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- W4386517154 abstract "Abstract Separation commonly occurs in political science, usually when a binary explanatory variable perfectly predicts a binary outcome. In these situations, methodologists often recommend penalized maximum likelihood or Bayesian estimation. But researchers might struggle to identify an appropriate penalty or prior distribution. Fortunately, I show that researchers can easily test hypotheses about the model coefficients with standard frequentist tools. While the popular Wald test produces misleading (even nonsensical) p -values under separation, I show that likelihood ratio tests and score tests behave in the usual manner. Therefore, researchers can produce meaningful p -values with standard frequentist tools under separation without the use of penalties or prior information ." @default.
- W4386517154 created "2023-09-08" @default.
- W4386517154 creator A5067433306 @default.
- W4386517154 date "2023-09-07" @default.
- W4386517154 modified "2023-10-16" @default.
- W4386517154 title "Hypothesis Tests under Separation" @default.
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- W4386517154 doi "https://doi.org/10.1017/pan.2023.28" @default.
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