Methods And Models In Statistics

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Linear Models in Statistics

Alvin C. Rencher,G. Bruce Schaalje
Linear Models in Statistics Book PDF

Author : Alvin C. Rencher,G. Bruce Schaalje
Publisher : John Wiley & Sons
Page : 690 pages
File Size : 41,6 Mb
Release : 2008-01-07
Category : Mathematics
ISBN : 9780470192603

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Linear Models in Statistics by Alvin C. Rencher,G. Bruce Schaalje Pdf

The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.

Methods and Models in Statistics

John A. Nelder,Niall M. Adams
Methods and Models in Statistics Book PDF

Author : John A. Nelder,Niall M. Adams
Publisher : Imperial College Press
Page : 266 pages
File Size : 43,6 Mb
Release : 2004
Category : Mathematics
ISBN : 1860945414

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Methods and Models in Statistics by John A. Nelder,Niall M. Adams Pdf

John Nelder is one of today''s leading statisticians, having made an impact on many parts of the discipline. This book contains reviews of some of those areas, written by top researchers. It is accessible to non-specialists, and is noteworthy for its breadth of coverage.

Data Mining Methods and Models

Daniel T. Larose
Data Mining Methods and Models Book PDF

Author : Daniel T. Larose
Publisher : John Wiley & Sons
Page : 340 pages
File Size : 55,9 Mb
Release : 2006-02-02
Category : Computers
ISBN : 9780471756477

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Data Mining Methods and Models by Daniel T. Larose Pdf

Apply powerful Data Mining Methods and Models to Leverage your Data for Actionable Results Data Mining Methods and Models provides: * The latest techniques for uncovering hidden nuggets of information * The insight into how the data mining algorithms actually work * The hands-on experience of performing data mining on large data sets Data Mining Methods and Models: * Applies a "white box" methodology, emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets, including a detailed case study, "Modeling Response to Direct-Mail Marketing" * Tests the reader's level of understanding of the concepts and methodologies, with over 110 chapter exercises * Demonstrates the Clementine data mining software suite, WEKA open source data mining software, SPSS statistical software, and Minitab statistical software * Includes a companion Web site, www.dataminingconsultant.com, where the data sets used in the book may be downloaded, along with a comprehensive set of data mining resources. Faculty adopters of the book have access to an array of helpful resources, including solutions to all exercises, a PowerPoint(r) presentation of each chapter, sample data mining course projects and accompanying data sets, and multiple-choice chapter quizzes. With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field. An Instructor's Manual presenting detailed solutions to all the problems in the book is available onlne.

Grade Models and Methods for Data Analysis

Teresa Kowalczyk,Elzbieta Pleszczynska
Grade Models and Methods for Data Analysis Book PDF

Author : Teresa Kowalczyk,Elzbieta Pleszczynska
Publisher : Springer Science & Business Media
Page : 504 pages
File Size : 54,5 Mb
Release : 2004-04-16
Category : Computers
ISBN : 3540211209

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Grade Models and Methods for Data Analysis by Teresa Kowalczyk,Elzbieta Pleszczynska Pdf

This book provides a new grade methodology for intelligent data analysis. It introduces a specific infrastructure of concepts needed to describe data analysis models and methods. This monograph is the only book presently available covering both the theory and application of grade data analysis and therefore aiming both at researchers, students, as well as applied practitioners. The text is richly illustrated through examples and case studies and includes a short introduction to software implementing grade methods, which can be downloaded from the editors.

Mixed Effects Models for Complex Data

Lang Wu
Mixed Effects Models for Complex Data Book PDF

Author : Lang Wu
Publisher : CRC Press
Page : 431 pages
File Size : 43,8 Mb
Release : 2009-11-11
Category : Mathematics
ISBN : 1420074083

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Mixed Effects Models for Complex Data by Lang Wu Pdf

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

Methods and Applications of Linear Models

Ronald R. Hocking
Methods and Applications of Linear Models Book PDF

Author : Ronald R. Hocking
Publisher : John Wiley & Sons
Page : 724 pages
File Size : 46,6 Mb
Release : 2013-07-01
Category : Mathematics
ISBN : 9781118593028

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Methods and Applications of Linear Models by Ronald R. Hocking Pdf

Praise for the Second Edition "An essential desktop reference book . . . it should definitely be on your bookshelf." —Technometrics A thoroughly updated book, Methods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition features innovative approaches to understanding and working with models and theory of linear regression. The Third Edition provides readers with the necessary theoretical concepts, which are presented using intuitive ideas rather than complicated proofs, to describe the inference that is appropriate for the methods being discussed. The book presents a unique discussion that combines coverage of mathematical theory of linear models with analysis of variance models, providing readers with a comprehensive understanding of both the theoretical and technical aspects of linear models. With a new focus on fixed effects models, Methods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition also features: Newly added topics including least squares, the cell means model, and graphical inspection of data in the AVE method Frequent conceptual and numerical examples for clarifying the statistical analyses and demonstrating potential pitfalls Graphics and computations developed using JMP® software to accompany the concepts and techniques presented Numerous exercises presented to test readers and deepen their understanding of the material An ideal book for courses on linear models and linear regression at the undergraduate and graduate levels, the Third Edition of Methods and Applications of Linear Models: Regression and the Analysis of Variance is also a valuable reference for applied statisticians and researchers who utilize linear model methodology.

Advances in Complex Data Modeling and Computational Methods in Statistics

Anna Maria Paganoni,Piercesare Secchi
Advances in Complex Data Modeling and Computational Methods in Statistics Book PDF

Author : Anna Maria Paganoni,Piercesare Secchi
Publisher : Springer
Page : 210 pages
File Size : 52,8 Mb
Release : 2014-11-04
Category : Mathematics
ISBN : 9783319111490

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Advances in Complex Data Modeling and Computational Methods in Statistics by Anna Maria Paganoni,Piercesare Secchi Pdf

The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.

Statistical Analysis of Network Data

Eric D. Kolaczyk
Statistical Analysis of Network Data Book PDF

Author : Eric D. Kolaczyk
Publisher : Springer Science & Business Media
Page : 397 pages
File Size : 42,6 Mb
Release : 2009-04-20
Category : Computers
ISBN : 9780387881461

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Statistical Analysis of Network Data by Eric D. Kolaczyk Pdf

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.

Statistical Models and Methods for Lifetime Data

Jerald F. Lawless
Statistical Models and Methods for Lifetime Data Book PDF

Author : Jerald F. Lawless
Publisher : John Wiley & Sons
Page : 662 pages
File Size : 48,9 Mb
Release : 2011-01-25
Category : Mathematics
ISBN : 9781118031254

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Statistical Models and Methods for Lifetime Data by Jerald F. Lawless Pdf

Praise for the First Edition "An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ." -Choice "This is an important book, which will appeal to statisticians working on survival analysis problems." -Biometrics "A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook." -Statistics in Medicine The statistical analysis of lifetime or response time data is a key tool in engineering, medicine, and many other scientific and technological areas. This book provides a unified treatment of the models and statistical methods used to analyze lifetime data. Equally useful as a reference for individuals interested in the analysis of lifetime data and as a text for advanced students, Statistical Models and Methods for Lifetime Data, Second Edition provides broad coverage of the area without concentrating on any single field of application. Extensive illustrations and examples drawn from engineering and the biomedical sciences provide readers with a clear understanding of key concepts. New and expanded coverage in this edition includes: * Observation schemes for lifetime data * Multiple failure modes * Counting process-martingale tools * Both special lifetime data and general optimization software * Mixture models * Treatment of interval-censored and truncated data * Multivariate lifetimes and event history models * Resampling and simulation methodology

Methods and Models in Statistics

Niall M. Adams
Methods and Models in Statistics Book PDF

Author : Niall M. Adams
Publisher : World Scientific
Page : 261 pages
File Size : 46,5 Mb
Release : 2004
Category : Mathematics
ISBN : 9781860944635

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Methods and Models in Statistics by Niall M. Adams Pdf

John Nelder was one of the most influential statisticians of his generation, having made an impact on many parts of the discipline. This book contains reviews of some of those areas, written by top researchers. It is accessible to non-specialists, and is noteworthy for its breadth of coverage.

R for Data Science

Hadley Wickham,Garrett Grolemund
R for Data Science Book PDF

Author : Hadley Wickham,Garrett Grolemund
Publisher : "O'Reilly Media, Inc."
Page : 521 pages
File Size : 55,8 Mb
Release : 2016-12-12
Category : Computers
ISBN : 9781491910368

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R for Data Science by Hadley Wickham,Garrett Grolemund Pdf

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Complex Models and Computational Methods in Statistics

Matteo Grigoletto,Francesco Lisi,Sonia Petrone
Complex Models and Computational Methods in Statistics Book PDF

Author : Matteo Grigoletto,Francesco Lisi,Sonia Petrone
Publisher : Springer Science & Business Media
Page : 228 pages
File Size : 54,7 Mb
Release : 2013-01-26
Category : Mathematics
ISBN : 9788847028715

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Complex Models and Computational Methods in Statistics by Matteo Grigoletto,Francesco Lisi,Sonia Petrone Pdf

The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.

Regression Methods in Biostatistics

Eric Vittinghoff,David V. Glidden,Stephen C. Shiboski,Charles E. McCulloch
Regression Methods in Biostatistics Book PDF

Author : Eric Vittinghoff,David V. Glidden,Stephen C. Shiboski,Charles E. McCulloch
Publisher : Springer Science & Business Media
Page : 526 pages
File Size : 42,7 Mb
Release : 2012
Category : Education
ISBN : 9781461413523

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Regression Methods in Biostatistics by Eric Vittinghoff,David V. Glidden,Stephen C. Shiboski,Charles E. McCulloch Pdf

This fresh edition, substantially revised and augmented, provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics. The examples used, analyzed using Stata, can be applied to other areas.

Statistical Models and Methods for Financial Markets

Tze Leung Lai,Haipeng Xing
Statistical Models and Methods for Financial Markets Book PDF

Author : Tze Leung Lai,Haipeng Xing
Publisher : Springer Science & Business Media
Page : 363 pages
File Size : 52,6 Mb
Release : 2008-09-08
Category : Business & Economics
ISBN : 9780387778273

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Statistical Models and Methods for Financial Markets by Tze Leung Lai,Haipeng Xing Pdf

The idea of writing this bookarosein 2000when the ?rst author wasassigned to teach the required course STATS 240 (Statistical Methods in Finance) in the new M. S. program in ?nancial mathematics at Stanford, which is an interdisciplinary program that aims to provide a master’s-level education in applied mathematics, statistics, computing, ?nance, and economics. Students in the programhad di?erent backgroundsin statistics. Some had only taken a basic course in statistical inference, while others had taken a broad spectrum of M. S. - and Ph. D. -level statistics courses. On the other hand, all of them had already taken required core courses in investment theory and derivative pricing, and STATS 240 was supposed to link the theory and pricing formulas to real-world data and pricing or investment strategies. Besides students in theprogram,thecoursealso attractedmanystudentsfromother departments in the university, further increasing the heterogeneity of students, as many of them had a strong background in mathematical and statistical modeling from the mathematical, physical, and engineering sciences but no previous experience in ?nance. To address the diversity in background but common strong interest in the subject and in a potential career as a “quant” in the ?nancialindustry,thecoursematerialwascarefullychosennotonlytopresent basic statistical methods of importance to quantitative ?nance but also to summarize domain knowledge in ?nance and show how it can be combined with statistical modeling in ?nancial analysis and decision making. The course material evolved over the years, especially after the second author helped as the head TA during the years 2004 and 2005.

Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman,Jennifer Hill
Data Analysis Using Regression and Multilevel Hierarchical Models Book PDF

Author : Andrew Gelman,Jennifer Hill
Publisher : Cambridge University Press
Page : 654 pages
File Size : 43,6 Mb
Release : 2007
Category : Mathematics
ISBN : 052168689X

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Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman,Jennifer Hill Pdf

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.