robust

Regression models for 2-timepoint non-experimental data

Purpose In the contemporary longitudinal data analysis literature, 2-timepoint data (a.k.a. pre/post data) get a bad wrap. Singer and Willett (2003, p. 10) described 2-timepoint data as only “marginally better” than cross-sectional data and Rogosa et al.

Bayesian robust correlations with brms (and why you should love Student's $t$)

[edited Apr 21, 2021] In this post, we’ll show how Student’s \(t\)-distribution can produce better correlation estimates when your data have outliers. As is often the case, we’ll do so as Bayesians.

Robust Linear Regression with Student’s $t$-Distribution

[edited Nov 30, 2020] The purpose of this post is to demonstrate the advantages of the Student’s \(t\)-distribution for regression with outliers, particularly within a Bayesian framework. I make assumptions I’m presuming you are familiar with linear regression, familiar with the basic differences between frequentist and Bayesian approaches to fitting regression models, and have a sense that the issue of outlier values is a pickle worth contending with.