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...

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Detalles Bibliográficos
Autores principales: Dennis, Brian, Ponciano, José Miguel, Taper, Mark L., Lele, Subhash R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293863/
https://www.ncbi.nlm.nih.gov/pubmed/34295904
http://dx.doi.org/10.3389/fevo.2019.00372

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293863/
https://www.ncbi.nlm.nih.gov/pubmed/34295904
http://dx.doi.org/10.3389/fevo.2019.00372

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