A Primer on Linear Models, Monahan, John F.

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: 186330.00 T
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.

Автор: Hoffmann John P.
Название: Linear Regression Models: Applications in R
ISBN: 0367753669 ISBN-13(EAN): 9780367753665
Издательство: Taylor&Francis
Рейтинг:
Цена: 74510.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book includes chapters on specifying the correct linear regression model, adjusting for measurement error, understanding the effects of influential observations, and using multilevel data.

Автор: West, Brady T. (university Of Michigan, Ann Arbor, Usa) Welch, Kathleen B. (university Of Michigan, Ann Arbor, Usa) Galecki, Andrzej T (university Of
Название: Linear mixed models
ISBN: 1032019328 ISBN-13(EAN): 9781032019321
Издательство: Taylor&Francis
Рейтинг:
Цена: 91860.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The third edition provides a comprehensive update of the available tools for fitting linear mixed-effects models in the newest versions of SAS, SPSS, R, Stata, and HLM. There is a focus on new tools for visualization of results and interpretation. New conceptual and theoretical developments in mixed-effects modeling have been included

Название: An Introduction to Generalized Linear Models, Third Edition
ISBN: 1584889500 ISBN-13(EAN): 9781584889502
Издательство: Taylor&Francis
Рейтинг:
Цена: 47970.00 T
Наличие на складе: Нет в наличии.
Описание: Offers a cohesive framework for statistical modeling. Emphasizing numerical and graphical methods, this work enables readers to understand the unifying structure that underpins GLMs. It discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, and longitudinal analysis.

Автор: Madsen, Henrik
Название: Introduction to General and Generalized Linear Models
ISBN: 1420091557 ISBN-13(EAN): 9781420091557
Издательство: Taylor&Francis
Рейтинг:
Цена: 71450.00 T
Наличие на складе: Нет в наличии.

Автор: M. Schomaker; C. Radhakrishna Rao; Helge Toutenbur
Название: Linear Models and Generalizations
ISBN: 3642093531 ISBN-13(EAN): 9783642093531
Издательство: Springer
Рейтинг:
Цена: 93130.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Third Edition explores the theory and applications of linear models. It presents a unified theory of inference from linear models and its generalizations with minimal assumptions, using least squares theory and alternative methods of estimation and testing.

Автор: Ronald Christensen
Название: Plane Answers to Complex Questions
ISBN: 1441929711 ISBN-13(EAN): 9781441929716
Издательство: Springer
Рейтинг:
Цена: 83810.00 T
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: Karl-Rudolf Koch
Название: Parameter Estimation and Hypothesis Testing in Linear Models
ISBN: 3642084613 ISBN-13(EAN): 9783642084614
Издательство: Springer
Рейтинг:
Цена: 97780.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Readers will find here presentations of the Gauss-Markoff model, the analysis of variance, the multivariate model, the model with unknown variance and covariance components and the regression model as well as the mixed model for estimating random parameters.

Автор: Molloy Graham
Название: Regression Analysis and Linear Models
ISBN: 1635497051 ISBN-13(EAN): 9781635497052
Издательство: Неизвестно
Цена: 163070.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.

Автор: Kaufman Robert L.
Название: Interaction Effects in Linear and Generalized Linear Models: Examples and Applications Using Stata
ISBN: 150636537X ISBN-13(EAN): 9781506365374
Издательство: Sage Publications
Рейтинг:
Цена: 120390.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approach to interpreting interaction effects.

Автор: Broemeling,
Название: Bayesian Analysis of Linear Models
ISBN: 0367451743 ISBN-13(EAN): 9780367451745
Издательство: Taylor&Francis
Рейтинг:
Цена: 63280.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents the basic theory of linear models from a Bayesian viewpoint. It is unique in that time series models such as autoregressive moving average processes are treated as linear models in the same way the general linear model is examined.

Автор: Vittinghoff
Название: Regression Methods in Biostatistics
ISBN: 0387202757 ISBN-13(EAN): 9780387202754
Издательство: Springer
Рейтинг:
Цена: 75420.00 T
Наличие на складе: Поставка под заказ.
Описание: An introduction to the multipredictor regression methods widely used in biostatistics. This book covers linear models for continuous outcomes; logistic models for binary outcomes; the Cox model for right-censored survival times; repeated-measures models for longitudinal and hierarchical outcomes; and linear models for counts and other outcomes.