Probabilistic Foundations Of Statistical Network Analysis

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Probabilistic Foundations of Statistical Network Analysis

Harry Crane
Probabilistic Foundations of Statistical Network Analysis Book PDF

Author : Harry Crane
Publisher : CRC Press
Page : 432 pages
File Size : 48,6 Mb
Release : 2018-04-17
Category : Business & Economics
ISBN : 9781351807326

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Probabilistic Foundations of Statistical Network Analysis by Harry Crane Pdf

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author’s incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane’s research interests cover a range of mathematical and applied topics in network science, probability theory, statistical inference, and mathematical logic. In addition to his technical work on edge and relational exchangeability, relative exchangeability, and graph-valued Markov processes, Prof. Crane’s methods have been applied to domain-specific cybersecurity and counterterrorism problems at the Foreign Policy Research Institute and RAND’s Project AIR FORCE.

Probabilistic Foundations of Statistical Network Analysis

Harry Crane
Probabilistic Foundations of Statistical Network Analysis Book PDF

Author : Harry Crane
Publisher : CRC Press
Page : 236 pages
File Size : 46,7 Mb
Release : 2018-04-17
Category : Business & Economics
ISBN : 9781351807333

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Probabilistic Foundations of Statistical Network Analysis by Harry Crane Pdf

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author’s incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane’s research interests cover a range of mathematical and applied topics in network science, probability theory, statistical inference, and mathematical logic. In addition to his technical work on edge and relational exchangeability, relative exchangeability, and graph-valued Markov processes, Prof. Crane’s methods have been applied to domain-specific cybersecurity and counterterrorism problems at the Foreign Policy Research Institute and RAND’s Project AIR FORCE.

Statistical Network Analysis: Models, Issues, and New Directions

Edoardo M. Airoldi,David M. Blei,Stephen E. Fienberg,Anna Goldenberg,Eric P. Xing,Alice X. Zheng
Statistical Network Analysis Models Issues and New Directions Book PDF

Author : Edoardo M. Airoldi,David M. Blei,Stephen E. Fienberg,Anna Goldenberg,Eric P. Xing,Alice X. Zheng
Publisher : Springer
Page : 200 pages
File Size : 40,8 Mb
Release : 2008-04-12
Category : Computers
ISBN : 9783540731337

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Statistical Network Analysis: Models, Issues, and New Directions by Edoardo M. Airoldi,David M. Blei,Stephen E. Fienberg,Anna Goldenberg,Eric P. Xing,Alice X. Zheng Pdf

This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Statistical Network Analysis: Models, Issues, and New Directions held in Pittsburgh, PA, USA in June 2006 as associated event of the 23rd International Conference on Machine Learning, ICML 2006. It covers probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference.

A Survey of Statistical Network Models

Anna Goldenberg,Alice X. Zheng,Stephen E. Fienberg,Edoardo M. Airoldi
A Survey of Statistical Network Models Book PDF

Author : Anna Goldenberg,Alice X. Zheng,Stephen E. Fienberg,Edoardo M. Airoldi
Publisher : Now Publishers Inc
Page : 118 pages
File Size : 46,7 Mb
Release : 2010
Category : Computers
ISBN : 9781601983206

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A Survey of Statistical Network Models by Anna Goldenberg,Alice X. Zheng,Stephen E. Fienberg,Edoardo M. Airoldi Pdf

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.

Networks and Chaos - Statistical and Probabilistic Aspects

J L Jensen,Wilfrid S. Kendall
Networks and Chaos Statistical and Probabilistic Aspects Book PDF

Author : J L Jensen,Wilfrid S. Kendall
Publisher : CRC Press
Page : 324 pages
File Size : 48,6 Mb
Release : 1993-07-22
Category : Mathematics
ISBN : 0412465302

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Networks and Chaos - Statistical and Probabilistic Aspects by J L Jensen,Wilfrid S. Kendall Pdf

This volume consists of a collection of tutorial papers by leading experts on statistical and probabilistic aspects of chaos and networks, in particular neural networks. While written for the non-expert, they are intended to bring the reader up to the forefront of knowledge and research in the subject areas concerned. The papers, which contain extensive references to the literature, can separately or in various combinations serve as bases for short- or full-length courses, at graduate or more advanced levels. The papers are directed not only to mathematical statisticians but also to students and researchers in related fields of biology, engineering, geology, physics and probability.

Topics at the Frontier of Statistics and Network Analysis

Eric D. Kolaczyk
Topics at the Frontier of Statistics and Network Analysis Book PDF

Author : Eric D. Kolaczyk
Publisher : Cambridge University Press
Page : 214 pages
File Size : 40,6 Mb
Release : 2017-08-10
Category : Mathematics
ISBN : 9781108305617

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Topics at the Frontier of Statistics and Network Analysis by Eric D. Kolaczyk Pdf

This snapshot of the current frontier of statistics and network analysis focuses on the foundational topics of modeling, sampling, and design. Primarily for graduate students and researchers in statistics and closely related fields, emphasis is not only on what has been done, but on what remains to be done.

Quantitative Analysis of Ecological Networks

Mark R. T. Dale,Marie-Josée Fortin
Quantitative Analysis of Ecological Networks Book PDF

Author : Mark R. T. Dale,Marie-Josée Fortin
Publisher : Cambridge University Press
Page : 233 pages
File Size : 43,8 Mb
Release : 2021-04-15
Category : Language Arts & Disciplines
ISBN : 9781108491846

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Quantitative Analysis of Ecological Networks by Mark R. T. Dale,Marie-Josée Fortin Pdf

Displays the broad range of quantitative approaches to analysing ecological networks, providing clear examples and guidance for researchers.

The Statistical Analysis of Multivariate Failure Time Data

Ross L. Prentice,Shanshan Zhao
The Statistical Analysis of Multivariate Failure Time Data Book PDF

Author : Ross L. Prentice,Shanshan Zhao
Publisher : CRC Press
Page : 110 pages
File Size : 54,7 Mb
Release : 2019-05-14
Category : Mathematics
ISBN : 9780429529702

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The Statistical Analysis of Multivariate Failure Time Data by Ross L. Prentice,Shanshan Zhao Pdf

The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text. Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women’s Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice. Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine. Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.

Handbook of Econometrics

Anonim
Handbook of Econometrics Book PDF

Author : Anonim
Publisher : Elsevier
Page : 594 pages
File Size : 46,8 Mb
Release : 2020-11-25
Category : Business & Economics
ISBN : 9780444636546

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Handbook of Econometrics by Anonim Pdf

Handbook of Econometrics, Volume 7A, examines recent advances in foundational issues and "hot" topics within econometrics, such as inference for moment inequalities and estimation of high dimensional models. With its world-class editors and contributors, it succeeds in unifying leading studies of economic models, mathematical statistics and economic data. Our flourishing ability to address empirical problems in economics by using economic theory and statistical methods has driven the field of econometrics to unimaginable places. By designing methods of inference from data based on models of human choice behavior and social interactions, econometricians have created new subfields now sufficiently mature to require sophisticated literature summaries. Presents a broader and more comprehensive view of this expanding field than any other handbook Emphasizes the connection between econometrics and economics Highlights current topics for which no good summaries exist

Statistical Analysis of Network Data with R

Eric D. Kolaczyk,Gábor Csárdi
Statistical Analysis of Network Data with R Book PDF

Author : Eric D. Kolaczyk,Gábor Csárdi
Publisher : Springer
Page : 207 pages
File Size : 47,9 Mb
Release : 2014-05-22
Category : Computers
ISBN : 9781493909834

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Statistical Analysis of Network Data with R by Eric D. Kolaczyk,Gábor Csárdi Pdf

Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).

Complex Networks and Their Applications VIII

Hocine Cherifi,Sabrina Gaito,José Fernendo Mendes,Esteban Moro,Luis Mateus Rocha
Complex Networks and Their Applications VIII Book PDF

Author : Hocine Cherifi,Sabrina Gaito,José Fernendo Mendes,Esteban Moro,Luis Mateus Rocha
Publisher : Springer Nature
Page : 1047 pages
File Size : 42,9 Mb
Release : 2019-11-26
Category : Technology & Engineering
ISBN : 9783030366834

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Complex Networks and Their Applications VIII by Hocine Cherifi,Sabrina Gaito,José Fernendo Mendes,Esteban Moro,Luis Mateus Rocha Pdf

This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students, and practitioners a unique update on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the Eighth International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2019), which took place in Lisbon, Portugal, on December 10–12, 2019. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure, and network dynamics; diffusion, epidemics, and spreading processes; resilience and control as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.

Multistate Models for the Analysis of Life History Data

Richard J Cook,Jerald F. Lawless
Multistate Models for the Analysis of Life History Data Book PDF

Author : Richard J Cook,Jerald F. Lawless
Publisher : CRC Press
Page : 440 pages
File Size : 55,5 Mb
Release : 2018-05-15
Category : Mathematics
ISBN : 9781498715614

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Multistate Models for the Analysis of Life History Data by Richard J Cook,Jerald F. Lawless Pdf

Multistate Models for the Analysis of Life History Data provides the first comprehensive treatment of multistate modeling and analysis, including parametric, nonparametric and semiparametric methods applicable to many types of life history data. Special models such as illness-death, competing risks and progressive processes are considered, as well as more complex models. The book provides both theoretical development and illustrations of analysis based on data from randomized trials and observational cohort studies in health research. It features: Discusses a wide range of applications of multistate models, Presents methods for both continuously and intermittently observed life history processes, Gives a thorough discussion of conditionally independent censoring and observation processes, Discusses models with random effects and joint models for two or more multistate processes, Discusses and illustrates software for multistate analysis that is available in R, Target audience includes those engaged in research and applications involving multistate models.

Statistical Analysis of Network Data

Eric D. Kolaczyk
Statistical Analysis of Network Data Book PDF

Author : Eric D. Kolaczyk
Publisher : Unknown
Page : 399 pages
File Size : 45,9 Mb
Release : 2009
Category : System analysis
ISBN : OCLC:990607226

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Statistical Analysis of Network Data by Eric D. Kolaczyk Pdf

Covers the foundations common to the statistical analysis of network data across the disciplines. This book contains topics that include network mapping, characterization of network structure, network sampling, and the modeling, inference, and prediction of networks, network processes, and network flows.

Nonparametric Models for Longitudinal Data

Colin O. Wu,Xin Tian
Nonparametric Models for Longitudinal Data Book PDF

Author : Colin O. Wu,Xin Tian
Publisher : CRC Press
Page : 552 pages
File Size : 51,6 Mb
Release : 2018-05-23
Category : Mathematics
ISBN : 9780429939082

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Nonparametric Models for Longitudinal Data by Colin O. Wu,Xin Tian Pdf

Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precision medicine. It also provides flexible tools to describe the temporal trends, covariate effects and correlation structures of repeated measurements in longitudinal data. This book is intended for graduate students in statistics, data scientists and statisticians in biomedical sciences and public health. As experts in this area, the authors present extensive materials that are balanced between theoretical and practical topics. The statistical applications in real-life examples lead into meaningful interpretations and inferences. Features: Provides an overview of parametric and semiparametric methods Shows smoothing methods for unstructured nonparametric models Covers structured nonparametric models with time-varying coefficients Discusses nonparametric shared-parameter and mixed-effects models Presents nonparametric models for conditional distributions and functionals Illustrates implementations using R software packages Includes datasets and code in the authors’ website Contains asymptotic results and theoretical derivations

Sequential Change Detection and Hypothesis Testing

Alexander Tartakovsky
Sequential Change Detection and Hypothesis Testing Book PDF

Author : Alexander Tartakovsky
Publisher : CRC Press
Page : 321 pages
File Size : 44,6 Mb
Release : 2019-12-11
Category : Mathematics
ISBN : 9781498757591

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Sequential Change Detection and Hypothesis Testing by Alexander Tartakovsky Pdf

Statistical methods for sequential hypothesis testing and changepoint detection have applications across many fields, including quality control, biomedical engineering, communication networks, econometrics, image processing, security, etc. This book presents an overview of methodology in these related areas, providing a synthesis of research from the last few decades. The methods are illustrated through real data examples, and software is referenced where possible. The emphasis is on providing all the theoretical details in a unified framework, with pointers to new research directions.