Автор: Hooshang Nayebi Название: Advanced Statistics for Testing Assumed Causal Relationships ISBN: 3030547531 ISBN-13(EAN): 9783030547530 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: Schroeder Larry D., Sjoquist David L., Stephan Pau Название: Understanding Regression Analysis: An Introductory Guide ISBN: 1506332889 ISBN-13(EAN): 9781506332888 Издательство: Sage Publications Рейтинг: Цена: 5859.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Presents the fundamentals of regression analysis, from its meaning to uses, in a concise, easy-to-read, and non-technical style.
Автор: Westfall, Peter , Arias, Andrea L. Название: Understanding Regression Analysis ISBN: 0367493519 ISBN-13(EAN): 9780367493516 Издательство: Taylor&Francis Рейтинг: Цена: 6583.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks and decision trees under a common umbrella; namely, the conditional distribution model.
Автор: Saleh Название: Theory of Ridge Regression Estimators with Applica tions ISBN: 1118644611 ISBN-13(EAN): 9781118644614 Издательство: Wiley Рейтинг: Цена: 16466.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book discusses current methods of estimation in linearmodels. In particular, the authors address the methodology oflinear multiple regression models that plays an important role inalmost every scientific investigations in various fields, including economics, engineering, and biostatistics. Thestandard estimation method for regression parameters is theordinary least square (OLS) principal where residual squared errorsare minimized. Applied statisticians are often not satisfied withOLS estimators when the design matrix is ill-conditioned, leadingto a multicollinearity problem and large variances that make the"prediction" inaccurate. This book details theridge regression estimator, which was developed to combat themulticollinearity problem. Another estimator, called theLiu-estimator due to Liu Kejian, is also addressed since itprovides a competing resolution to the multicollinearityproblem. The ridge regression estimators are complicatednon-linear functions of the ridge parameter, whereas, theLiu estimators are a linear function of the shrinkage parameter.With a focus on the ridge regression and LIU estimators, this bookprovides expanded coverage beyond the usual preliminary test andStein type estimator. In this case, we propose a class of compositeestimators that are obtained by multiplying the OLS, restrictedOLS, preliminary test OLS, and Stein-type OLS by the "ridgefactor" and "Liu-factor." This research is asignificant step towards the study of dominance properties as wellas the comparison of the extent of LASSO properties. In addition, research materials involving shrinkage and model selection forlinear regression models are provided. Topical coverageincludes: preliminaries; linear regression models; multipleregression models; measurement error models; generalized linearmodels; and autoregressive Gaussian processes.
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: Stanley, T.D. Название: Meta-Regression Analysis in Economics and Business ISBN: 0415670780 ISBN-13(EAN): 9780415670784 Издательство: Taylor&Francis Рейтинг: Цена: 23734.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Ashish Sen; Muni Srivastava Название: Regression Analysis ISBN: 1461287898 ISBN-13(EAN): 9781461287896 Издательство: Springer Рейтинг: Цена: 12571.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An up-to-date, rigorous, and lucid treatment of the theory, methods, and applications of regression analysis, and thus ideally suited for those interested in the theory as well as those whose interests lie primarily with applications.
Автор: John O. Rawlings; Sastry G. Pantula; David A. Dick Название: Applied Regression Analysis ISBN: 147577155X ISBN-13(EAN): 9781475771558 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. This book is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an appied regression course to graduate students. This book seves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to statistical methods and a thoeretical linear models course. This book emphasizes the concepts and the analysis of data sets. It provides a review of the key concepts in simple linear regression, matrix operations, and multiple regression. Methods and criteria for selecting regression variables and geometric interpretations are discussed. Polynomial, trigonometric, analysis of variance, nonlinear, time series, logistic, random effects, and mixed effects models are also discussed. Detailed case studies and exercises based on real data sets are used to reinforce the concepts. John O. Rawlings, Professor Emeritus in the Department of Statistics at North Carolina State University, retired after 34 years of teaching, consulting, and research in statistical methods. He was instrumental in developing, and for many years taught, the course on which this text is based. He is a Fellow of the American Statistical Association and the Crop Science Society of America. Sastry G. Pantula is Professor and Directory of Graduate Programs in the Department of Statistics at North Carolina State University. He is a member of the Academy of Outstanding Teachers at North Carolina State University. David A. Dickey is Professor of Statistics at North Carolina State University. He is a member of the Academy of Outstanding Teachers at North Carolina State University.
Описание: This richly illustrated book presents the objectives of, and the latest techniques for, the identifiability analysis and standard and robust regression analysis of complex dynamical models.
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: J.D. Jobson Название: Applied Multivariate Data Analysis ISBN: 1461269601 ISBN-13(EAN): 9781461269601 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: Onyiah, Leonard C. Название: Design and Analysis of Experiments ISBN: 0367387077 ISBN-13(EAN): 9780367387075 Издательство: Taylor&Francis Рейтинг: Цена: 9798.00 р. Наличие на складе: Поставка под заказ.
Описание:
Unlike other books on the modeling and analysis of experimental data, Design and Analysis of Experiments: Classical and Regression Approaches with SAS not only covers classical experimental design theory, it also explores regression approaches. Capitalizing on the availability of cutting-edge software, the author uses both manual methods and SAS programs to carry out analyses.
The book presents most of the different designs covered in a typical experimental design course. It discusses the requirements for good experimentation, the completely randomized design, the use of orthogonal contrast to test hypotheses, and the model adequacy check. With an emphasis on two-factor factorial experiments, the author analyzes repeated measures as well as fixed, random, and mixed effects models. He also describes designs with randomization restrictions, before delving into the special cases of the 2k and 3k factorial designs, including fractional replication and confounding. In addition, the book covers response surfaces, balanced incomplete block and hierarchical designs, ANOVA, ANCOVA, and MANOVA.
Fortifying the theory and computations with practical exercises and supplemental material, this distinctive text provides a modern, comprehensive treatment of experimental design and analysis.
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