Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can D...
source link: https://academic.oup.com/esr/article-abstract/26/1/67/540767
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do About It
Abstract
Logistic regression estimates do not behave like linear regression estimates in one important respect: They are affected by omitted variables, even when these variables are unrelated to the independent variables in the model. This fact has important implications that have gone largely unnoticed by sociologists. Importantly, we cannot straightforwardly interpret log-odds ratios or odds ratios as effect measures, because they also reflect the degree of unobserved heterogeneity in the model. In addition, we cannot compare log-odds ratios or odds ratios for similar models across groups, samples, or time points, or across models with different independent variables in a sample. This article discusses these problems and possible ways of overcoming them.
Purchase
Subscription prices and ordering for this journal
Short-term Access
To purchase short term access, please sign in to your Oxford Academic account above.
Don't already have an Oxford Academic account? Register
Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do About It - 24 Hours access
Rental
Recommend
-
78
在本篇文章中，我們將以 Ranking 階段常用的方法之一：Logistic Regression 邏輯迴歸為例，利用 Apache Spark 的 Logistic Regression 模型建立一個 GitHub repositories 的推薦系統，以用戶對 repo 的打星紀錄和用戶與 repo 的各項屬性做為特徵，預測出用戶會不會...
-
41
Efficient Logistic Regression on Large Encrypted Data Kyoohyung Han and Seungwan Hong and Jung Hee Cheon and Daejun Park Abstract: Machine learning on encrypted data is a cryptographic method fo...
-
63
-
58
-
58
-
22
There’s already a bunch of amazing articles and videos on Logistic Regression, but it was a struggle for me to understand the connection between the probabilities and the linearity of Logistic, so I fig...
-
27
In this post we'll take a helpful look at the relationship between Bayes' Theroem and logistic regression. Despite being a very commonly used tool in statistics, machine learning and data science, I've found people frequen...
-
36
How likely am I to subscribe a term deposit? Posterior probability, credible interval, odds ratio, WAIC
-
15
本文简单介绍一下Logistic Regression的定义和原理。对于(Linear Regression)线性回归模型，输入$x$，网络参数为$w$和$b$，输出值为$y$，是个连续值。但是分类问题最终的输出值应该为离散的，那么如何转化为分类问题呢？ 可以考虑...
-
33
Logistic Regression — Idea and Application
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK