♥ Book Title: Classical and Modern Regression with Applications♣ Name Author: Raymond H. Myers∞ Launching: 1990◊ Info ISBN Link: ⊗ Detail ISBN code: 168⊕ Number Pages: Total 488 sheet♮ News id: LOHHKQAACAAJ☯ Full Synopsis: 'Regression analysis is a vitally important statistical tool, with major advancements made by both practical data analysts and statistical theorists. In CLASSICAL AND MODERN REGRESSION WITH APPLICATIONS, Second Edition, Raymond H. Myers provides a solid foundation in classical regression, while introducing modern techniques. Throughout the text, a broad spectrum of applications are included from the physical sciences, engineering, biology, management, and economics.
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In robust statistics, robust regression is a form of regression analysis designed to overcome. Robust regression methods are designed to be not overly affected by violations of. Also, modern statistical software packages such as R, Statsmodels, Stata and S-PLUS. Create a book Download as PDF Printable version.
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'Article Raymond H. Myers Statement.' ♥ Book Title: Modern Multivariate Statistical Techniques♣ Name Author: Alan J.
Izenman∞ Launching: 2009-03-02◊ Info ISBN Link: ⊗ Detail ISBN code: 891⊕ Number Pages: Total 733 sheet♮ News id: 1CuznRORa3EC☯ Full Synopsis: 'This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before. 'Article Alan J. Izenman Statement.' ♥ Book Title: Generalized Linear Models♣ Name Author: Raymond H.
Myers∞ Launching: 2012-01-20◊ Info ISBN Link: 979⊗ Detail ISBN code: ⊕ Number Pages: Total 544 sheet♮ News id: LBiT207QLZgC☯ Full Synopsis: 'Praise for the First Edition 'The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities.'
—Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences. This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. ♥ Book Title: Multivariate Reduced-Rank Regression♣ Name Author: Raja Velu∞ Launching: 2013-04-17◊ Info ISBN Link: 538⊗ Detail ISBN code: ⊕ Number Pages: Total 258 sheet♮ News id: dsfSBwAAQBAJ☯ Full Synopsis: 'In the area of multivariate analysis, there are two broad themes that have emerged over time.
The analysis typically involves exploring the variations in a set of interrelated variables or investigating the simultaneous relation ships between two or more sets of variables. In either case, the themes involve explicit modeling of the relationships or dimension-reduction of the sets of variables. The multivariate regression methodology and its variants are the preferred tools for the parametric modeling and descriptive tools such as principal components or canonical correlations are the tools used for addressing the dimension-reduction issues.
Both act as complementary to each other and data analysts typically want to make use of these tools for a thorough analysis of multivariate data. A technique that combines the two broad themes in a natural fashion is the method of reduced-rank regres sion. This method starts with the classical multivariate regression model framework but recognizes the possibility for the reduction in the number of parameters through a restrietion on the rank of the regression coefficient matrix.
This feature is attractive because regression methods, whether they are in the context of a single response variable or in the context of several response variables, are popular statistical tools. The technique of reduced rank regression and its encompassing features are the primary focus of this book. The book develops the method of reduced-rank regression starting from the classical multivariate linear regression model.
'Article Raja Velu Statement.' ♥ Book Title: Modern Regression Methods♣ Name Author: Thomas P. Ryan∞ Launching: 2008-11-10◊ Info ISBN Link: 860⊗ Detail ISBN code: ⊕ Number Pages: Total 642 sheet♮ News id: edJPj2pqbMC☯ Full Synopsis: 'Over the years, I have had the opportunity to teach several regression courses, and I cannot think of a better undergraduate text than this one.' (The American Statistician) 'The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite.
I also highly recommend it to practitioners who want to solve real-life prediction problems.' (Computing Reviews) Modern Regression Methods, Second Edition maintains the accessible organization, breadth of coverage, and cutting-edge appeal that earned its predecessor the title of being one of the top five books for statisticians by an Amstat News book editor in 2003. This new edition has been updated and enhanced to include all-new information on the latest advances and research in the evolving field of regression analysis.
The book provides a unique treatment of fundamental regression methods, such as diagnostics, transformations, robust regression, and ridge regression. Unifying key concepts and procedures, this new edition emphasizes applications to provide a more hands-on and comprehensive understanding of regression diagnostics. ♥ Book Title: Regression♣ Name Author: Ludwig Fahrmeir∞ Launching: 2013-05-09◊ Info ISBN Link: 339⊗ Detail ISBN code: ⊕ Number Pages: Total 698 sheet♮ News id: EQxU9iJtipAC☯ Full Synopsis: 'The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application.
The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics.
The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference. 'Article Ludwig Fahrmeir Statement.' ♥ Book Title: Regression Models as a Tool in Medical Research♣ Name Author: Werner Vach∞ Launching: 2012-11-27◊ Info ISBN Link: 493⊗ Detail ISBN code: ⊕ Number Pages: Total 496 sheet♮ News id: HXvNBQAAQBAJ☯ Full Synopsis: 'While regression models have become standard tools in medical research, understanding how to properly apply the models and interpret the results is often challenging for beginners. Regression Models as a Tool in Medical Research presents the fundamental concepts and important aspects of regression models most commonly used in medical research, including the classical regression model for continuous outcomes, the logistic regression model for binary outcomes, and the Cox proportional hazards model for survival data. The text emphasizes adequate use, correct interpretation of results, appropriate presentation of results, and avoidance of potential pitfalls.
After reviewing popular models and basic methods, the book focuses on advanced topics and techniques. It considers the comparison of regression coefficients, the selection of covariates, the modeling of nonlinear and nonadditive effects, and the analysis of clustered and longitudinal data, highlighting the impact of selection mechanisms, measurement error, and incomplete covariate data. The text then covers the use of regression models to construct risk scores and predictors. It also gives an overview of more specific regression models and their applications as well as alternatives to regression modeling. The mathematical details underlying the estimation and inference techniques are provided in the appendices.
'Article Werner Vach Statement.' ♥ Book Title: Regression Modeling Strategies♣ Name Author: Frank E.
Harrell∞ Launching: 2013-03-09◊ Info ISBN Link: 621⊗ Detail ISBN code: 147573462X⊕ Number Pages: Total 572 sheet♮ News id: 7D0mBQAAQBAJ☯ Full Synopsis: 'Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with 'too many variables to analyze and not enough observations,' and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve 'safe data mining'. 'Article Frank E.
Harrell Statement.'
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