Описание: 1. Introduction.- 2. Estimation.- 3. Testing.- 4. One-Way ANOVA.- 5. Multiple Comparison Techniques.- 6. Regression Analysis.- 7. Multifactor Analysis of Variance.- 8. Experimental Design Models.- 9. Analysis of Covariance.- 10. General Gauss-Markov Models.- 11. Split Plot Models.- 12. Model Diagnostics.- 13. Collinearity and Alternative Estimates.- 14. Variable Selection.- Appendix A - 6.- References.- Index.- Author Index.
Автор: Jammalamadaka S Rao, Sengupta Debasis Название: Linear Models And Regression With R: An Integrated Approach ISBN: 9811200408 ISBN-13(EAN): 9789811200403 Издательство: World Scientific Publishing Рейтинг: Цена: от 6763.00 р. Наличие на складе: Есть
Описание:
Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same statistical ideas can be used to explore the more general linear model with rank-deficient design and/or covariance matrices. The unified treatment presented here provides a clearer understanding of the general linear model from a statistical perspective, thus avoiding the complex matrix-algebraic arguments that are often used in the rank-deficient case. Elegant geometric arguments are used as needed.
The book has a very broad coverage, from illustrative practical examples in Regression and Analysis of Variance alongside their implementation using R, to providing comprehensive theory of the general linear model with 181 worked-out examples, 227 exercises with solutions, 152 exercises without solutions (so that they may be used as assignments in a course), and 320 up-to-date references.
This completely updated and new edition of Linear Models: An Integrated Approach includes the following features:
Applications with data sets, and their implementation in R,
Comprehensive coverage of regression diagnostics and model building,
Coverage of other special topics such as collinearity, stochastic and inequality constraints, misspecified models, etc.,
Use of simple statistical ideas and interpretations to explain advanced concepts, and simpler proofs of many known results,
Discussion of models covering mixed-effects/variance components, spatial, and time series data with partially unknown dispersion matrix,
Thorough treatment of the singular linear model, including the case of multivariate response,
Insight into updates in the linear model, and their connection with diagnostics, design, variable selection, Kalman filter, etc.,
Extensive discussion of the foundations of linear inference, along with linear alternatives to least squares.
Автор: Ronald Christensen Название: Plane Answers to Complex Questions ISBN: 1441929711 ISBN-13(EAN): 9781441929716 Издательство: Springer Рейтинг: Цена: 12571.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The authors emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas.
Автор: Ronald Christensen Название: Plane Answers to Complex Questions ISBN: 1461428858 ISBN-13(EAN): 9781461428855 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This updated textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author`s emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models.
Автор: Heidi H. Andersen; Malene Hojbjerre; Dorte Sorense Название: Linear and Graphical Models ISBN: 0387945210 ISBN-13(EAN): 9780387945217 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides an account of graphical models for multivariate complex normal distributions. Beginning with an introduction to the multivariate complex normal distribution, the authors develop the marginal and conditional distributions of random vectors and matrices. Then they introduce complex MANOVA models and hypothesis testing for these models.
Автор: Salkind Neil J. Название: 100 Questions (and Answers) about Statistics ISBN: 1452283389 ISBN-13(EAN): 9781452283388 Издательство: Sage Publications Рейтинг: Цена: 5859.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book summarizes the most important questions about basic statistics from the simple understanding of summary statistics to more sophisticated inferential statistics
Автор: Faraway, Julian J. Название: Linear models with python ISBN: 1138483958 ISBN-13(EAN): 9781138483958 Издательство: Taylor&Francis Рейтинг: Цена: 13779.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Linear Models with Python offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using Python
Автор: Wu, Lang Название: Mixed Effects Models for Complex Data ISBN: 0367384914 ISBN-13(EAN): 9780367384913 Издательство: Taylor&Francis Рейтинг: Цена: 9798.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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
Автор: Banisch Sven Название: Markov Chain Aggregation for Agent-Based Models ISBN: 3319796917 ISBN-13(EAN): 9783319796918 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This self-contained text develops a Markov chain approach that makes the rigorous analysis of a class of microscopic models that specify the dynamics of complex systems at the individual level possible.
Автор: Dunn Название: Generalized linear models with examples ISBN: 1441901175 ISBN-13(EAN): 9781441901170 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics.
Описание: This book enables readers to understand, model, and predict complex dynamical systems using new methods with stochastic tools. The author presents a unique combination of qualitative and quantitative modeling skills, novel efficient computational methods, rigorous mathematical theory, as well as physical intuitions and thinking. An emphasis is placed on the balance between computational efficiency and modeling accuracy, providing readers with ideas to build useful models in practice. Successful modeling of complex systems requires a comprehensive use of qualitative and quantitative modeling approaches, novel efficient computational methods, physical intuitions and thinking, as well as rigorous mathematical theories. As such, mathematical tools for understanding, modeling, and predicting complex dynamical systems using various suitable stochastic tools are presented. Both theoretical and numerical approaches are included, allowing readers to choose suitable methods in different practical situations. The author provides practical examples and motivations when introducing various mathematical and stochastic tools and merges mathematics, statistics, information theory, computational science, and data science. In addition, the author discusses how to choose and apply suitable mathematical tools to several disciplines including pure and applied mathematics, physics, engineering, neural science, material science, climate and atmosphere, ocean science, and many others. Readers will not only learn detailed techniques for stochastic modeling and prediction, but will develop their intuition as well. Important topics in modeling and prediction including extreme events, high-dimensional systems, and multiscale features are discussed.
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