Statistical Inference On Random Graphs

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Models for Probability and Statistical Inference

Author : James H. Stapleton
Publisher : John Wiley & Sons
ISBN 13 : 0470183403
Total Pages : 466 pages
Book Rating : 4.4/5 (71 download)

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Book Synopsis Models for Probability and Statistical Inference by : James H. Stapleton

Download or read book Models for Probability and Statistical Inference written by James H. Stapleton and published by John Wiley & Sons. This book was released on 2007-12-14 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readers Models for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping. Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses modes of convergence of sequences of random variables, with special attention to convergence in distribution. The second half of the book addresses statistical inference, beginning with a discussion on point estimation and followed by coverage of consistency and confidence intervals. Further areas of exploration include: distributions defined in terms of the multivariate normal, chi-square, t, and F (central and non-central); the one- and two-sample Wilcoxon test, together with methods of estimation based on both; linear models with a linear space-projection approach; and logistic regression. Each section contains a set of problems ranging in difficulty from simple to more complex, and selected answers as well as proofs to almost all statements are provided. An abundant amount of figures in addition to helpful simulations and graphs produced by the statistical package S-Plus(r) are included to help build the intuition of readers.

Random Graphs for Statistical Pattern Recognition

Author : David J. Marchette
Publisher : John Wiley & Sons
ISBN 13 : 0471722081
Total Pages : 261 pages
Book Rating : 4.4/5 (717 download)

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Book Synopsis Random Graphs for Statistical Pattern Recognition by : David J. Marchette

Download or read book Random Graphs for Statistical Pattern Recognition written by David J. Marchette and published by John Wiley & Sons. This book was released on 2005-02-11 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely convergence of two widely used disciplines Random Graphs for Statistical Pattern Recognition is the first book to address the topic of random graphs as it applies to statistical pattern recognition. Both topics are of vital interest to researchers in various mathematical and statistical fields and have never before been treated together in one book. The use of data random graphs in pattern recognition in clustering and classification is discussed, and the applications for both disciplines are enhanced with new tools for the statistical pattern recognition community. New and interesting applications for random graph users are also introduced. This important addition to statistical literature features: Information that previously has been available only through scattered journal articles Practical tools and techniques for a wide range of real-world applications New perspectives on the relationship between pattern recognition and computational geometry Numerous experimental problems to encourage practical applications With its comprehensive coverage of two timely fields, enhanced with many references and real-world examples, Random Graphs for Statistical Pattern Recognition is a valuable resource for industry professionals and students alike.

Statistical Inference as Severe Testing

Author : Deborah G. Mayo
Publisher : Cambridge University Press
ISBN 13 : 1108563309
Total Pages : 503 pages
Book Rating : 4.1/5 (85 download)

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Book Synopsis Statistical Inference as Severe Testing by : Deborah G. Mayo

Download or read book Statistical Inference as Severe Testing written by Deborah G. Mayo and published by Cambridge University Press. This book was released on 2018-09-20 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.

Inferential Network Analysis

Author : Skyler J. Cranmer
Publisher : Cambridge University Press
ISBN 13 : 1107158125
Total Pages : 317 pages
Book Rating : 4.1/5 (71 download)

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Book Synopsis Inferential Network Analysis by : Skyler J. Cranmer

Download or read book Inferential Network Analysis written by Skyler J. Cranmer and published by Cambridge University Press. This book was released on 2020-11-19 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pioneering introduction of unprecedented breadth and scope to inferential and statistical methods for network analysis.

All of Statistics

Author : Larry Wasserman
Publisher : Springer Science & Business Media
ISBN 13 : 0387217363
Total Pages : 446 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis All of Statistics by : Larry Wasserman

Download or read book All of Statistics written by Larry Wasserman and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

Exponential Random Graph Models for Social Networks

Author : Dean Lusher
Publisher : Cambridge University Press
ISBN 13 : 0521193567
Total Pages : 361 pages
Book Rating : 4.5/5 (211 download)

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Book Synopsis Exponential Random Graph Models for Social Networks by : Dean Lusher

Download or read book Exponential Random Graph Models for Social Networks written by Dean Lusher and published by Cambridge University Press. This book was released on 2013 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an account of the theoretical and methodological underpinnings of exponential random graph models (ERGMs).

Probably Not

Author : Lawrence N. Dworsky
Publisher : John Wiley & Sons
ISBN 13 : 0470282045
Total Pages : 328 pages
Book Rating : 4.4/5 (72 download)

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Book Synopsis Probably Not by : Lawrence N. Dworsky

Download or read book Probably Not written by Lawrence N. Dworsky and published by John Wiley & Sons. This book was released on 2008-05-23 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: An engaging, entertaining, and informative introduction to probability and prediction in our everyday lives Although Probably Not deals with probability and statistics, it is not heavily mathematical and is not filled with complex derivations, proofs, and theoretical problem sets. This book unveils the world of statistics through questions such as what is known based upon the information at hand and what can be expected to happen. While learning essential concepts including "the confidence factor" and "random walks," readers will be entertained and intrigued as they move from chapter to chapter. Moreover, the author provides a foundation of basic principles to guide decision making in almost all facets of life including playing games, developing winning business strategies, and managing personal finances. Much of the book is organized around easy-to-follow examples that address common, everyday issues such as: How travel time is affected by congestion, driving speed, and traffic lights Why different gambling casino strategies ultimately offer players no advantage How to estimate how many different birds of one species are seen on a walk through the woods Seemingly random events—coin flip games, the Central Limit Theorem, binomial distributions and Poisson distributions, Parrando's Paradox, and Benford's Law—are addressed and treated through key concepts and methods in probability. In addition, fun-to-solve problems including "the shared birthday" and "the prize behind door number one, two, or three" are found throughout the book, which allow readers to test and practice their new probability skills. Requiring little background knowledge of mathematics, readers will gain a greater understanding of the many daily activities and events that involve random processes and statistics. Combining the mathematics of probability with real-world examples, Probably Not is an ideal reference for practitioners and students who would like to learn more about the role of probability and statistics in everyday decision making.

Statistical Analysis of Network Data

Author : Eric D. Kolaczyk
Publisher : Springer Science & Business Media
ISBN 13 : 0387881468
Total Pages : 397 pages
Book Rating : 4.3/5 (878 download)

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Book Synopsis Statistical Analysis of Network Data by : Eric D. Kolaczyk

Download or read book Statistical Analysis of Network Data written by Eric D. Kolaczyk and published by Springer Science & Business Media. This book was released on 2009-04-20 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years there has been an explosion of network data – that is, measu- ments that are either of or from a system conceptualized as a network – from se- ingly all corners of science. The combination of an increasingly pervasive interest in scienti c analysis at a systems level and the ever-growing capabilities for hi- throughput data collection in various elds has fueled this trend. Researchers from biology and bioinformatics to physics, from computer science to the information sciences, and from economics to sociology are more and more engaged in the c- lection and statistical analysis of data from a network-centric perspective. Accordingly, the contributions to statistical methods and modeling in this area have come from a similarly broad spectrum of areas, often independently of each other. Many books already have been written addressing network data and network problems in speci c individual disciplines. However, there is at present no single book that provides a modern treatment of a core body of knowledge for statistical analysis of network data that cuts across the various disciplines and is organized rather according to a statistical taxonomy of tasks and techniques. This book seeks to ll that gap and, as such, it aims to contribute to a growing trend in recent years to facilitate the exchange of knowledge across the pre-existing boundaries between those disciplines that play a role in what is coming to be called ‘network science.

A Survey of Statistical Network Models

Author : Anna Goldenberg
Publisher : Now Publishers Inc
ISBN 13 : 1601983204
Total Pages : 118 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis A Survey of Statistical Network Models by : Anna Goldenberg

Download or read book A Survey of Statistical Network Models written by Anna Goldenberg and published by Now Publishers Inc. This book was released on 2010 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.

Stochastic Epidemic Models with Inference

Author : Tom Britton
Publisher : Springer Nature
ISBN 13 : 3030309002
Total Pages : 477 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Stochastic Epidemic Models with Inference by : Tom Britton

Download or read book Stochastic Epidemic Models with Inference written by Tom Britton and published by Springer Nature. This book was released on 2019-11-30 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focussing on stochastic models for the spread of infectious diseases in a human population, this book is the outcome of a two-week ICPAM/CIMPA school on "Stochastic models of epidemics" which took place in Ziguinchor, Senegal, December 5–16, 2015. The text is divided into four parts, each based on one of the courses given at the school: homogeneous models (Tom Britton and Etienne Pardoux), two-level mixing models (David Sirl and Frank Ball), epidemics on graphs (Viet Chi Tran), and statistics for epidemic models (Catherine Larédo). The CIMPA school was aimed at PhD students and Post Docs in the mathematical sciences. Parts (or all) of this book can be used as the basis for traditional or individual reading courses on the topic. For this reason, examples and exercises (some with solutions) are provided throughout.

Statistical Inference for Everyone

Author : Brian Blais
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781499715071
Total Pages : 200 pages
Book Rating : 4.7/5 (15 download)

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Book Synopsis Statistical Inference for Everyone by : Brian Blais

Download or read book Statistical Inference for Everyone written by Brian Blais and published by Createspace Independent Publishing Platform. This book was released on 2014-08-27 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Approaching an introductory statistical inference textbook in a novel way, this book is motivated by the perspective of "probability theory as logic". Targeted to the typical "Statistics 101" college student this book covers the topics typically treated in such a course - but from a fresh angle. This book walks through a simple introduction to probability, and then applies those principles to all problems of inference. Topics include hypothesis testing, data visualization, parameter inference, and model comparison. Statistical Inference for Everyone is freely available under the Creative Commons License, and includes a software library in Python for making calculations and visualizations straightforward.

Statistics for Mathematicians

Author : Victor M. Panaretos
Publisher : Birkhäuser
ISBN 13 : 3319283413
Total Pages : 190 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Statistics for Mathematicians by : Victor M. Panaretos

Download or read book Statistics for Mathematicians written by Victor M. Panaretos and published by Birkhäuser. This book was released on 2016-06-01 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a coherent introduction to the main concepts and methods of one-parameter statistical inference. Intended for students of Mathematics taking their first course in Statistics, the focus is on Statistics for Mathematicians rather than on Mathematical Statistics. The goal is not to focus on the mathematical/theoretical aspects of the subject, but rather to provide an introduction to the subject tailored to the mindset and tastes of Mathematics students, who are sometimes turned off by the informal nature of Statistics courses. This book can be used as the basis for an elementary semester-long first course on Statistics with a firm sense of direction that does not sacrifice rigor. The deeper goal of the text is to attract the attention of promising Mathematics students.

Multilevel Network Analysis for the Social Sciences

Author : Emmanuel Lazega
Publisher : Springer
ISBN 13 : 3319245201
Total Pages : 373 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Multilevel Network Analysis for the Social Sciences by : Emmanuel Lazega

Download or read book Multilevel Network Analysis for the Social Sciences written by Emmanuel Lazega and published by Springer. This book was released on 2015-12-16 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides new insights into the functioning of organizational, managerial and market societies. Multilevel analysis and social network analysis are described and the authors show how they can be combined in developing the theory, methods and empirical applications of the social sciences. This book maps out the development of multilevel reasoning and shows how it can explain behavior, through two different ways of contextualizing it. First, by identifying levels of influence on behavior and different aggregations of actors and behavior, and complex interactions between context and behavior. Second, by identifying different levels as truly different systems of agency: such levels of agency can be examined separately and jointly since the link between them is affiliation of members of one level to collective actors at the superior level. It is by combining these approaches that this work offers new insights. New case studies and datasets that explore new avenues of theorizing and new applications of methodology are presented. This book will be useful as a reference work for all social scientists, economists and historians who use network analyses and multilevel statistical analyses. Philosophers interested in the philosophy of science or epistemology will also find this book valuable.

Statistical Inference in Graphs

Author : Ove Frank
Publisher :
ISBN 13 :
Total Pages : 568 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Statistical Inference in Graphs by : Ove Frank

Download or read book Statistical Inference in Graphs written by Ove Frank and published by . This book was released on 1971 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Introduction to Statistical Modelling and Inference

Author : Murray Aitkin
Publisher : CRC Press
ISBN 13 : 100064457X
Total Pages : 391 pages
Book Rating : 4.0/5 (6 download)

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Book Synopsis Introduction to Statistical Modelling and Inference by : Murray Aitkin

Download or read book Introduction to Statistical Modelling and Inference written by Murray Aitkin and published by CRC Press. This book was released on 2022-09-30 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: The complexity of large-scale data sets (“Big Data”) has stimulated the development of advanced computational methods for analysing them. There are two different kinds of methods to aid this. The model-based method uses probability models and likelihood and Bayesian theory, while the model-free method does not require a probability model, likelihood or Bayesian theory. These two approaches are based on different philosophical principles of probability theory, espoused by the famous statisticians Ronald Fisher and Jerzy Neyman. Introduction to Statistical Modelling and Inference covers simple experimental and survey designs, and probability models up to and including generalised linear (regression) models and some extensions of these, including finite mixtures. A wide range of examples from different application fields are also discussed and analysed. No special software is used, beyond that needed for maximum likelihood analysis of generalised linear models. Students are expected to have a basic mathematical background in algebra, coordinate geometry and calculus. Features • Probability models are developed from the shape of the sample empirical cumulative distribution function (cdf) or a transformation of it. • Bounds for the value of the population cumulative distribution function are obtained from the Beta distribution at each point of the empirical cdf. • Bayes’s theorem is developed from the properties of the screening test for a rare condition. • The multinomial distribution provides an always-true model for any randomly sampled data. • The model-free bootstrap method for finding the precision of a sample estimate has a model-based parallel – the Bayesian bootstrap – based on the always-true multinomial distribution. • The Bayesian posterior distributions of model parameters can be obtained from the maximum likelihood analysis of the model. This book is aimed at students in a wide range of disciplines including Data Science. The book is based on the model-based theory, used widely by scientists in many fields, and compares it, in less detail, with the model-free theory, popular in computer science, machine learning and official survey analysis. The development of the model-based theory is accelerated by recent developments in Bayesian analysis.

Probability Theory and Statistical Inference

Author : Aris Spanos
Publisher : Cambridge University Press
ISBN 13 : 1107185149
Total Pages : 787 pages
Book Rating : 4.1/5 (71 download)

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Book Synopsis Probability Theory and Statistical Inference by : Aris Spanos

Download or read book Probability Theory and Statistical Inference written by Aris Spanos and published by Cambridge University Press. This book was released on 2019-09-19 with total page 787 pages. Available in PDF, EPUB and Kindle. Book excerpt: This empirical research methods course enables informed implementation of statistical procedures, giving rise to trustworthy evidence.

Constrained Statistical Inference

Author : Mervyn J. Silvapulle
Publisher : John Wiley & Sons
ISBN 13 : 1118165632
Total Pages : 560 pages
Book Rating : 4.1/5 (181 download)

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Book Synopsis Constrained Statistical Inference by : Mervyn J. Silvapulle

Download or read book Constrained Statistical Inference written by Mervyn J. Silvapulle and published by John Wiley & Sons. This book was released on 2011-09-15 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date approach to understanding statistical inference Statistical inference is finding useful applications in numerous fields, from sociology and econometrics to biostatistics. This volume enables professionals in these and related fields to master the concepts of statistical inference under inequality constraints and to apply the theory to problems in a variety of areas. Constrained Statistical Inference: Order, Inequality, and Shape Constraints provides a unified and up-to-date treatment of the methodology. It clearly illustrates concepts with practical examples from a variety of fields, focusing on sociology, econometrics, and biostatistics. The authors also discuss a broad range of other inequality-constrained inference problems that do not fit well in the contemplated unified framework, providing a meaningful way for readers to comprehend methodological resolutions. Chapter coverage includes: Population means and isotonic regression Inequality-constrained tests on normal means Tests in general parametric models Likelihood and alternatives Analysis of categorical data Inference on monotone density function, unimodal density function, shape constraints, and DMRL functions Bayesian perspectives, including Stein’s Paradox, shrinkage estimation, and decision theory