap and resampling methods at the upper-undergraduate and graduate levels. It also serves as an insightful reference for practitioners working with data in engineering, medicine, and the social sciences who would like to acquire a basic understanding of bootstrap methods.
A practical and accessible introduction to the bootstrap method——newly revised and updated Over the past decade, the application of bootstrap methods to new areas of study has expanded, resulting in theoretical and applied advances across various fields. Bootstrap Methods, Second Edition is a highly approachable guide to the multidisciplinary, real-world uses of bootstrapping and is ideal for readers who have a professional interest in its methods, but are without an advanced background in mathematics. Updated to reflect current techniques and the most up-to-date work on the topic, the Second Edition features: The addition of a second, extended bibliography devoted solely to publications from 1999–2007, which is a valuable collection of references on the latest research in the field A discussion of the new areas of applicability for bootstrap methods, including use in the pharmaceutical industry for estimating individual and population bioequivalence in clinical trials A revised chapter on when and why bootstrap fails and remedies for overcoming these drawbacks Added coverage on regression, censored data applications, P-value adjustment, ratio estimators, and missing data New examples and illustrations as well as extensive historical notes at the end of each chapter With a strong focus on application, detailed explanations of methodology, and complete coverage of modern developments in the field, Bootstrap Methods, Second Edition is an indispensable reference for applied statisticians, engineers, scientists, clinicians, and other practitioners who regularly use statistical methods in research. It is also suitable as a supplementary text for courses in statistics and resampling methods at the upper-undergraduate and graduate levels.
The definitive text in its field, McGlamry's Comprehensive Textbook of Foot and Ankle Surgery, is the ideal reference for the podiatric or orthopedic surgeon, resident, or student preparing for certification exams. From perioperative management to postoperative complications and considerations, this must-have resource prepares you for a full range of podiatric surgeries and procedures ranging from routine trauma of the foot and leg to compound deformities, enabling you to face any challenge with confidence. This is the tablet version of McGlamry's Comprehensive Textbook of Foot and Ankle Surgery which does not include access to the supplemental content mentioned in the text.
An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data. The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models. Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models. Multivariate techniques, including classification and ordination, are then introduced. Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results. The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature. The book is supported by a website that provides all data sets, questions for each chapter and links to software.
Claims reserving is central to the insurance industry. Insurance liabilities depend on a number of different risk factors which need to be predicted accurately. This prediction of risk factors and outstanding loss liabilities is the core for pricing insurance products, determining the profitability of an insurance company and for considering the financial strength (solvency) of the company. Following several high-profile company insolvencies, regulatory requirements have moved towards a risk-adjusted basis which has lead to the Solvency II developments. The key focus in the new regime is that financial companies need to analyze adverse developments in their portfolios. Reserving actuaries now have to not only estimate reserves for the outstanding loss liabilities but also to quantify possible shortfalls in these reserves that may lead to potential losses. Such an analysis requires stochastic modeling of loss liability cash flows and it can only be done within a stochastic framework. Therefore stochastic loss liability modeling and quantifying prediction uncertainties has become standard under the new legal framework for the financial industry. This book covers all the mathematical theory and practical guidance needed in order to adhere to these stochastic techniques. Starting with the basic mathematical methods, working right through to the latest developments relevant for practical applications; readers will find out how to estimate total claims reserves while at the same time predicting errors and uncertainty are quantified. Accompanying datasets demonstrate all the techniques, which are easily implemented in a spreadsheet. A practical and essential guide, this book is a must-read in the light of the new solvency requirements for the whole insurance industry.
This is the first full-length biography of Judah Leib Gordon (1830-92), the most important Hebrew poet of the 19th century, and one of the pivotal intellectual and cultural figures in Russian Jewry. Setting Gordon's life and work amidst the political, cultural, and religious upheavals of his society, Stanislawski attempts to counter traditional stereotypical readings of Eastern European Jewish history. As a prominent and passionate exponent of the Jewish Enlightenment in Russia, Gordon advocated a humanist and liberal approach to all the major questions facing Jews in their tortuous transition to modernity--the religious reform of Judaism, the attractions and limits of political liberalism, the relations between Jews and Gentiles, the nature of modern anti-Semitism, the status of women in Jewish life, the possibility of a secular Jewish culture, the nature of Zionism, and the relations between Jews in the Diaspora and the Jewish community in the Land of Israel. His personal story is a fascinating drama that both symbolizes and summarizes the cultural and political challenges facing Russian Jewry at a crucial time in its history, challenges that remain pertinent and controversial today.
An initial course in scientific data analysis and hypothesis testing designed for students in all science, technology, engineering, and mathematics disciplines Data Analysis for the Geosciences: Essentials of Uncertainty, Comparison, and Visualization is a textbook for upper-level undergraduate STEM students, designed to be their statistics course in a degree program. This volume provides a comprehensive introduction to data analysis, visualization, and data-model comparisons and metrics, within the framework of the uncertainty around the values. It offers a learning experience based on real data from the Earth, ocean, atmospheric, space, and planetary sciences. About this volume: Serves as an initial course in scientific data analysis and hypothesis testing Focuses on the methods of data processing Introduces a wide range of analysis techniques Describes the many ways to compare data with models Centers on applications rather than derivations Explains how to select appropriate statistics for meaningful decisions Explores the importance of the concept of uncertainty Uses examples from real geoscience observations Homework problems at the end of chapters The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
Since Efron's profound paper on the bootstrap, an enormous amount of effort has been spent on the development of bootstrap, jacknife, and other resampling methods. The primary goal of these computer-intensive methods has been to provide statistical tools that work in complex situations without imposing unrealistic or unverifiable assumptions about the data generating mechanism. This book sets out to lay some of the foundations for subsampling methodology and related methods.
This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.
Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures
This is the first major study based on Soviet documents and revelations of the Soviet state security during the period 1939-1953—a period about which relatively little is known. The book documents the role of Stalin and the major players in massive crimes carried out during this period against the Soviet people. It also provides the first detailed biography of V. S. Abakumov, Minister of State Security, 1946-1951. Based on Glasnost revelations and recently released archival material, this study covers the operations of Soviet state security from Beriia's appointment in 1938 until Stalin's death. The book pays particular attention to the career of V. S. Abakumov, head of SMERSH counterintelligence during the war and minister in charge of the MGB (the predecessor of the KGB) from 1946 until his removal and arrest in July 1951. The author argues that terror remained the central feature of Stalin's rule even after the Great Terror and he provides examples of how he micromanaged the repressions. The book catalogs the major crimes committed by the security organs and the leading perpetrators and provides evidence that the crimes were similar to those for which the Nazi leaders were punished after the war. Subjects covered include Katyn and its aftermath, the arrest and execution of senior military officers, the killing of political prisoners near Orel in September 1941, and the deportations of various nationalities during the war. The post-war period saw the Aviator and Leningrad affairs as well as the anti-cosmopolitan campaign whose target was mainly Jewish intellectuals. Later chapters cover Abakumov's downfall, the hatching of the Mingrelian and Doctors plots and the events that followed Stalin's death. Finally, there are chapters on the fate of those who ran Stalin's machinery of terror in the last 13 years of his rule. These and other topics will be of concern to all students and scholars of Soviet history and those interested in secret police and intelligence operations.
Prophets of the Past is the first book to examine in depth how modern Jewish historians have interpreted Jewish history. Michael Brenner reveals that perhaps no other national or religious group has used their shared history for so many different ideological and political purposes as the Jews. He deftly traces the master narratives of Jewish history from the beginnings of the scholarly study of Jews and Judaism in nineteenth-century Germany; to eastern European approaches by Simon Dubnow, the interwar school of Polish-Jewish historians, and the short-lived efforts of Soviet-Jewish historians; to the work of British and American scholars such as Cecil Roth and Salo Baron; and to Zionist and post-Zionist interpretations of Jewish history. He also unravels the distortions of Jewish history writing, including antisemitic Nazi research into the "Jewish question," the Soviet portrayal of Jewish history as class struggle, and Orthodox Jewish interpretations of history as divinely inspired. History proved to be a uniquely powerful weapon for modern Jewish scholars during a period when they had no nation or army to fight for their ideological and political objectives, whether the goal was Jewish emancipation, diasporic autonomy, or the creation of a Jewish state. As Brenner demonstrates in this illuminating and incisive book, these historians often found legitimacy for these struggles in the Jewish past.
“Much of what we experience in life results from a combination of skill and luck.” — From the Introduction The trick, of course, is figuring out just how many of our successes (and failures) can be attributed to each—and how we can learn to tell the difference ahead of time. In most domains of life, skill and luck seem hopelessly entangled. Different levels of skill and varying degrees of good and bad luck are the realities that shape our lives—yet few of us are adept at accurately distinguishing between the two. Imagine what we could accomplish if we were able to tease out these two threads, examine them, and use the resulting knowledge to make better decisions. In this provocative book, Michael Mauboussin helps to untangle these intricate strands to offer the structure needed to analyze the relative importance of skill and luck. He offers concrete suggestions for making these insights work to your advantage. Once we understand the extent to which skill and luck contribute to our achievements, we can learn to deal with them in making decisions. The Success Equation helps us move toward this goal by: • Establishing a foundation so we better understand skill and luck, and can pinpoint where each is most relevant • Helping us develop the analytical tools necessary to understand skill and luck • Offering concrete suggestions about how to take these findings and put them to work Showcasing Mauboussin’s trademark wit, insight, and analytical genius, The Success Equation is a must-read for anyone seeking to make better decisions—in business and in life.
In the year 2025 in an alternate reality on Earth 2 life is not only different in this alternate world but history has also happened very differently and will continue to move forward in a different path. In Virginia Beach, Virginia the city has just suffered a catastrophic terrorist attack launched by the Klu Klux Klan a terror group founded by terrorist mastermind James Earl Ray Jr a southern white man who holds a grudge against the now modernized liberal united states government that he hopes to overthrow and take down and it's up to one fearless hero who isn't afraid to play dirty to defeat terrorism. That hero is 40-year old FBI Agent Michael Blount a former police officer who's a divorced single father of two teenage girls and the son of a Russian Immigrant who went AWOL from the Russian Army to track down and take down this terrorist group once and for all with the help of his teammates. Not only will this be a mission to take this white supremacists terror group but this mission will also uncover so much Corruption and Treason within the United States Government the main enemy of this state would turn out to be a traitor who paved the way for a new enemy to come out of hiding. Agent Michael Blount who went from a high school jock growing up to now one of the most decorated FBI Agents in US History would also encounter a surprising blast from his past which would shake him to the core forcing him to dig deep and fight for the fate of the free world.
First the press became the media, and now the media have become the Imperial Media—or have they? In this timely and comprehensive analysis, Michael Robinson and Margaret Sheehan examine how the news media behaved (or misbehaved) in covering the 1980 presidential campaign. Using the media's own traditional standards as a guide, Robinson and Sheehan measure the level of objectivity, fairness, seriousness, and criticism displayed by CBS News and United Press International between January and December of 1980. Drawing on statistical analyses of almost 6,000 news stories and dozens of interviews with writers and reporters, the authors reach convincing and sometimes surprising conclusions. They demonstrate, for example, that both CBS and UPI strictly avoided subjective assessments of the candidates and their positions on the issues. Both gave the major parties remarkably equal access. But the media seem to give more negative coverage to front-runners, treating serious challengers less harshly. Perhaps the most surprising finding is that networks were not more superficial than print; CBS attended to the issues at least as often as UPI. Robinson and Sheehan find television coverage more subjective, more volatile, and substantially more negative than traditional print. But CBS behaved neither imperially nor irresponsibly in Campaign '80. The networks did, however, emulate the more highly charged journalism of the eastern elite print press. By blending the quantitative techniques of social science and the tools of Washington-based journalism, Robinson and Sheehan have produced a book that will be essential reading for students and practitioners of politics, public opinion research, journalism, and communications. Lively and readable, it should also appeal to anyone interested in the role of the news media in contemporary politics.
First-passage percolation (FPP) is a fundamental model in probability theory that has a wide range of applications to other scientific areas (growth and infection in biology, optimization in computer science, disordered media in physics), as well as other areas of mathematics, including analysis and geometry. FPP was introduced in the 1960s as a random metric space. Although it is simple to define, and despite years of work by leading researchers, many of its central problems remain unsolved. In this book, the authors describe the main results of FPP, with two purposes in mind. First, they give self-contained proofs of seminal results obtained until the 1990s on limit shapes and geodesics. Second, they discuss recent perspectives and directions including (1) tools from metric geometry, (2) applications of concentration of measure, and (3) related growth and competition models. The authors also provide a collection of old and new open questions. This book is intended as a textbook for a graduate course or as a learning tool for researchers.
Computational linguistics can be used to uncover mysteries in text which are not always obvious to visual inspection. For example, the computer analysis of writing style can show who might be the true author of a text in cases of disputed authorship or suspected plagiarism. The theoretical background to authorship attribution is presented in a step by step manner, and comprehensive reviews of the field are given in two specialist areas, the writings of William Shakespeare and his contemporaries, and the various writing styles seen in religious texts. The final chapter looks at the progress computers have made in the decipherment of lost languages. This book is written for students and researchers of general linguistics, computational and corpus linguistics, and computer forensics. It will inspire future researchers to study these topics for themselves, and gives sufficient details of the methods and resources to get them started.
Missing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described. Provides a practical guide to the analysis of clinical trials and related studies with missing data. Examines the problems caused by missing data, enabling a complete understanding of how to overcome them. Presents conventional, simple methods to tackle these problems, before addressing more advanced approaches, including sensitivity analysis, and the MAR missingness mechanism. Illustrated throughout with real-life case studies and worked examples from clinical trials. Details the use and implementation of the necessary statistical software, primarily SAS. Missing Data in Clinical Studies has been developed through a series of courses and lectures. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations. Graduate students of biostatistics will also find much of benefit.
Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians, epidemiologists, and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patient’s individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest. Features: All you need to know to correctly make an online risk calculator from scratch Discrimination, calibration, and predictive performance with censored data and competing risks R-code and illustrative examples Interpretation of prediction performance via benchmarks Comparison and combination of rival modeling strategies via cross-validation Thomas A. Gerds is a professor at the Biostatistics Unit at the University of Copenhagen and is affiliated with the Danish Heart Foundation. He is the author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years. Michael W. Kattan is a highly cited author and Chair of the Department of Quantitative Health Sciences at Cleveland Clinic. He is a Fellow of the American Statistical Association and has received two awards from the Society for Medical Decision Making: the Eugene L. Saenger Award for Distinguished Service, and the John M. Eisenberg Award for Practical Application of Medical Decision-Making Research.
Wireless Distributed Computing and Cognitive Sensing defines high-dimensional data processing in the context of wireless distributed computing and cognitive sensing. This book presents the challenges that are unique to this area such as synchronization caused by the high mobility of the nodes. The author will discuss the integration of software defined radio implementation and testbed development. The book will also bridge new research results and contextual reviews. Also the author provides an examination of large cognitive radio network; hardware testbed; distributed sensing; and distributed computing.
Introductory Biostatistics for the Health Sciences" ist eine fundierte Einführung in die Biostatistik und ihre Anwendungsgebiete. Der Band richtet sich vorwiegend an Mediziner und Statistiker. Theorie und Praxis stehen im ausgewogenen Verhältnis, d.h. praktische Anwendungen werden, wo nötig, durch den theoretischen Hintergrund ergänzt. Der Schwerpunkt liegt eindeutig auf der praktischen Anwendung. Der Band geht auch auf jüngste Fortschritte bei der Bootstrap-, Outlier- und Meta-Analyse ein, Themen, die in der Regel in Konkurrenzwerken, nicht behandelt werden. Mit einer Fülle von Übungsaufgaben. Auch Statistiksoftware wird ausführlich besprochen.
Now in its second edition, this book provides a focused, comprehensive overview of both categorical and nonparametric statistics, offering a conceptual framework for choosing the most appropriate test in various scenarios. The book’s clear explanations and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of these techniques. Basic statistics and probability are reviewed for those needing a refresher with mathematical derivations placed in optional appendices. Highlights include the following: • Three chapters co-authored with Edgar Brunner address modern nonparametric techniques, along with accompanying R code. • Unique coverage of both categorical and nonparametric statistics better prepares readers to select the best technique for particular research projects. • Designed to be used with most statistical packages, clear examples of how to use the tests in SPSS, R, and Excel foster conceptual understanding. • Exploring the Concept boxes integrated throughout prompt students to draw links between the concepts to deepen understanding. • Fully developed Instructor and Student Resources featuring datasets for the book's problems and a guide to R, and for the instructor PowerPoints, author's syllabus, and answers to even-numbered problems. Intended for graduate or advanced undergraduate courses in categorical and nonparametric statistics taught in psychology, education, human development, sociology, political science, and other social and life sciences.
This book emphasizes the importance of the likelihood function in statistical theory and applications and discusses it in the context of biology and ecology. Bayesian and frequentist methods both use the likelihood function and provide differing but related insights. This is examined here both through review of basic methodology and also the integr
A comprehensive account of the statistical theory of exponential families of stochastic processes. The book reviews the progress in the field made over the last ten years or so by the authors - two of the leading experts in the field - and several other researchers. The theory is applied to a broad spectrum of examples, covering a large number of frequently applied stochastic process models with discrete as well as continuous time. To make the reading even easier for statisticians with only a basic background in the theory of stochastic process, the first part of the book is based on classical theory of stochastic processes only, while stochastic calculus is used later. Most of the concepts and tools from stochastic calculus needed when working with inference for stochastic processes are introduced and explained without proof in an appendix. This appendix can also be used independently as an introduction to stochastic calculus for statisticians. Numerous exercises are also included.
Detection Theory: A User’s Guide is an introduction to one of the most important tools for the analysis of data where choices must be made and performance is not perfect. In these cases, detection theory can transform judgments about subjective experiences, such as perceptions and memories, into quantitative data ready for analysis and modeling. For beginners, the first three chapters introduce measuring detection and discrimination, evaluating decision criteria, and the utility of receiver operating characteristics. Later chapters cover more advanced research paradigms, including: complete tools for application, including flowcharts, tables, and software; student-friendly language; complete coverage of content area, including both one-dimensional and multidimensional models; integrated treatment of threshold and nonparametric approaches; an organized, tutorial level introduction to multidimensional detection theory; and popular discrimination paradigms presented as applications of multidimensional detection theory. This modern summary of signal detection theory is both a self-contained reference work for users and a readable text for graduate students and researchers learning the material either in courses or on their own.
Remorse is a powerful, important and yet academically neglected emotion. This book, one of the very few extended examinations of remorse, draws on psychology, law and philosophy to present a unique interdisciplinary study of this intriguing emotion. The psychological chapters examine the fundamental nature of remorse, its interpersonal effects, and its relationship with regret, guilt and shame. A practical focus is also provided in an examination of the place of remorse in psychotherapeutic interventions with criminal offenders. The book's jurisprudential chapters explore the problem of how offender remorse is proved in court and the contentious issues concerning the effect that remorse - and its absence - should have on sentencing criminal offenders. The legal and psychological perspectives are then interwoven in a discussion of the role of remorse in restorative justice. In Remorse: Psychological and Jurisprudential Perspectives, Proeve and Tudor bring together insights of neighbouring disciplines to advance our understanding of remorse. It will be of interest to theoreticians in psychology, law and philosophy, and will be of benefit to practising psychologists and lawyers.
Understand the benefits of robust statistics for signal processing with this authoritative yet accessible text. The first ever book on the subject, it provides a comprehensive overview of the field, moving from fundamental theory through to important new results and recent advances. Topics covered include advanced robust methods for complex-valued data, robust covariance estimation, penalized regression models, dependent data, robust bootstrap, and tensors. Robustness issues are illustrated throughout using real-world examples and key algorithms are included in a MATLAB Robust Signal Processing Toolbox accompanying the book online, allowing the methods discussed to be easily applied and adapted to multiple practical situations. This unique resource provides a powerful tool for researchers and practitioners working in the field of signal processing.
Praise for the Second Edition: "The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter... [He] has taken great care to select examples that are interesting and practical for computer scientists. ... The content is illustrated with numerous figures, and concludes with appendices and an index. The book is erudite and ... could work well as a required text for an advanced undergraduate or graduate course." ---Computing Reviews Probability and Statistics for Computer Scientists, Third Edition helps students understand fundamental concepts of Probability and Statistics, general methods of stochastic modeling, simulation, queuing, and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB, this classroom-tested book can be used for one- or two-semester courses. Features: Axiomatic introduction of probability Expanded coverage of statistical inference and data analysis, including estimation and testing, Bayesian approach, multivariate regression, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrap Numerous motivating examples and exercises including computer projects Fully annotated R codes in parallel to MATLAB Applications in computer science, software engineering, telecommunications, and related areas In-Depth yet Accessible Treatment of Computer Science-Related Topics Starting with the fundamentals of probability, the text takes students through topics heavily featured in modern computer science, computer engineering, software engineering, and associated fields, such as computer simulations, Monte Carlo methods, stochastic processes, Markov chains, queuing theory, statistical inference, and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET). About the Author Michael Baron is David Carroll Professor of Mathematics and Statistics at American University in Washington D. C. He conducts research in sequential analysis and optimal stopping, change-point detection, Bayesian inference, and applications of statistics in epidemiology, clinical trials, semiconductor manufacturing, and other fields. M. Baron is a Fellow of the American Statistical Association and a recipient of the Abraham Wald Prize for the best paper in Sequential Analysis and the Regents Outstanding Teaching Award. M. Baron holds a Ph.D. in statistics from the University of Maryland. In his turn, he supervised twelve doctoral students, mostly employed on academic and research positions.
Every one of us have watched television shows, movies and listened to our favorite songs but how many of us have wondered how theyve affected and influenced us? Do we still have a fondness for the mediums we enjoyed as a child or do we outgrow the past? As an adult, is it easier or harder to accept the past or embrace the future?
Bayesian ideas have recently been applied across such diverse fields as philosophy, statistics, economics, psychology, artificial intelligence, and legal theory. Fundamentals of Bayesian Epistemology examines epistemologists' use of Bayesian probability mathematics to represent degrees of belief. Michael G. Titelbaum provides an accessible introduction to the key concepts and principles of the Bayesian formalism, enabling the reader both to follow epistemological debates and to see broader implications Volume 1 begins by motivating the use of degrees of belief in epistemology. It then introduces, explains, and applies the five core Bayesian normative rules: Kolmogorov's three probability axioms, the Ratio Formula for conditional degrees of belief, and Conditionalization for updating attitudes over time. Finally, it discusses further normative rules (such as the Principal Principle, or indifference principles) that have been proposed to supplement or replace the core five. Volume 2 gives arguments for the five core rules introduced in Volume 1, then considers challenges to Bayesian epistemology. It begins by detailing Bayesianism's successful applications to confirmation and decision theory. Then it describes three types of arguments for Bayesian rules, based on representation theorems, Dutch Books, and accuracy measures. Finally, it takes on objections to the Bayesian approach and alternative formalisms, including the statistical approaches of frequentism and likelihoodism.
For twenty years, Reverse Shot, a journal for film criticism and the house publication of New York’s Museum of the Moving Image, has been a home for movie lovers to find incisive, intelligent writings from a diverse group of the best critics working today. To celebrate the publication's run, MoMI has published this special anniversary anthology, which collects central pieces from the journal’s beginnings up through the latest releases. Broken into four chronological movements, this volume captures not only the films and filmmakers that Reverse Shot’s writers have championed and wrestled over but also tells a story of cinema’s progress and change over the first two decades of the 21st century. More than just for the many longtime readers of Reverse Shot, this collection is an essential reference for the past, present, and future of the moving image and a gift for anyone who cares about films and serious writings about them. “This New York-based publication has remained not only a beacon for quality film writing but also, in so many cases, the domain for the internet’s best piece on a given film. ... Digging into the earliest writings here affirms a site quickly setting an Olympian standard for online movie analysis, pole-vaulting even over many esteemed print publications with less space to play with on the page ... Any one essay gives you a taste of the levels of insight routinely put to bear by its shifting stable of contributors, including Nick Pinkerton, Genevieve Yue, Eric Hynes and Devika Girish.” —Sight & Sound magazine, March 2024
Analysis and Management of Animal Populations deals with the processes involved in making informed decisions about the management of animal populations. It covers the modeling of population responses to management actions, the estimation of quantities needed in the modeling effort, and the application of these estimates and models to the development of sound management decisions. The book synthesizes and integrates in a single volume the methods associated with these themes, as they apply to ecological assessment and conservation of animal populations. Integrates population modeling, parameter estimation and decision-theoretic approaches to management in a single, cohesive framework Provides authoritative, state-of-the-art descriptions of quantitative approaches to modeling, estimation and decision-making Emphasizes the role of mathematical modeling in the conduct of science and management Utilizes a unifying biological context, consistent mathematical notation, and numerous biological examples
This is a reprint of a previously published book. It deals with providing a rationale as to why some companies that appear to be on the brink of corporate collapse are taken over rather than entering into receivership. Do not put Lightning logo on cover.
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