Titlebook: Regression; Models, Methods and Ludwig Fahrmeir,Thomas Kneib,Brian Marx Textbook 20131st edition Springer-Verlag Berlin Heidelberg 2013 ge

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obability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.978-3-642-34333-9

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It also describes suitable approaches to target these probes to specific cellular organelles, thereby enabling medical researchers to visualise sub-cellular oxidative stress levels, and addressing the typicall978-3-030-10371-2978-3-319-73405-7Series ISSN 2190-5053 Series E-ISSN 2190-5061

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Textbook 20131st edition application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and prese

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Categorical Regression Models,ilarly, voters choose between several parties or they assess the quality of candidates in ordered categories. In medicine, we may, for example, not only distinguish between “infection” and “no infection” but also between several types of infection, as in Example 6.1 below.

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Extensions of the Classical Linear Model, This model allows for correlated errors and heteroscedastic variances of the errors. Section 4.2 discusses several techniques to regularize the least squares estimator. Such a regularization may be useful in cases where the design matrix is highly collinear or even rank deficient. Moreover, regular

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Categorical Regression Models,ns, from social science to medicine, response variables often have more than two categories. For example, consumers may choose between different brands of a product or they may express their opinion about some product in ordered categories ranging from “very satisfied” to “not satisfied at all.” Sim