#statstab #504 Likelihood Ratio Test for Publication Bias

Thoughts: Interesting idea for a problem many of us think is present, but is hard to measure.

#likelihood #publicationbias #QRPs #metascience #metapsychology #metaresearch

https://freestylerscientist.pl/projects/likelihood-ratio-test-for-publication-bias/

Likelihood Ratio Test for Publication Bias

Likelihood Ratio Test for Publication Bias — a statistical method to detect and quantify publication bias in heterogeneous datasets.

Paweł Lenartowicz

#statstab #472 Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying
and Embrace the Distribution

Thoughts: Good intro to bayesian inference.

#bayesian #likelihood #tutorial

https://www.biorxiv.org/content/10.1101/2023.11.19.567743v2.full.pdf

Here's a new paper summarizing methods for evaluating whether the absence of a statistically significant difference (from a NHST) is actually no difference--test of equivalence, confidence interval bounds, likelihood ratios, Bayes factors, and Bayesian estimation. There's nothing new here, but it's a nice readable overview that might be worth citing for some audiences.

https://royalsocietypublishing.org/doi/full/10.1098/rsbl.2025.0506?af=R

#Science #Statistics #Likelihood #BayesFactors

#statstab #445 What are credible priors and what are skeptical priors?

Thoughts: An excellent thread on prior elicitation by some of the big names in the field (frequentist and bayesian).

#priors #bayesian #bayes #likelihood #medicine #clinical #debate

https://discourse.datamethods.org/t/what-are-credible-priors-and-what-are-skeptical-priors/580

What are credible priors and what are skeptical priors?

A few weeks ago Dan Scharfstein asked a group of colleagues about how to report an odds ratio of 1.70 with 95% confidence limits of 0.96 and 3.02. Back-calculating from these statistics gives a two-sided P of 0.06 or 0.07, corresponding to an S-value (surprisal, log base 2 of P) of about 4 bits of information against the null hypothesis of OR=1. So, not much evidence against the null from the result, but still favoring a positive association over an inverse one, and so thought worthy of reportin...

Datamethods Discussion Forum
Ah yes, the groundbreaking 2018 #tutorial on "likelihood," where the real mystery is not statistical analysis, but navigating the dark arts of enabling #JavaScript and #cookies. 🍪🔍 Because who needs math when you can unravel the secrets of #browser #settings instead? 🙄
https://journals.sagepub.com/doi/10.1177/2515245917744314 #likelihood #tech #humor #HackerNews #ngated
Introduction to the Concept of Likelihood and Its Applications - Alexander Etz, 2018

This Tutorial explains the statistical concept known as likelihood and discusses how it underlies common frequentist and Bayesian statistical methods. The artic...

Sage Journals
Introduction to the Concept of Likelihood and Its Applications - Alexander Etz, 2018

This Tutorial explains the statistical concept known as likelihood and discusses how it underlies common frequentist and Bayesian statistical methods. The artic...

Sage Journals

#statstab #443 Dienes Bayes factor calculator

Thoughts: Dienes presents a different way to compute BFs using the sample data. But, this can be seen as an acceptable double-dipping.

#bayesian #bayesfactor #evidence #likelihood #guide

https://bencepalfi.shinyapps.io/Dienes_BF_calculator/

Dienes Bayes factor calculator

After doing very sophisticated* web searching, it seems that it is not uncommon typo, and I could not find a definition.

So, there seems to be an opening for introducing a new term. Maybe outside #Statistics, shall we talk about a #Likeligood Function in #WelfareEconomics?
Or does it describe a new equilibrium in #GameTheory, the one that is likely good?

#Likelihood

* 😂 😂 😅

I thought it was an innocent, unassuming typo.

But now I see a need to figure out whether "Likeligood estimation" is actually a thing...

#Statistics #RabbitHole #Likelihood

'Determine the Number of States in Hidden Markov Models via Marginal Likelihood', by Yang Chen, Cheng-Der Fuh, Chu-Lan Michael Kao.

http://jmlr.org/papers/v26/23-0343.html

#markov #bayesian #likelihood

Determine the Number of States in Hidden Markov Models via Marginal Likelihood