The Association Graph And The Multigraph For Loglinear Models

Author: Harry J. Khamis
Editor: SAGE
ISBN: 1452238952
Size: 18,69 MB
Format: PDF, Kindle
Read: 475
Download

The Association Graph and the Multigraph for Loglinear Models will help students, particularly those studying the analysis of categorical data, to develop the ability to evaluate and unravel even the most complex loglinear models without heavy calculations or statistical software. This supplemental text reviews loglinear models, explains the association graph, and introduces the multigraph to students who may have little prior experience of graphical techniques, but have some familiarity with categorical variable modeling. The author presents logical step-by-step techniques from the point of view of the practitioner, focusing on how the technique is applied to contingency table data and how the results are interpreted.

Nonrecursive Models

Author: Pamela Paxton
Editor: SAGE
ISBN: 1452237867
Size: 15,49 MB
Format: PDF, Docs
Read: 899
Download

Nonrecursive Models is a clear and concise introduction to the estimation and assessment of nonrecursive simultaneous equation models. This unique monograph gives practical advice on the specification and identification of simultaneous equation models, how to assess the quality of the estimates, and how to correctly interpret results.

Multivariate General Linear Models

Author: Richard F. Haase
Editor: SAGE
ISBN: 1412972493
Size: 11,67 MB
Format: PDF, Docs
Read: 919
Download

This book provides a graduate level introduction to multivariate multiple regression analysis. The book can be used as a sole text for that topic, or as a supplemental text in a course that addresses a larger number of multivariate topics. The text is divided into seven short chapters. Apart from the introductory chapter giving an overview of multivariate multiple regression models, the content outline follows the classic steps required to solve multivariate general linear model problems: (a) specifying the model (b)estimating the parameters of the model (c) establishing measures of goodness of fit of the model (d) establishing test statistics and testing hypotheses about the model (e) diagnosing the adequacy of the model.

Social Network Analysis

Author: Stanley Wasserman
Editor: Cambridge University Press
ISBN: 1139788612
Size: 16,21 MB
Format: PDF, ePub, Mobi
Read: 743
Download

Social network analysis is used widely in the social and behavioral sciences, as well as in economics, marketing, and industrial engineering. The social network perspective focuses on relationships among social entities and is an important addition to standard social and behavioral research, which is primarily concerned with attributes of the social units. Social Network Analysis: Methods and Applications reviews and discusses methods for the analysis of social networks with a focus on applications of these methods to many substantive examples. It is a reference book that can be used by those who want a comprehensive review of network methods, or by researchers who have gathered network data and want to find the most appropriate method by which to analyze it. It is also intended for use as a textbook as it is the first book to provide comprehensive coverage of the methodology and applications of the field.

Contingency Table Analysis

Author: Maria Kateri
Editor: Springer
ISBN: 0817648119
Size: 12,83 MB
Format: PDF, Docs
Read: 699
Download

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.

A Mathematical Primer For Social Statistics

Author: John Fox
Editor: SAGE
ISBN: 1412960800
Size: 10,79 MB
Format: PDF, Docs
Read: 830
Download

Beyond the introductory level, learning and effectively using statistical methods in the social sciences requires some knowledge of mathematics. This handy volume introduces the areas of mathematics that are most important to applied social statistics.

Pattern Recognition Theory And Applications

Author: J. Kittler
Editor: Springer Science & Business Media
ISBN: 9400977727
Size: 11,49 MB
Format: PDF
Read: 403
Download

This book is the outcome of the successful NATO Advanced Study Institute on Pattern Recognition Theory and Applications, held at St. Anne's College, Oxford, in April 1981., The aim of the meeting was to review the recent advances in the theory of pattern recognition and to assess its current and future practical potential. The theme of the Institute - the decision making aspects of pattern recognition with the emphasis on the novel hybrid approaches - and its scope - a high level tutorial coverage of pattern recognition methodologies counterpointed with contrib uted papers on advanced theoretical topics and applications - are faithfully reflected by the volume. The material is divided into five sections: 1. Methodology 2. Image Understanding and Interpretation 3. Medical Applications 4. Speech Processing and Other Applications 5. Panel Discussions. The first section covers a broad spectrum of pattern recognition methodologies, including geometric, statistical, fuzzy set, syntactic, graph-theoretic and hybrid approaches. Its cove,r age of hybrid methods places the volume in a unique position among existing books on pattern recognition. The second section provides an extensive treatment of the topical problem of image understanding from both the artificial intelligence and pattern recognition points of view. The two application sections demonstrate the usefulness of the novel methodologies in traditional pattern 'recognition application areas. They address the problems of hardware/software implementation and of algorithm robustness, flexibility and general reliability. The final section reports on a panel discussion held during the Institute.

Social Network Data Analytics

Author: Charu C. Aggarwal
Editor: Springer Science & Business Media
ISBN: 1441984623
Size: 11,24 MB
Format: PDF
Read: 786
Download

Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

Machine Learning And Knowledge Discovery In Databases

Author: Hendrik Blockeel
Editor: Springer
ISBN: 3642409881
Size: 17,67 MB
Format: PDF, ePub
Read: 222
Download

This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized in topical sections on reinforcement learning; Markov decision processes; active learning and optimization; learning from sequences; time series and spatio-temporal data; data streams; graphs and networks; social network analysis; natural language processing and information extraction; ranking and recommender systems; matrix and tensor analysis; structured output prediction, multi-label and multi-task learning; transfer learning; bayesian learning; graphical models; nearest-neighbor methods; ensembles; statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; and medical applications.

Computational Medicine In Data Mining And Modeling

Author: Goran Rakocevic
Editor: Springer Science & Business Media
ISBN: 1461487854
Size: 18,74 MB
Format: PDF, Kindle
Read: 255
Download

This book presents an overview of a variety of contemporary statistical, mathematical and computer science techniques which are used to further the knowledge in the medical domain. The authors focus on applying data mining to the medical domain, including mining the sets of clinical data typically found in patient’s medical records, image mining, medical mining, data mining and machine learning applied to generic genomic data and more. This work also introduces modeling behavior of cancer cells, multi-scale computational models and simulations of blood flow through vessels by using patient-specific models. The authors cover different imaging techniques used to generate patient-specific models. This is used in computational fluid dynamics software to analyze fluid flow. Case studies are provided at the end of each chapter. Professionals and researchers with quantitative backgrounds will find Computational Medicine in Data Mining and Modeling useful as a reference. Advanced-level students studying computer science, mathematics, statistics and biomedicine will also find this book valuable as a reference or secondary text book.