Errors in Statistical Inference Under Model Misspecification: Evidence, Hypothesis Testing, and AIC

Errors in Statistical Inference Under Model Misspecification: Evidence, Hypothesis Testing, and AIC

The methods for making statistical inferences in scientific analysis have diversified even within the frequentist branch of statistics, but comparison has been elusive. We approximate analytically and numerically the performance of Neyman-Pearson hypothesis testing, Fisher significance testing, info...

Full description

Bibliographic Details
Main Authors: Brian Dennis, José Miguel Ponciano, Mark L. Taper, Subhash R. Lele
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-10-01
Series:Frontiers in Ecology and Evolution
Subjects:
model misspecification
evidential statistics
evidence
error rates in model selection
Kullback-Leibler divergence
hypothesis testing
Online Access:https://www.frontiersin.org/article/10.3389/fevo.2019.00372/full

Internet

https://www.frontiersin.org/article/10.3389/fevo.2019.00372/full

Similar Items