Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression and Survival Analysis (2nd Edition)
By James E. Helmreich. 2016
1,667 citations recorded by the source corpus.
Regression Modeling Strategies is an advanced text, aimed at graduate students and re-searchers with a solid, comprehensive background in regression modeling. Readers should have a good working knowledge of regression analysis as well as R as all code is written for that software. The extensive graphics are in color and make use of the package ggplot2 . There is a companion package rms ; Hmisc (Harrell’s well-known miscellaneous package) is used frequently as well. All R commands for analyses are in the text and available at the companion website. There are extensive notes on further reading as well as exercises for students at the end of each chapter.
Subfields: Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/ed78361e546a1ba562dcb8fbd5d93f1627a4cba4
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In Management Science and Operations Research
- Fotios Petropoulos
- Rob J Hyndman
- Konstantinos Nikolopoulos
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Spyros Makridakis
- Evangelos Spiliotis
- Christoph Bergmeir
Citations in the corpus
- Developing clinical prediction models: a step-by-step guide (2024, positive): “55 67 68 If variable selection is desirable—for instance, to simplify the implementation of the model by further reducing the number of predetermined predictors— more suitable methods can be used as described below.”
- Finding Optimal Normalizing Transformations via bestNormalize (2021, background): “Transform-both-sides (TBS) regression has several benefits that have been explored thoroughly elsewhere (see Harrell (2015) for an overview).”
- Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes (2018, methodology): “Now we can use equation (15) to obtain:”
- Forecasting: theory and practice (2020, background): “However, goodness-of-fit should not be confused with forecastability (Harrell, 2015).”
- Ethical Machine Learning in Healthcare (2020, background): “Weaknesses of stepwise regressions include the misleading nature of p-values and the final set depending on if and when features were considered (109).”
- Perceived Overqualification and Collectivism Orientation: Implications for Work and Nonwork Outcomes (2020, result): “Using a small sample size may also lead to overfitting (Harrell, 2001) and limit the generalizability of findings because our results could reflect idiosyncratic sample characteristics.”
- The development of theta and alpha neural oscillations from ages 3 to 24 years (2021, methodology): “We fit clustered regression models using the rms package (Harrell, 2015) in R v3.”