Автор: Erik W. Grafarend , Silvelyn Zwanzig , Joseph L. Awange Название: Applications of Linear and Nonlinear Models ISBN: 3030945979 ISBN-13(EAN): 9783030945978 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides numerous examples of linear and nonlinear model applications. Here, we present a nearly complete treatment of the Grand Universe of linear and weakly nonlinear regression models within the first 8 chapters. Our point of view is both an algebraic view and a stochastic one. For example, there is an equivalent lemma between a best, linear uniformly unbiased estimation (BLUUE) in a Gauss–Markov model and a least squares solution (LESS) in a system of linear equations. While BLUUE is a stochastic regression model, LESS is an algebraic solution. In the first six chapters, we concentrate on underdetermined and overdetermined linear systems as well as systems with a datum defect. We review estimators/algebraic solutions of type MINOLESS, BLIMBE, BLUMBE, BLUUE, BIQUE, BLE, BIQUE, and total least squares. The highlight is the simultaneous determination of the first moment and the second central moment of a probability distribution in an inhomogeneous multilinear estimation by the so-called E-D correspondence as well as its Bayes design. In addition, we discuss continuous networks versus discrete networks, use of Grassmann–Plucker coordinates, criterion matrices of type Taylor–Karman as well as FUZZY sets. Chapter seven is a speciality in the treatment of an overjet. This second edition adds three new chapters: (1) Chapter on integer least squares that covers (i) model for positioning as a mixed integer linear model which includes integer parameters. (ii) The general integer least squares problem is formulated, and the optimality of the least squares solution is shown. (iii) The relation to the closest vector problem is considered, and the notion of reduced lattice basis is introduced. (iv) The famous LLL algorithm for generating a Lovasz reduced basis is explained. (2) Bayes methods that covers (i) general principle of Bayesian modeling. Explain the notion of prior distribution and posterior distribution. Choose the pragmatic approach for exploring the advantages of iterative Bayesian calculations and hierarchical modeling. (ii) Present the Bayes methods for linear models with normal distributed errors, including noninformative priors, conjugate priors, normal gamma distributions and (iii) short outview to modern application of Bayesian modeling. Useful in case of nonlinear models or linear models with no normal distribution: Monte Carlo (MC), Markov chain Monte Carlo (MCMC), approximative Bayesian computation (ABC) methods. (3) Error-in-variables models, which cover: (i) Introduce the error-in-variables (EIV) model, discuss the difference to least squares estimators (LSE), (ii) calculate the total least squares (TLS) estimator. Summarize the properties of TLS, (iii) explain the idea of simulation extrapolation (SIMEX) estimators, (iv) introduce the symmetrized SIMEX (SYMEX) estimator and its relation to TLS, and (v) short outview to nonlinear EIV models. The chapter on algebraic solution of nonlinear system of equations has also been updated in line with the new emerging field of hybrid numeric-symbolic solutions to systems of nonlinear equations, ermined system of nonlinear equations on curved manifolds. The von Mises–Fisher distribution is characteristic for circular or (hyper) spherical data. Our last chapter is devoted to probabilistic regression, the special Gauss–Markov model with random effects leading to estimators of type BLIP and VIP including Bayesian estimation. A great part of the work is presented in four appendices. Appendix A is a treatment, of tensor algebra, namely linear algebra, matrix algebra, and multilinear algebra. Appendix B is devoted to sampling distributions and their use in terms of confidence intervals and confidence regions. Appendix C reviews the elementary notions of statistics, namely random events and stochastic processes. Appendix D introduces the basics of Groebner basis algebra, its careful definition, the Buchberger algorithm, especially the C. F. Gauss combinatorial algorithm.
Название: Regression Modeling Strategies ISBN: 3319194240 ISBN-13(EAN): 9783319194240 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalised least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models and the Cox semi parametric survival model.
Автор: Sengupta Debasis, Jammalamadaka S. Rao Название: Linear Models and Regression with R: An Integrated Approach ISBN: 9811229287 ISBN-13(EAN): 9789811229282 Издательство: World Scientific Publishing Рейтинг: Цена: 14852.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Gill, Jefferson M. Torres Pacheco, Silvia Michelle Название: Generalized linear models ISBN: 1506387349 ISBN-13(EAN): 9781506387345 Издательство: Sage Publications Рейтинг: Цена: 5859.00 р. Наличие на складе: Поставка под заказ.
Описание: Explaining the theoretical underpinning of generalized linear models, this text enables researchers to decide how to select the best way to adapt their data for this type of analysis, with examples to illustrate the application of GLM.
Автор: Wolfgang H?rdle; Hua Liang; Jiti Gao Название: Partially Linear Models ISBN: 3790813001 ISBN-13(EAN): 9783790813005 Издательство: Springer Рейтинг: Цена: 16070.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing in statistics.
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: Jiang Jiming, Nguyen Thuan Название: Linear and Generalized Linear Mixed Models and Their Applications ISBN: 1071612840 ISBN-13(EAN): 9781071612842 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. Furthermore, it includes recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models.
Автор: Brinda W. D. Название: Visualizing Linear Models ISBN: 3030641694 ISBN-13(EAN): 9783030641696 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Designed to develop fluency with the underlying mathematics and to build a deep understanding of the principles, it`s an excellent basis for a one-semester course on statistical theory and linear modeling for intermediate undergraduates or graduate students. Three chapters gradually develop the essentials of linear model theory.
Автор: Farebrother, R.W. , Schyns, Michael Название: Visualizing Statistical Models And Concepts ISBN: 0367447053 ISBN-13(EAN): 9780367447052 Издательство: Taylor&Francis Рейтинг: Цена: 9798.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In this book, the author finds that many of the important concepts of mathematical statistics can be associated with physical models; and that the optimality criteria of statistical estimation procedures can often be interpreted in terms of the concept of potential energy.
Автор: Farebrother, R.W. Название: Visualizing Statistical Models And Concepts ISBN: 0824707184 ISBN-13(EAN): 9780824707187 Издательство: Taylor&Francis Рейтинг: Цена: 19906.00 р. Наличие на складе: Нет в наличии.
Описание: He does not simply give rules and advice, but bases these on general principles and provide a clear path between them This book is concerned with the graphical representation of time data and is written to cover a range of different users.
Автор: Antony Unwin; Martin Theus; Heike Hofmann Название: Graphics of Large Datasets ISBN: 149393869X ISBN-13(EAN): 9781493938698 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases, or large in numbers of variables, or large in both. All ideas are illustrated with displays from analyses of real datasets.
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