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a study of frequentist hypothesis tests under separation
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README.md

p-Values Without Penalties With Perfect Predictions

Question

Previous research suggests using penalized maximum likelihood for dealing with separation in logistic regression models (Zorn 2005), but notes that the penalty is a meaningful, substantive decision (Rainey 2016). In the project I show that researchers can use the likelihood ratio to compute reasonable, well-behaved p-values without using frequentist penalties or prior information.

The latest (in-progress) draft is here.

Directory Structure

I named files and directory so that their purpose can (hopefully) be understood from the name. The Makefile formally documents the relationship among the files and the steps to reproduce my work.

Key Figures and Tables

Barrilleaux and Rainey (2014)

Raw Data

The project uses two data sets discussed from previous research.

Project Progress

Completed Tasks

  • Obtain raw data.
  • Wrangle the raw data into a usable format consistent with Broman and Woo (2018).

Immediate Next Steps

  • Complete manuscript section on the theory of hypothesis tests.

Someday/Maybe

  • Perform MC simulations for a general logistic regression.
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