Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Inference Of Grouped Time Varying Network Vector Autoregression Models
Download Inference Of Grouped Time Varying Network Vector Autoregression Models full books in PDF, epub, and Kindle. Read online Inference Of Grouped Time Varying Network Vector Autoregression Models ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Bayesian Inference for Gene Expression and Proteomics by : Kim-Anh Do
Download or read book Bayesian Inference for Gene Expression and Proteomics written by Kim-Anh Do and published by Cambridge University Press. This book was released on 2006-07-24 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.
Book Synopsis Network Psychometrics with R by : Adela-Maria Isvoranu
Download or read book Network Psychometrics with R written by Adela-Maria Isvoranu and published by Taylor & Francis. This book was released on 2022-04-28 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: A systematic, innovative introduction to the field of network analysis, Network Psychometrics with R: A Guide for Behavioral and Social Scientists provides a comprehensive overview of and guide to both the theoretical foundations of network psychometrics as well as modelling techniques developed from this perspective. Written by pioneers in the field, this textbook showcases cutting-edge methods in an easily accessible format, accompanied by problem sets and code. After working through this book, readers will be able to understand the theoretical foundations behind network modelling, infer network topology, and estimate network parameters from different sources of data. This book features an introduction on the statistical programming language R that guides readers on how to analyse network structures and their stability using R. While Network Psychometrics with R is written in the context of social and behavioral science, the methods introduced in this book are widely applicable to data sets from related fields of study. Additionally, while the text is written in a non-technical manner, technical content is highlighted in textboxes for the interested reader. Network Psychometrics with R is ideal for instructors and students of undergraduate and graduate level courses and workshops in the field of network psychometrics as well as established researchers looking to master new methods. This book is accompanied by a companion website with resources for both students and lecturers.
Book Synopsis Time-Varying Effect Modeling for the Behavioral, Social, and Health Sciences by : Stephanie T. Lanza
Download or read book Time-Varying Effect Modeling for the Behavioral, Social, and Health Sciences written by Stephanie T. Lanza and published by Springer Nature. This book was released on 2021-05-06 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first to introduce applied behavioral, social, and health sciences researchers to a new analytic method, the time-varying effect model (TVEM). It details how TVEM may be used to advance research on developmental and dynamic processes by examining how associations between variables change across time. The book describes how TVEM is a direct and intuitive extension of standard linear regression; whereas standard linear regression coefficients are static estimates that do not change with time, TVEM coefficients are allowed to change as continuous functions of real time, including developmental age, historical time, time of day, days since an event, and so forth. The book introduces readers to new research questions that can be addressed by applying TVEM in their research. Readers gain the practical skills necessary for specifying a wide variety of time-varying effect models, including those with continuous, binary, and count outcomes. The book presents technical details of TVEM estimation and three novel empirical studies focused on developmental questions using TVEM to estimate age-varying effects, historical shifts in behavior and attitudes, and real-time changes across days relative to an event. The volume provides a walkthrough of the process for conducting each of these studies, presenting decisions that were made, and offering sufficient detail so that readers may embark on similar studies in their own research. The book concludes with comments about additional uses of TVEM in applied research as well as software considerations and future directions. Throughout the book, proper interpretation of the output provided by TVEM is emphasized. Time-Varying Effect Modeling for the Behavioral, Social, and Health Sciences is an essential resource for researchers, clinicians/practitioners as well as graduate students in developmental psychology, public health, statistics and methodology for the social, behavioral, developmental, and public health sciences.
Book Synopsis Random Coefficient Autoregressive Models: An Introduction by : D.F. Nicholls
Download or read book Random Coefficient Autoregressive Models: An Introduction written by D.F. Nicholls and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this monograph we have considered a class of autoregressive models whose coefficients are random. The models have special appeal among the non-linear models so far considered in the statistical literature, in that their analysis is quite tractable. It has been possible to find conditions for stationarity and stability, to derive estimates of the unknown parameters, to establish asymptotic properties of these estimates and to obtain tests of certain hypotheses of interest. We are grateful to many colleagues in both Departments of Statistics at the Australian National University and in the Department of Mathematics at the University of Wo110ngong. Their constructive criticism has aided in the presentation of this monograph. We would also like to thank Dr M. A. Ward of the Department of Mathematics, Australian National University whose program produced, after minor modifications, the "three dimensional" graphs of the log-likelihood functions which appear on pages 83-86. Finally we would like to thank J. Radley, H. Patrikka and D. Hewson for their contributions towards the typing of a difficult manuscript. IV CONTENTS CHAPTER 1 INTRODUCTION 1. 1 Introduction 1 Appendix 1. 1 11 Appendix 1. 2 14 CHAPTER 2 STATIONARITY AND STABILITY 15 2. 1 Introduction 15 2. 2 Singly-Infinite Stationarity 16 2. 3 Doubly-Infinite Stationarity 19 2. 4 The Case of a Unit Eigenvalue 31 2. 5 Stability of RCA Models 33 2. 6 Strict Stationarity 37 Appendix 2. 1 38 CHAPTER 3 LEAST SQUARES ESTIMATION OF SCALAR MODELS 40 3.
Book Synopsis Spatial Econometrics by : J. Paul Elhorst
Download or read book Spatial Econometrics written by J. Paul Elhorst and published by Springer Science & Business Media. This book was released on 2013-09-30 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of three generations of spatial econometric models: models based on cross-sectional data, static models based on spatial panels and dynamic spatial panel data models. The book not only presents different model specifications and their corresponding estimators, but also critically discusses the purposes for which these models can be used and how their results should be interpreted.
Book Synopsis Structural Vector Autoregressive Analysis by : Lutz Kilian
Download or read book Structural Vector Autoregressive Analysis written by Lutz Kilian and published by Cambridge University Press. This book was released on 2017-11-23 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields.
Book Synopsis Statistical Parametric Mapping: The Analysis of Functional Brain Images by : William D. Penny
Download or read book Statistical Parametric Mapping: The Analysis of Functional Brain Images written by William D. Penny and published by Elsevier. This book was released on 2011-04-28 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. - An essential reference and companion for users of the SPM software - Provides a complete description of the concepts and procedures entailed by the analysis of brain images - Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data - Stands as a compendium of all the advances in neuroimaging data analysis over the past decade - Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes - Structured treatment of data analysis issues that links different modalities and models - Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible
Book Synopsis Causality in Time Series: Challenges in Machine Learning by : Florin Popescu
Download or read book Causality in Time Series: Challenges in Machine Learning written by Florin Popescu and published by . This book was released on 2013-06 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume in the Challenges in Machine Learning series gathers papers from the Mini Symposium on Causality in Time Series, which was part of the Neural Information Processing Systems (NIPS) confernce in 2009 in Vancouver, Canada. These papers present state-of-the-art research in time-series causality to the machine learning community, unifying methodological interests in the various communities that require such inference.
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.
Book Synopsis Graph Representation Learning by : William L. William L. Hamilton
Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Book Synopsis Social, Cultural, and Behavioral Modeling by : Robert Thomson
Download or read book Social, Cultural, and Behavioral Modeling written by Robert Thomson and published by Springer Nature. This book was released on 2020-10-10 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 13th International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2020, which was planned to take place in Washington, DC, USA. Due to the COVID-19 pandemic the conference was held online during October 18–21, 2020. The 33 full papers presented in this volume were carefully reviewed and selected from 66 submissions. A wide number of disciplines are represented including computer science, psychology, sociology, communication science, public health, bioinformatics, political science, and organizational science. Numerous types of computational methods are used, such as machine learning, language technology, social network analysis and visualization, agent-based simulation, and statistics.
Book Synopsis Bayesian Multivariate Time Series Methods for Empirical Macroeconomics by : Gary Koop
Download or read book Bayesian Multivariate Time Series Methods for Empirical Macroeconomics written by Gary Koop and published by Now Publishers Inc. This book was released on 2010 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Multivariate Time Series Methods for Empirical Macroeconomics provides a survey of the Bayesian methods used in modern empirical macroeconomics. These models have been developed to address the fact that most questions of interest to empirical macroeconomists involve several variables and must be addressed using multivariate time series methods. Many different multivariate time series models have been used in macroeconomics, but Vector Autoregressive (VAR) models have been among the most popular. Bayesian Multivariate Time Series Methods for Empirical Macroeconomics reviews and extends the Bayesian literature on VARs, TVP-VARs and TVP-FAVARs with a focus on the practitioner. The authors go beyond simply defining each model, but specify how to use them in practice, discuss the advantages and disadvantages of each and offer tips on when and why each model can be used.
Book Synopsis Bayesian Structural Equation Modeling by : Sarah Depaoli
Download or read book Bayesian Structural Equation Modeling written by Sarah Depaoli and published by Guilford Publications. This book was released on 2021-08-16 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies data sets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book’s examples.
Book Synopsis Borehole Climatology by : Louise Bodri
Download or read book Borehole Climatology written by Louise Bodri and published by Elsevier. This book was released on 2011-08-29 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Climate for the 21st century is expected to be considerably different from the present and recent past. Industrialization growth combined with the increasing CO2 concentration in the atmosphere and massive deforestation are well above the values over the past several decades and are expected to further grow. Air temperature is rising rapidly well as does the weather variability producing frequent extreme events. Six of the ten warmest years occurred in the 1990s. Temperatures predicted for the 21st century ranges well above the present day value. The time period of the last 100-200 years covered by the direct meteorological observations is too short and does not provide material to reliably assess what may happen over the next hundred(s) years. A faithful prediction of the future requires understanding how climate system works, i.e. to reconstruct past climate much further in the past. Borehole paleoclimatology enables climate reconstruction of the past several millennia, unlike proxy methods provides direct past temperature assessment and can well broaden the areal range to the remote regions poorly covered with meteorological observations. Considerable debates have recently focused on the causes of the present-day warming, i.e. to distinguish between the natural and anthropogenic contribution to the observed temperature increase, eventually to quantify their regional distribution. Complex interpretation of borehole data with the proxies and additional socio-economic information can hopefully help. On observed data taken in various places all over the world we demonstrate suitable examples of the interaction between the subsurface temperature response to time changes in vegetation cover, land-use (farming) and urbanization. Precise temperature-time monitoring in shallow subsurface can further provide the magnitude of the present-day warming within relatively short time intervals. As far as we know, there exists so far no book dealing entirely with the subject of the Borehole climatology. Only relatively rarely this method is mentioned in otherwise plentiful literature on climate reconstruction or on climate modelling. There are, however, series of papers focussing on various borehole--climate related studies in numerous journals (e.g. Global and Planetary Change, Climate Change, Tectonophysics, Journal of Geophysical Research, Geophysical Research Letters, etc). Time to time a special issue appears to summarize papers on this topic presented during specialized symposia. Key Features - Description of a new useful alternative paleoclimate reconstruction method - A suitable source of information for those wishing to learn more about climate change - Material for lecturing and use in the classroom - Ample practical examples of borehole temperature inversions worldwide - Ample illustrations and reference list - Authors have a good knowledge of the problem based on more than 20 years of experience, one of them actually pioneered the method - Description of a new useful alternative paleoclimate reconstruction method - A suitable source of information for those wishing to learn more about climate change - Material for lecturing and use in the classroom - Ample practical examples of borehole temperature inversions worldwide - Ample illustrations and reference list - Authors have a good knowledge of the problem based on more than 20 years of experience, one of them actually pioneered the method
Book Synopsis Handbook of Economic Forecasting by : Graham Elliott
Download or read book Handbook of Economic Forecasting written by Graham Elliott and published by Elsevier. This book was released on 2013-08-23 with total page 667 pages. Available in PDF, EPUB and Kindle. Book excerpt: The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. - Focuses on innovation in economic forecasting via industry applications - Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications - Makes details about economic forecasting accessible to scholars in fields outside economics
Book Synopsis Introductory Econometrics by : Phoebus Dhrymes
Download or read book Introductory Econometrics written by Phoebus Dhrymes and published by Springer. This book was released on 2017-11-21 with total page 637 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a rigorous introduction to the principles of econometrics and gives students and practitioners the tools they need to effectively and accurately analyze real data. Thoroughly updated to address the developments in the field that have occurred since the original publication of this classic text, the second edition has been expanded to include two chapters on time series analysis and one on nonparametric methods. Discussions on covariance (including GMM), partial identification, and empirical likelihood have also been added. The selection of topics and the level of discourse give sufficient variety so that the book can serve as the basis for several types of courses. This book is intended for upper undergraduate and first year graduate courses in economics and statistics and also has applications in mathematics and some social sciences where a reasonable knowledge of matrix algebra and probability theory is common. It is also ideally suited for practicing professionals who want to deepen their understanding of the methods they employ. Also available for the new edition is a solutions manual, containing answers to the end-of-chapter exercises.
Book Synopsis Applied Econometrics with R by : Christian Kleiber
Download or read book Applied Econometrics with R written by Christian Kleiber and published by Springer Science & Business Media. This book was released on 2008-12-10 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.
