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Regression Modeling Strategies

With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis




Link zum Volltext (1-User Lizenz, Gekauft am 29.09.2022)

Statistics comprises among other areas study design, hypothesis testing, estimation, and prediction. This text aims at the last area, by presenting methods that enable an analyst to develop models that will make accurate predictions of responses for future observations. Prediction could be considered a superset of hypothesis testing and estimation, so the methods presented here will also assist the analyst in those areas. It is worth pausing to explain how this is so.

  • 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 response data, redundancy analysis, bootstrap confidence intervals for rankings of predictors, expanded material on multiple imputation and predictive mean matching and more


  • Lift Curve
  • Ordinal Logistic Model
  • Parametric Survival Model
  • Penalize Maximum Likelihood Estimation
  • Propensity Score