2022
DOI: 10.48550/arxiv.2202.09096
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics

Abstract: Parameter estimation in the empirical fields is usually undertaken using parametric models, and such models are convenient because they readily facilitate statistical inference. Unfortunately, they are unlikely to have a sufficiently flexible functional form to be able to adequately model realworld phenomena, and their usage may therefore result in biased estimates and invalid inference. Unfortunately, whilst non-parametric machine learning models may provide the needed flexibility to adapt to the complexity o… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 51 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?