Statistical Modeling: The Two Cultures. Breiman, L. 16(3):199–231. ![Statistical Modeling: The Two Cultures [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated bya given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical communityhas been committed to the almost exclusive use of data models. This commit- ment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current prob- lems. Algorithmic modeling, both in theoryand practice, has developed rapidlyin fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move awayfrom exclusive dependence on data models and adopt a more diverse set of tools.
@article{breimanStatisticalModelingTwo2001,
title = {Statistical Modeling: The Two Cultures},
author = {Breiman, Leo},
date = {2001},
journaltitle = {Statistical Science},
volume = {16},
number = {3},
pages = {199--231},
url = {http://www.projecteuclid.org/Dienst/UI/1.0/Summarize/euclid.ss/1009213726},
abstract = {There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated bya given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical communityhas been committed to the almost exclusive use of data models. This commit- ment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current prob- lems. Algorithmic modeling, both in theoryand practice, has developed rapidlyin fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move awayfrom exclusive dependence on data models and adopt a more diverse set of tools.},
file = {/home/alexis/Zotero/storage/BGRBUJ92/Statistical modeling - 2001.pdf}
}
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