{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/top-down-transformation-choice","title":"Top-down Transformation Choice","arxiv_id":"1706.08269","date":"2017-06-26","proceeding":null,"authors":["Torsten Hothorn"],"abstract":"Simple models are preferred over complex models, but over-simplistic models\ncould lead to erroneous interpretations. The classical approach is to start\nwith a simple model, whose shortcomings are assessed in residual-based model\ndiagnostics. Eventually, one increases the complexity of this initial overly\nsimple model and obtains a better-fitting model. I illustrate how\ntransformation analysis can be used as an alternative approach to model choice.\nInstead of adding complexity to simple models, step-wise complexity reduction\nis used to help identify simpler and better-interpretable models. As an\nexample, body mass index distributions in Switzerland are modelled by means of\ntransformation models to understand the impact of sex, age, smoking and other\nlifestyle factors on a person's body mass index. In this process, I searched\nfor a compromise between model fit and model interpretability. Special emphasis\nis given to the understanding of the connections between transformation models\nof increasing complexity. The models used in this analysis ranged from\nevergreens, such as the normal linear regression model with constant variance,\nto novel models with extremely flexible conditional distribution functions,\nsuch as transformation trees and transformation forests.","url_abs":"http://arxiv.org/abs/1706.08269v2","url_pdf":"http://arxiv.org/pdf/1706.08269v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"top-down-transformation-choice","repo_url":"https://github.com/r-forge/ctm","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"linear-regression","method_name":"Linear Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}