Frank E. Harrell , Jr. 著
ISBN: 978-3-319-19424-0 (Print) 978-3-319-19425-7 (Online)

简介：This book links standard regression modeling approaches with

• methods for relaxing linearity assumptions that still allow one to easily obtain predictions and confidence limits for future observations, and to do formal hypothesis tests,

• non-additive modeling approaches not requiring the assumption that interactions are always linear × linear,

• methods for imputing missing data and for penalizing variances for incomplete data,

• methods for handling large numbers of predictors without resorting to problematic stepwise variable selection techniques,

• data reduction methods (unsupervised learning methods, some of which are based on multivariate psychometric techniques too seldom used in statistics) that help with the problem of“too many variables to analyze and not enough observations” as well as making the model more interpretable when there are predictor variables containing overlapping information,

• methods for quantifying predictive accuracy of a fitted model,

• powerful model validation techniques based on the bootstrap that allow the analyst to estimate predictive accuracy nearly unbiasedly without holding back data from the model development process, and

• graphical methods for understanding complex models.