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Statistical Models
Author: David A. Freedman
Publisher: Cambridge University Press
ISBN: 1139477315
Pages:
Year: 2009-04-27
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This lively and engaging book explains the things you have to know in order to read empirical papers in the social and health sciences, as well as the techniques you need to build statistical models of your own. The discussion in the book is organized around published studies, as are many of the exercises. Relevant journal articles are reprinted at the back of the book. Freedman makes a thorough appraisal of the statistical methods in these papers and in a variety of other examples. He illustrates the principles of modelling, and the pitfalls. The discussion shows you how to think about the critical issues - including the connection (or lack of it) between the statistical models and the real phenomena. The book is written for advanced undergraduates and beginning graduate students in statistics, as well as students and professionals in the social and health sciences.
Statistical Models in S
Author: T.J. Hastie
Publisher: Routledge
ISBN: 1351414224
Pages: 624
Year: 2017-11-01
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Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linear models, additive models, local regression, and tree-based models. The contributions of the ten authors-most of whom work in the statistics research department at AT&T Bell Laboratories-represent results of research in both the computational and statistical aspects of modeling data.
Statistical Models
Author: A. C. Davison
Publisher: Cambridge University Press
ISBN: 1139437410
Pages:
Year: 2003-08-04
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Models and likelihood are the backbone of modern statistics. This 2003 book gives an integrated development of these topics that blends theory and practice, intended for advanced undergraduate and graduate students, researchers and practitioners. Its breadth is unrivaled, with sections on survival analysis, missing data, Markov chains, Markov random fields, point processes, graphical models, simulation and Markov chain Monte Carlo, estimating functions, asymptotic approximations, local likelihood and spline regressions as well as on more standard topics such as likelihood and linear and generalized linear models. Each chapter contains a wide range of problems and exercises. Practicals in the S language designed to build computing and data analysis skills, and a library of data sets to accompany the book, are available over the Web.
Statistical Models and Analysis in Auditing
Author:
Publisher: National Academies
ISBN:
Pages: 91
Year: 1988-01-01
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Statistical Models in Epidemiology
Author: David Clayton, Michael Hills
Publisher: OUP Oxford
ISBN: 0191650900
Pages: 384
Year: 2013-01-17
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This self-contained account of the statistical basis of epidemiology has been written specifically for those with a basic training in biology, therefore no previous knowledge is assumed and the mathematics is deliberately kept at a manageable level. The authors show how all statistical analysis of data is based on probability models, and once one understands the model, analysis follows easily. In showing how to use models in epidemiology the authors have chosen to emphasize the role of likelihood, an approach to statistics which is both simple and intuitively satisfying. More complex problems can then be tackled by natural extensions of the simple methods. Based on a highly successful course, this book explains the essential statistics for all epidemiologists.
Probability and Statistical Models with Applications
Author: CH. A. Charalambides, M.V. Koutras, N. Balakrishnan
Publisher: CRC Press
ISBN: 1420036084
Pages: 664
Year: 2000-09-21
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This monograph of carefully collected articles reviews recent developments in theoretical and applied statistical science, highlights current noteworthy results and illustrates their applications; and points out possible new directions to pursue. With its enlightening account of statistical discoveries and its numerous figures and tables, Probability and Statistical Models with Applications is a must read for probabilists and theoretical and applied statisticians.
Statistical Models for Causal Analysis
Author: Robert D. Retherford, Minja Kim Choe
Publisher: John Wiley & Sons
ISBN: 1118031342
Pages: 272
Year: 2011-02-01
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Simplifies the treatment of statistical inference focusing on how to specify and interpret models in the context of testing causal theories. Simple bivariate regression, multiple regression, multiple classification analysis, path analysis, logit regression, multinomial logit regression and survival models are among the subjects covered. Features an appendix of computer programs (for major statistical packages) that are used to generate illustrative examples contained in the chapters.
Statistical Models for Proportions and Probabilities
Author: George A.F. Seber
Publisher: Springer Science & Business Media
ISBN: 3642390412
Pages: 69
Year: 2013-07-30
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​Methods for making inferences from data about one or more probabilities and proportions are a fundamental part of a statistician’s toolbox and statistics courses. Unfortunately many of the quick, approximate methods currently taught have recently been found to be inappropriate. This monograph gives an up-to-date review of recent research on the topic and presents both exact methods and helpful approximations. Detailed theory is also presented for the different distributions involved, and can be used in a classroom setting. It will be useful for those teaching statistics at university level and for those involved in statistical consulting.
Multilevel Statistical Models
Author: Harvey Goldstein
Publisher: John Wiley & Sons
ISBN: 111995682X
Pages: 382
Year: 2011-07-08
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Throughout the social, medical and other sciences the importance of understanding complex hierarchical data structures is well understood. Multilevel modelling is now the accepted statistical technique for handling such data and is widely available in computer software packages. A thorough understanding of these techniques is therefore important for all those working in these areas. This new edition of Multilevel Statistical Models brings these techniques together, starting from basic ideas and illustrating how more complex models are derived. Bayesian methodology using MCMC has been extended along with new material on smoothing models, multivariate responses, missing data, latent normal transformations for discrete responses, structural equation modeling and survival models. Key Features: Provides a clear introduction and a comprehensive account of multilevel models. New methodological developments and applications are explored. Written by a leading expert in the field of multilevel methodology. Illustrated throughout with real-life examples, explaining theoretical concepts. This book is suitable as a comprehensive text for postgraduate courses, as well as a general reference guide. Applied statisticians in the social sciences, economics, biological and medical disciplines will find this book beneficial.
Statistical modeling techniques
Author: Samuel S. Shapiro, Alan J. Gross
Publisher: Marcel Dekker Inc
ISBN:
Pages: 367
Year: 1981
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Basic concepts of statistical models; Concepts of statistical theory; Model for measurement: continuous case; Models for measurement: discrete case; Empirical models; Testing models assumptions; Analysis of systems.
Statistical Models in Epidemiology, the Environment, and Clinical Trials
Author: M.Elizabeth Halloran, Donald Berry
Publisher: Springer Science & Business Media
ISBN: 0387989242
Pages: 283
Year: 1999-10-29
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This IMA Volume in Mathematics and its Applications STATISTICAL MODELS IN EPIDEMIOLOGY, THE ENVIRONMENT,AND CLINICAL TRIALS is a combined proceedings on "Design and Analysis of Clinical Trials" and "Statistics and Epidemiology: Environment and Health. " This volume is the third series based on the proceedings of a very successful 1997 IMA Summer Program on "Statistics in the Health Sciences. " I would like to thank the organizers: M. Elizabeth Halloran of Emory University (Biostatistics) and Donald A. Berry of Duke University (Insti tute of Statistics and Decision Sciences and Cancer Center Biostatistics) for their excellent work as organizers of the meeting and for editing the proceedings. I am grateful to Seymour Geisser of University of Minnesota (Statistics), Patricia Grambsch, University of Minnesota (Biostatistics); Joel Greenhouse, Carnegie Mellon University (Statistics); Nicholas Lange, Harvard Medical School (Brain Imaging Center, McLean Hospital); Barry Margolin, University of North Carolina-Chapel Hill (Biostatistics); Sandy Weisberg, University of Minnesota (Statistics); Scott Zeger, Johns Hop kins University (Biostatistics); and Marvin Zelen, Harvard School of Public Health (Biostatistics) for organizing the six weeks summer program. I also take this opportunity to thank the National Science Foundation (NSF) and the Army Research Office (ARO), whose financial support made the workshop possible. Willard Miller, Jr.
Statistical Models and Causal Inference
Author: David A. Freedman, David Collier, Jasjeet S. Sekhon
Publisher: Cambridge University Press
ISBN: 0521195004
Pages: 399
Year: 2010
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David A. Freedman presents a definitive synthesis of his approach to statistical modeling and causal inference in the social sciences.
The EM Algorithm and Related Statistical Models
Author: Michiko Watanabe, Kazunori Yamaguchi
Publisher: CRC Press
ISBN: 0824757025
Pages: 250
Year: 2003-10-15
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Exploring the application and formulation of the EM algorithm, The EM Algorithm and Related Statistical Models offers a valuable method for constructing statistical models when only incomplete information is available, and proposes specific estimation algorithms for solutions to incomplete data problems. The text covers current topics including statistical models with latent variables, as well as neural network models, and Markov Chain Monte Carlo methods. It describes software resources valuable for the processing of the EM algorithm with incomplete data and for general analysis of latent structure models of categorical data, and studies accelerated versions of the EM algorithm.
Statistical Models for Data Analysis
Author: Paolo Giudici, Salvatore Ingrassia, Maurizio Vichi
Publisher: Springer Science & Business Media
ISBN: 3319000322
Pages: 419
Year: 2013-07-01
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The papers in this book cover issues related to the development of novel statistical models for the analysis of data. They offer solutions for relevant problems in statistical data analysis and contain the explicit derivation of the proposed models as well as their implementation. The book assembles the selected and refereed proceedings of the biannual conference of the Italian Classification and Data Analysis Group (CLADAG), a section of the Italian Statistical Society. ​
Statistical Models of Shape
Author: Rhodri Davies, carole twining, Chris Taylor
Publisher: Springer Science & Business Media
ISBN: 184800138X
Pages: 302
Year: 2008-12-15
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The goal of image interpretation is to convert raw image data into me- ingful information. Images are often interpreted manually. In medicine, for example, a radiologist looks at a medical image, interprets it, and tra- lates the data into a clinically useful form. Manual image interpretation is, however, a time-consuming, error-prone, and subjective process that often requires specialist knowledge. Automated methods that promise fast and - jective image interpretation have therefore stirred up much interest and have become a signi?cant area of research activity. Early work on automated interpretation used low-level operations such as edge detection and region growing to label objects in images. These can p- ducereasonableresultsonsimpleimages,butthepresenceofnoise,occlusion, andstructuralcomplexity oftenleadstoerroneouslabelling. Furthermore,- belling an object is often only the ?rst step of the interpretation process. In order to perform higher-level analysis, a priori information must be incor- rated into the interpretation process. A convenient way of achieving this is to use a ?exible model to encode information such as the expected size, shape, appearance, and position of objects in an image. The use of ?exible models was popularized by the active contour model, or ‘snake’ [98]. A snake deforms so as to match image evidence (e.g., edges) whilst ensuring that it satis?es structural constraints. However, a snake lacks speci?city as it has little knowledge of the domain, limiting its value in image interpretation.