Download or read book High Dimensional Graphical Model for Categorical Variables written by Yuqi Zhao and published by . This book was released on 2017 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Sparse Graphical Modeling for High Dimensional Data written by Faming Liang and published by CRC Press. This book was released on 2023-08-02 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: A general framework for learning sparse graphical models with conditional independence tests Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data Unified treatments for data integration, network comparison, and covariate adjustment Unified treatments for missing data and heterogeneous data Efficient methods for joint estimation of multiple graphical models Effective methods of high-dimensional variable selection Effective methods of high-dimensional inference

Download or read book Contingency Table Analysis written by Maria Kateri and published by Springer. This book was released on 2014-05-17 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contingency tables arise in diverse fields, including life sciences, education, social and political sciences, notably market research and opinion surveys. Their analysis plays an essential role in gaining insight into structures of the quantities under consideration and in supporting decision making. Combining both theory and applications, this book presents models and methods for the analysis of two- and multidimensional-contingency tables. An excellent reference for advanced undergraduates, graduate students, and practitioners in statistics as well as biosciences, social sciences, education, and economics, the work may also be used as a textbook for a course on categorical data analysis. Prerequisites include basic background on statistical inference and knowledge of statistical software packages.

Download or read book Graphical Models for Categorical Data written by Alberto Roverato and published by Cambridge University Press. This book was released on 2017-08-24 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: For advanced students of network data science, this compact account covers both well-established methodology and the theory of models recently introduced in the graphical model literature. It focuses on the discrete case where all variables involved are categorical and, in this context, it achieves a unified presentation of classical and recent results.

Download or read book Graphical Tools for the Exploration of Multivariate Categorical Data written by Heike Hofmann and published by BoD – Books on Demand. This book was released on 2001 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Trends and Challenges in Categorical Data Analysis written by Maria Kateri and published by Springer Nature. This book was released on 2023-07-08 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a selection of modern and sophisticated methodologies for the analysis of large and complex univariate and multivariate categorical data. It gives an overview of a substantive and broad collection of topics in the analysis of categorical data, including association, marginal and graphical models, time series and fixed effects models, as well as modern methods of estimation such as regularization, Bayesian estimation and bias reduction methods, along with new simple measures for model interpretability. Methodological innovations and developments are illustrated and explained through real-world applications, together with useful R packages, allowing readers to replicate most of the analyses using the provided code. The applications span a variety of disciplines, including education, psychology, health, economics, and social sciences.

Download or read book Regression for Categorical Data written by Gerhard Tutz and published by Cambridge University Press. This book was released on 2011-11-21 with total page 573 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and high-dimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics not normally included in books on categorical data analysis are treated here, such as nonparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods. The book is accompanied by an R package that contains data sets and code for all the examples.

Download or read book Introduction to the Statistical Analysis of Categorical Data written by Erling B. Andersen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with the analysis of categorical data. Statistical models, especially log-linear models for contingency tables and logistic regression, are described and applied to real life data. Special emphasis is given to the use of graphical methods. The book is intended as a text for both undergraduate and graduate courses for statisticians, applied statisticians, social scientists, economists and epidemiologists. Many examples and exercises with solutions should help the reader to understand the material.

Download or read book Categorical Data Analysis written by Alan Agresti and published by John Wiley & Sons. This book was released on 2013-04-08 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the Second Edition "A must-have book for anyone expecting to do research and/or applications in categorical data analysis." —Statistics in Medicine "It is a total delight reading this book." —Pharmaceutical Research "If you do any analysis of categorical data, this is an essential desktop reference." —Technometrics The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections introducing the Bayesian approach for methods in that chapter More than 100 analyses of data sets and over 600 exercises Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.

Download or read book High dimensional Graphical Models Learning written by Ru Wang and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic graphical models are graphical representations of probability distributions. Graphical models have broad applications in the fields of biology, social science, linguistic, neuro-science, etc. In this thesis, we study graphical model structure learning under the high-dimension-low-sample-size regime. We propose various strategies using data perturbation, model aggregation and model regularization to avoid over-fitting and to consequently reduce false positives. We also develop efficient algorithms which greatly improve existing ones and make computationally intensive learning procedures such as model aggregation feasible. We discuss a connection between directed acyclic graph models learning and Markov random field models learning, with Gaussian graphical models as a special case of the latter. We also discuss covariance matrix estimation of high-dimensional Gaussian distributions through directed acyclic graph models learning. In Chapter 2 of the thesis, we focus on graphical models with continuously distributed nodes. In Chapter 3, we propose a hierarchical Poisson log-Normal model for graphical model learning with Poisson counts data with applications in RNA-seq studies. In Chapter 4, the proposed methods are examined by extensive simulation studies. In Chapter 5, they are applied to two genomic data sets for the purpose of constructing genetic regulatory networks.

Download or read book Introduction to Graphical Modelling written by David Edwards and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: A useful introduction to this topic for both students and researchers, with an emphasis on applications and practicalities rather than on a formal development. It is based on the popular software package for graphical modelling, MIM, freely available for downloading from the Internet. Following a description of some of the basic ideas of graphical modelling, subsequent chapters describe particular families of models, including log-linear models, Gaussian models, and models for mixed discrete and continuous variables. Further chapters cover hypothesis testing and model selection. Chapters 7 and 8 are new to this second edition and describe the use of directed, chain, and other graphs, complete with a summary of recent work on causal inference.

Download or read book Model Based Clustering and Classification for Data Science written by Charles Bouveyron and published by Cambridge University Press. This book was released on 2019-07-25 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.

Download or read book Hybrid Random Fields written by Antonino Freno and published by Springer. This book was released on 2011-05-26 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives. -- Manfred Jaeger, Aalborg Universitet The book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [...] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it. -- Marco Gori, Università degli Studi di Siena Graphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data.

Download or read book New Advances in Structure Learning of Graphical Models written by Jun Tao and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphical modeling of multivariate data is drawing increasing attention in theoretical and applied statistics. It is powerful due to its effective representation of high-dimensional data, making the structure more intuitive and easier to understand. Over the past decades, graphical models have been proposed for various fields of study. In this dissertation, we study statistical graphical models to handle different types of data. In particular, we propose three new graphical models: (1) an additive semi-graphoid model for discrete data, (2) a time-varying transnormal model for non-Gaussian data, and (3) a graphical temporal point process model for event stream data, especially for the path to purchase data in multi-touch attribution. Methodologically, we provide new tools to represent statistical relations between variables. The additive semi-graphoid model for discrete data focuses on additive conditional independence. The time-varying transnormal model explores the dynamic pattern of conditional independence. The graphical temporal point process model for event stream data aims at revealing the Granger causality between events in longitudinal order. We also develop learning methods for the new graphical models via the penalized estimation and establish the consistency of the estimators under the high-dimensional setting. Along with these methodological developments, we conduct numerical experiments with synthetic and real data to demonstrate the performance of the new methods.

Download or read book Designing Data Visualizations written by Noah Iliinsky and published by "O'Reilly Media, Inc.". This book was released on 2011-09-16 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data visualization is an efficient and effective medium for communicating large amounts of information, but the design process can often seem like an unexplainable creative endeavor. This concise book aims to demystify the design process by showing you how to use a linear decision-making process to encode your information visually. Delve into different kinds of visualization, including infographics and visual art, and explore the influences at work in each one. Then learn how to apply these concepts to your design process. Learn data visualization classifications, including explanatory, exploratory, and hybrid Discover how three fundamental influences—the designer, the reader, and the data—shape what you create Learn how to describe the specific goal of your visualization and identify the supporting data Decide the spatial position of your visual entities with axes Encode the various dimensions of your data with appropriate visual properties, such as shape and color See visualization best practices and suggestions for encoding various specific data types

Download or read book Visualization of Categorical Data written by Jörg Blasius and published by Academic Press. This book was released on 1998-02-09 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique and timely monograph, Visualization of Categorical Data contains a useful balance of theoretical and practical material on this important new area. Top researchers in the field present the books four main topics: visualization, correspondence analysis, biplots and multidimensional scaling, and contingency table models. This volume discusses how surveys, which are employed in many different research areas, generate categorical data. It will be of great interest to anyone involved in collecting or analyzing categorical data. * Correspondence Analysis * Homogeneity Analysis * Loglinear and Association Models * Latent Class Analysis * Multidimensional Scaling * Cluster Analysis * Ideal Point Discriminant Analysis * CHAID * Formal Concept Analysis * Graphical Models

Download or read book Data Driven Computational Neuroscience written by Concha Bielza and published by Cambridge University Press. This book was released on 2020-11-26 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered.