Regression Basics, Kahane, Leo H.

Автор: David Melamed, Eric W. Schoon, Ronald L. Breiger
Название: Regression Inside Out
ISBN: 1108744885 ISBN-13(EAN): 9781108744881
Издательство: Cambridge Academ
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Цена: 27450.00 T
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Описание: Linear regression analysis, with its many generalizations, is the predominant quantitative method used throughout the social sciences and beyond. The goal of the method is to study relations among variables. In this book, Schoon, Melamed and Breiger turn regression modeling inside out to put the emphasis on the cases (people, organizations, and nations) that comprise the variables. By re-analyzing influential published research, they reveal new insights and present a principled way to unlock a set of more nuanced interpretations than has previously been attainable. The emphasis is on intuition and examples that can be reproduced using the code and datasets provided. Relating their contributions to methodologies that operate under quite different philosophical assumptions, the authors advance multi-method social science and help to bridge the divide between quantitative and qualitative research. The result is a modern, accessible, and innovative take on extracting knowledge from data.

Автор: Hooshang Nayebi
Название: Advanced Statistics for Testing Assumed Causal Relationships
ISBN: 3030547531 ISBN-13(EAN): 9783030547530
Издательство: Springer
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Цена: 74530.00 T
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Описание: It presents that potential effects of each independent variable on the dependent variable are not limited to direct and indirect effects. The path analysis shows each independent variable has a pure effect on the dependent variable. So, it can be shown the unique contribution of each independent variable to the variation of the dependent variable.

Автор: Fahrmeir Ludwig, Kneib Thomas, Lang Stefan
Название: Regression: Models, Methods and Applications
ISBN: 3662638819 ISBN-13(EAN): 9783662638811
Издательство: Springer
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Цена: 102480.00 T
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Описание: Now in its second edition, this textbook provides an applied and unified introduction to parametric, nonparametric 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 numerous examples and case studies. The most important definitions and statements are concisely summarized in boxes, and the underlying data sets and code are available online on the book’s dedicated website. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. The chapters address the classical linear model and its extensions, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression, quantile regression and distributional regression models. Two appendices describe the required matrix algebra, as well as elements of probability calculus and statistical inference. In this substantially revised and updated new edition the overview on regression models has been extended, and now includes the relation between regression models and machine learning, additional details on statistical inference in structured additive regression models have been added and a completely reworked chapter augments the presentation of quantile regression with a comprehensive introduction to distributional regression models. Regularization approaches are now more extensively discussed in most chapters of the book. 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 at an intermediate mathematical level and assumes only knowledge of basic probability, calculus, matrix algebra and statistics.

Автор: Wakefield
Название: Bayesian and Frequentist Regression Methods
ISBN: 1441909249 ISBN-13(EAN): 9781441909244
Издательство: Springer
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Цена: 102480.00 T
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Описание: Bayesian and Frequentist Regression Methods provides a modern account of both Bayesian and frequentist methods of regression analysis.

Автор: Miles J & Shevlin M
Название: Applying Regression and Correlation
ISBN: 0761962301 ISBN-13(EAN): 9780761962304
Издательство: Sage Publications
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Цена: 58080.00 T
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Описание: By introducing the reader to regression analysis through a simple model-building approach, this book takes a fresh look at applying regression analysis in the behavioural sciences.

Автор: Mohanty
Название: Swarm Intelligence Methods for Statistical Regression
ISBN: 1138558184 ISBN-13(EAN): 9781138558182
Издательство: Taylor&Francis
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Цена: 60220.00 T
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Описание: Swarm Intelligence Methods for Statistical Regression describes methods from the field of computational swarm intelligence (SI), and how they can be used to overcome the optimization bottleneck encountered in statistical analysis.

Автор: Kahane, Leo H.
Название: Regression basics
ISBN: 1032393181 ISBN-13(EAN): 9781032393186
Издательство: Taylor&Francis
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Цена: 54090.00 T
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Автор: Keith
Название: Multiple Regression and Beyond
ISBN: 1138061441 ISBN-13(EAN): 9781138061446
Издательство: Taylor&Francis
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Цена: 86760.00 T
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Описание: Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods.

Автор: Clive Loader
Название: Local Regression and Likelihood
ISBN: 1475772580 ISBN-13(EAN): 9781475772586
Издательство: Springer
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Цена: 149060.00 T
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Описание: Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation.

Автор: J.D. Jobson
Название: Applied Multivariate Data Analysis
ISBN: 1461269601 ISBN-13(EAN): 9781461269601
Издательство: Springer
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Цена: 46570.00 T
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Описание: An easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory.

Автор: Alexander Silbersdorff
Название: Analysing Inequalities in Germany
ISBN: 331965330X ISBN-13(EAN): 9783319653303
Издательство: Springer
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Цена: 51230.00 T
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Описание: This book seeks new perspectives on the growing inequalities that our societies face, putting forward Structured Additive Distributional Regression as a means of statistical analysis that circumvents the common problem of analytical reduction to simple point estimators.

Автор: Sengupta Ashis Et Al
Название: Statistical Paradigms: Recent Advances And Reconciliations
ISBN: 9814343951 ISBN-13(EAN): 9789814343954
Издательство: World Scientific Publishing
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Цена: 105600.00 T
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Описание: A collection of research articles on classical and emerging Statistical Paradigms - parametric, non-parametric and semi-parametric, frequentist and Bayesian - encompassing both theoretical advances and emerging applications in a variety of scientific disciplines.