Regression with Linear Predictors, Per Kragh Andersen; Lene Theil Skovgaard

Автор: Per Kragh Andersen; Lene Theil Skovgaard
Название: Regression with Linear Predictors
ISBN: 1441971696 ISBN-13(EAN): 9781441971692
Издательство: Springer
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Цена: 74530.00 T
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Описание: This is a book about regression analysis, that is, the situation in statistics where the distribution of a response (or outcome) variable is related to - planatory variables (or covariates). This is an extremely common situation in the application of statistical methods in many ?elds, andlinear regression, - gistic regression, and Cox proportional hazards regression are frequently used for quantitative, binary, and survival time outcome variables, respectively. Several books on these topics have appeared and for that reason one may well ask why we embark on writing still another book on regression. We have two main reasons for doing this: 1. First, we want to highlightsimilaritiesamonglinear, logistic, proportional hazards, andotherregressionmodelsthatincludealinearpredictor. These modelsareoftentreatedentirelyseparatelyintextsinspiteofthefactthat alloperationsonthemodelsdealingwiththelinearpredictorareprecisely the same, including handling of categorical and quantitative covariates, testing for linearity and studying interactions. 2. Second, we want to emphasize that, for any type of outcome variable, multiple regression models are composed of simple building blocks that areaddedtogetherinthelinearpredictor: thatis, t-tests, one-wayanalyses of variance and simple linear regressions for quantitative outcomes, 2 2, 2 (k+1) tables and simple logistic regressions for binary outcomes, and 2-and (k+1)-sample logrank testsand simple Cox regressionsfor survival data. Thishastwoconsequences. Allthesesimpleandwellknownmethods can be considered as special cases of the regression models. On the other hand, the e?ect of a single explanatory variable in a multiple regression model can be interpreted in a way similar to that obtained in the simple analysis, however, now valid only for the other explanatory variables in the model "held ?xed."

Автор: Christian Lucas
Название: Sponsor- and Country-Related Predictors of Sponsorship Effectiveness
ISBN: 3658076836 ISBN-13(EAN): 9783658076832
Издательство: Springer
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Цена: 52240.00 T
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Описание: Christian Lucas investigates the effectiveness of sports sponsorships. The first study analyses activation means by conducting a multi-level field study combining data about the sponsorship instruments of Bundesliga sponsors with fan perceptions.

Автор: Darlington Richard B., Hayes Andrew F.
Название: Regression Analysis and Linear Models: Concepts, Applications, and Implementation
ISBN: 1462521134 ISBN-13(EAN): 9781462521135
Издательство: Taylor&Francis
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Цена: 83690.00 T
Наличие на складе: Невозможна поставка.
Описание: Ephasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book`s examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable`s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.

Автор: Hechtman, Lily
Название: Attention Deficit Hyperactivity Disorder Adult Outcome and Its Predictors (Paperback)
ISBN: 0190213582 ISBN-13(EAN): 9780190213589
Издательство: Oxford Academ
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Цена: 72870.00 T
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Описание: Comprehensive description of adult outcome in educational, occupational, emotional, social, substance use, legal, antisocial functioning is described via the best well-controlled prospective follow-up studies of children with ADHD into adulthood.

Автор: Iasson Karafyllis; Miroslav Krstic
Название: Predictor Feedback for Delay Systems: Implementations and Approximations
ISBN: 3319423770 ISBN-13(EAN): 9783319423777
Издательство: Springer
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Цена: 102480.00 T
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Описание: Preview of Predictor Feedback and Delay Compensation.- Part I: Linear Systems Under Predictor Feedback.- Linear Systems with State Measurement.- Linear Systems with Output Measurement.- Part II: Nonlinear Systems Under Predictor Feedback.- Nonlinear Systems with State Measurement.- Nonlinear Systems with Output Measurement.- Application to the Chemostat.- Part III: Extensions of Predictor Feedback.- Systems Described by Integral Delay Equations.- Discrete-Time Systems.

Автор: Bin Zhou
Название: Truncated Predictor Feedback for Time-Delay Systems
ISBN: 3642542050 ISBN-13(EAN): 9783642542053
Издательство: Springer
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Цена: 158380.00 T
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Описание: A groundbreaking new work on the topic, this book includes detailed proofs, summaries of technical results, and solutions to the semi-global and global stabilization problems of linear time delay systems subject to energy and magnitude saturation constraints.

Автор: Olive, David.
Название: Linear regression /
ISBN: 3319552503 ISBN-13(EAN): 9783319552507
Издательство: Springer
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Цена: 83850.00 T
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Описание:

This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models.

This text is for graduates and undergraduates with a strong mathematical background. The prerequisites for this text are linear algebra and a calculus based course in statistics.

Автор: Ronald Christensen
Название: Log-Linear Models and Logistic Regression
ISBN: 1475771134 ISBN-13(EAN): 9781475771138
Издательство: Springer
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Цена: 83850.00 T
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Описание: As the new title indicates, this second edition of Log-Linear Models has been modi?ed to place greater emphasis on logistic regression. In addition to new material, the book has been radically rearranged. The fundamental material is contained in Chapters 1-4. Intermediate topics are presented in Chapters 5 through 8. Generalized linear models are presented in Ch- ter 9. The matrix approach to log-linear models and logistic regression is presented in Chapters 10-12, with Chapters 10 and 11 at the applied Ph.D. level and Chapter 12 doing theory at the Ph.D. level. The largest single addition to the book is Chapter 13 on Bayesian bi- mial regression. This chapter includes not only logistic regression but also probit and complementary log-log regression. With the simplicity of the Bayesian approach and the ability to do (almost) exact small sample s- tistical inference, I personally ?nd it hard to justify doing traditional large sample inferences. (Another possibility is to do exact conditional inference, but that is another story.) Naturally, Ihavecleaneduptheminor?awsinthetextthatIhavefound. All examples, theorems, proofs, lemmas, etc. are numbered consecutively within each section with no distinctions between them, thus Example 2.3.1 willcomebeforeProposition2.3.2.Exercisesthatdonotappearinasection at the end have a separate numbering scheme. Within the section in which it appears, an equation is numbered with a single value, e.g., equation (1)

Автор: W. Kraemer; H. Sonnberger
Название: The Linear Regression Model Under Test
ISBN: 3642958788 ISBN-13(EAN): 9783642958786
Издательство: Springer
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Цена: 102480.00 T
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Описание: Similar credits are due to Adrian Pagan, Roberto Mariano and Garry Phillips, the econometrics guest professors at the Institute in the 1982 - 1984 period, who through their lectures and advice have contributed greatly to our effort.