Selection of Regression Models for Health Care Data
Bibliographic Data
| ID | 9097948 |
|---|---|
| Authors | Susan D Horn (0000-0003-0199-4513, Johns Hopkins University, corresponding author), Charles E Yesalis (University of Iowa), Richard H Bartels (Johns Hopkins University) |
| Year | 1978 |
| Volume | 16 |
| Issue | 7 |
| Pages | 574-583 |
| Publication date | 1978-07-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/00005650-197807000-00004 |
| PMID | 672269 |
| OpenAlex | W2001057327 |
| Language | EN |
| References cited | 2 |
We discuss several linear and nonlinear regression models and their appropriateness in modeling a set of data from a cohort study of the use of medical services in a prepaid medical plan. Although computational considerations once forced researchers to use linear models almost exclusively, modern computer hardware and efficient algorithms have made it possible to use nonlinear models almost as easily as linear ones. Thus, in choosing a regression model, a researcher should give strong consideration to how well the model reflects (presumed) intrinsic properties of the phenomena under study, even if the consideration leads to the choice of a nonlinear model. Modern computer programs for nonlinear minimization should be employed to fit the model. The model should be judged on how well it describes the data and how reasonably it predicts the future
Data mining · Data set · Econometrics · Linear model · Linear regression · Machine learning · Mathematical optimization · Model selection · Nonlinear regression · Nonlinear system · Polynomial regression · Proper linear model · Regression · Regression analysis · Selection (genetic algorithm) · Set (abstract data type) · Statistics · Artificial Intelligence · Computer Science · Mathematics · Mental Health Research Topics
| Citation velocity | historical |
|---|---|
| Highly cited | No |