Applied Logistic Regression, Second Edition
By David W. Hosmer Jr., S. Lemeshow. 1989
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Jorge M. Lobo
- A. Jiménez‐Valverde
- Joaquín Hortal
- Wei-Long Zheng
- Tomás Václavík
- V. Vredenburg
- Jean-Pierre Hubaux
- C. Davidson
Citations in the corpus
- Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Most authors agree on the different techniques used to analyze and express the soundness of LR [174, 175, 176, 177], including the overall model evaluation, statistical tests of individual predictors, goodness-of-fit statistics and validation of the predicted probabilities.”
- Corporate Governance and Financial Distress: Evidence from Taiwan (2004, unknown): “Hosmer and Lemeshow (1989) provided a thorough discussion of binary logistical regression.”
- Measuring Securities Litigation Risk (2012, methodology): “We use a number of alternative approaches suggested in the statistics literature (e.g., Hosmer and Lemeshow, 2000; hereafter H&L) to evaluate model fit and predictive ability, most notably the receiver operating characteristic (ROC) curve.”
- Linear versus logistic regression when the dependent variable is a dichotomy (2009, methodology): “Hosmer and Lemeshow (1989), Applied Logistic Regression (New York: John Wiley & Sons). where the phenomena studied usually occur more frequently, this interpretation often becomes misleading.”
- Mapping the Decision to Quit: A Refinement and Test of the Unfolding Model of Voluntary Turnover (2008, unknown): “Goodness-of-fit was assessed using change in log likelihood (Hosmer & Lemeshow, 1989).”
- Dead wood threshold values for the three-toed woodpecker presence in boreal and sub-alpine forest (2004, methodology): “Because of the binary nature of the response variable for the three-toed woodpecker (‘‘presence’’ and ‘‘absence’’, coded as 1 and 0), logistic regression (Hosmer and Lemeshow, 1989) was chosen as the appropriate method to evaluate the existence of dead wood threshold values.”
- The promise of implementing machine learning in earthquake engineering: A state-of-the-art review (2020, background): “Additional information about LR is available in the literature (Hosmer and Lemeshow, 2000).”