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Titlebook: Regression Modeling Strategies; With Applications to Frank E. Harrell , Jr. Textbook 2015Latest edition Springer Nature Switzerland AG 2015

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書目名稱Regression Modeling Strategies
副標(biāo)題With Applications to
編輯Frank E. Harrell , Jr.
視頻videohttp://file.papertrans.cn/826/825516/825516.mp4
概述Fully revised new edition features new material and color figures.Published with mature, supplementary R package: rms.New chapters and sections on generalized least squares for analysis of serial resp
叢書名稱Springer Series in Statistics
圖書封面Titlebook: Regression Modeling Strategies; With Applications to Frank E. Harrell , Jr. Textbook 2015Latest edition Springer Nature Switzerland AG 2015
描述.This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modelling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasises problem solving strategies that address the many issues arising when developing multi-variable models using real data and not standard textbook examples.?.Regression Modelling?Strategies .presents full-scale case studies of non-trivial data-sets?instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation and interpretation tasks described in the book relatively easy to do. 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.?A new emphasis is given to the robust anal
出版日期Textbook 2015Latest edition
關(guān)鍵詞Generalized least squares; Linear models; Logistic regression; Predictive modeling; R statistical softwa
版次2
doihttps://doi.org/10.1007/978-3-319-19425-7
isbn_softcover978-3-319-33039-6
isbn_ebook978-3-319-19425-7Series ISSN 0172-7397 Series E-ISSN 2197-568X
issn_series 0172-7397
copyrightSpringer Nature Switzerland AG 2015
The information of publication is updating

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Case Study in Ordinal Regression, Data Reduction, and Penalization,ical signs and symptoms were used to develop a predictive model for an ordinal response. This response consists of laboratory assessments of diagnosis and severity of illness related to pneumonia, meningitis, and sepsis.
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Missing Data,There are missing data in the majority of datasets one is likely to encounter. Before discussing some of the problems of analyzing data in which some variables are missing for some subjects, we define some nomenclature.
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Case Study in Data Reduction,Recall that the aim of data reduction is to reduce (without using the outcome) the number of parameters needed in the outcome model.
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Regression Models for Continuous , and Case Study in Ordinal Regression,This chapter concerns univariate continuous .. There are many multivariable models for predicting such response variables, such as
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Frank E. Harrell , Jr.Fully revised new edition features new material and color figures.Published with mature, supplementary R package: rms.New chapters and sections on generalized least squares for analysis of serial resp
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