handbook of cluster analysis chapman hall crc handbooks of modern statistical methods

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Handbook Of Cluster Analysis

Author : Christian Hennig
ISBN : 1466551887
Genre : Business & Economics
File Size : 28. 74 MB
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The application of cluster analysis (collecting entities that belong together in some sense in a class) is widespread in engineering, medicine, biology, social science, and many other areas. This handbook provides a comprehensive overview of cluster analysis methodology and applications. It summarizes all the recent research in one volume. The book helps researchers understand the state of the art in the field, how to apply cluster analysis using existing methods, and how to choose a method and evaluate the result.

Handbook Of Spatial Epidemiology

Author : Andrew B. Lawson
ISBN : 9781482253023
Genre : Mathematics
File Size : 20. 92 MB
Format : PDF, ePub, Mobi
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Handbook of Spatial Epidemiology explains how to model epidemiological problems and improve inference about disease etiology from a geographical perspective. Top epidemiologists, geographers, and statisticians share interdisciplinary viewpoints on analyzing spatial data and space–time variations in disease incidences. These analyses can provide important information that leads to better decision making in public health. The first part of the book addresses general issues related to epidemiology, GIS, environmental studies, clustering, and ecological analysis. The second part presents basic statistical methods used in spatial epidemiology, including fundamental likelihood principles, Bayesian methods, and testing and nonparametric approaches. With a focus on special methods, the third part describes geostatistical models, splines, quantile regression, focused clustering, mixtures, multivariate methods, and much more. The final part examines special problems and application areas, such as residential history analysis, segregation, health services research, health surveys, infectious disease, veterinary topics, and health surveillance and clustering. Spatial epidemiology, also known as disease mapping, studies the geographical or spatial distribution of health outcomes. This handbook offers a wide-ranging overview of state-of-the-art approaches to determine the relationships between health and various risk factors, empowering researchers and policy makers to tackle public health problems.

Handbook Of Spatial Statistics

Author : Alan E. Gelfand
ISBN : 1420072889
Genre : Mathematics
File Size : 64. 30 MB
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Assembling a collection of very prominent researchers in the field, the Handbook of Spatial Statistics presents a comprehensive treatment of both classical and state-of-the-art aspects of this maturing area. It takes a unified, integrated approach to the material, providing cross-references among chapters. The handbook begins with a historical introduction detailing the evolution of the field. It then focuses on the three main branches of spatial statistics: continuous spatial variation (point referenced data); discrete spatial variation, including lattice and areal unit data; and spatial point patterns. The book also contains a section on space–time work as well as a section on important topics that build upon earlier chapters. By collecting the major work in the field in one source, along with including an extensive bibliography, this handbook will assist future research efforts. It deftly balances theory and application, strongly emphasizes modeling, and introduces many real data analysis examples.

Handbook Of Survival Analysis

Author : John P. Klein
ISBN : 9781466555679
Genre : Mathematics
File Size : 42. 12 MB
Format : PDF, Mobi
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Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians

Handbook Of Mixed Membership Models And Their Applications

Author : Edoardo M. Airoldi
ISBN : 9781466504080
Genre : Computers
File Size : 34. 66 MB
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In response to scientific needs for more diverse and structured explanations of statistical data, researchers have discovered how to model individual data points as belonging to multiple groups. Handbook of Mixed Membership Models and Their Applications shows you how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology. Through examples using real data sets, you’ll discover how to characterize complex multivariate data in: Studies involving genetic databases Patterns in the progression of diseases and disabilities Combinations of topics covered by text documents Political ideology or electorate voting patterns Heterogeneous relationships in networks, and much more The handbook spans more than 20 years of the editors’ and contributors’ statistical work in the field. Top researchers compare partial and mixed membership models, explain how to interpret mixed membership, delve into factor analysis, and describe nonparametric mixed membership models. They also present extensions of the mixed membership model for text analysis, sequence and rank data, and network data as well as semi-supervised mixed membership models.

Computational Statistics Handbook With Matlab Second Edition

Author : Wendy L. Martinez
ISBN : 9781420010862
Genre : Mathematics
File Size : 36. 53 MB
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As with the bestselling first edition, Computational Statistics Handbook with MATLAB®, Second Edition covers some of the most commonly used contemporary techniques in computational statistics. With a strong, practical focus on implementing the methods, the authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of the algorithms in data analysis. Updated for MATLAB® R2007a and the Statistics Toolbox, Version 6.0, this edition incorporates many additional computational statistics topics. New to the Second Edition • New functions for multivariate normal and multivariate t distributions • Updated information on the new MATLAB functionality for univariate and bivariate histograms, glyphs, and parallel coordinate plots • New content on independent component analysis, nonlinear dimensionality reduction, and multidimensional scaling • New topics on linear classifiers, quadratic classifiers, and voting methods, such as bagging, boosting, and random forests • More methods for unsupervised learning, including model-based clustering and techniques for assessing the results of clustering • A new chapter on parametric models that covers spline regression models, logistic regression, and generalized linear models • Expanded information on smoothers, such as bin smoothing, running mean and line smoothers, and smoothing splines With numerous problems and suggestions for further reading, this accessible text facilitates an understanding of computational statistics concepts and how they are employed in data analysis.

Handbook Of Big Data

Author : Peter Bühlmann
ISBN : 1482249073
Genre : Business & Economics
File Size : 49. 79 MB
Format : PDF, Docs
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This book describes modern approaches to analyzing and understanding the structure of Big Data. The editors have carefully curated representative methods and techniques from both industry and academia to provide the reader not only with a wide-ranging view of the Big Data landscape but also the fundamental concepts needed to tackle individual challenges. It quickly orients readers and provides a working understanding of key statistical and computing ideas.

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