Andrew Heiss • 5/9/2022

A guide to modeling outcomes that have lots of zeros with Bayesian hurdle lognormal and hurdle Gaussian regression models

Read Original

This article provides a detailed tutorial on using Bayesian hurdle regression models (lognormal and Gaussian) in R with brms and Stan to analyze outcomes with excess zeros, such as foreign aid data. It explains the two-process modeling approach and compares it to other methods like beta regression.

0 comments
#R #Bayesian Statistics #Brm
A guide to modeling outcomes that have lots of zeros with Bayesian hurdle lognormal and hurdle Gaussian regression models

Comments

No comments yet

Be the first to share your thoughts!

Browser Extension

Get instant access to AllDevBlogs from your browser

Top of the Week

1
React, useEffect, and Stale Closures: How to Avoid Them with Dependencies and useRef
Jivbcoop • 4 votes
2
Better react-hook-form Smart Form Components
Maarten Hus • 2 votes
3
AGI, ASI, A*I – Do we have all we need to get there?
John D. Cook • 1 votes
4
Quoting Thariq Shihipar
Simon Willison • 1 votes
5
Dew Drop – January 15, 2026 (#4583)
Alvin Ashcraft • 1 votes
6
Using Browser Apis In React Practical Guide
Jivbcoop • 1 votes