Extended Fiducial Inference: Toward an Automated Process of Statistical Inference Article Swipe
Faming Liang
,
Sehwan Kim
,
Yan Sun
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.21622
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.21622
While fiducial inference was widely considered a big blunder by R.A. Fisher, the goal he initially set --`inferring the uncertainty of model parameters on the basis of observations' -- has been continually pursued by many statisticians. To this end, we develop a new statistical inference method called extended Fiducial inference (EFI). The new method achieves the goal of fiducial inference by leveraging advanced statistical computing techniques while remaining scalable for big data. EFI involves jointly imputing random errors realized in observations using stochastic gradient Markov chain Monte Carlo and estimating the inverse function using a sparse deep neural network (DNN). The consistency of the sparse DNN estimator ensures that the uncertainty embedded in observations is properly propagated to model parameters through the estimated inverse function, thereby validating downstream statistical inference. Compared to frequentist and Bayesian methods, EFI offers significant advantages in parameter estimation and hypothesis testing. Specifically, EFI provides higher fidelity in parameter estimation, especially when outliers are present in the observations; and eliminates the need for theoretical reference distributions in hypothesis testing, thereby automating the statistical inference process. EFI also provides an innovative framework for semi-supervised learning.
Related Topics
Concepts
Fiducial marker
Fiducial inference
Inference
Statistical inference
Computer science
Process (computing)
Artificial intelligence
Frequentist inference
Machine learning
Bayesian inference
Statistics
Mathematics
Bayesian probability
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.21622
- https://arxiv.org/pdf/2407.21622
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401306750
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401306750Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.21622Digital Object Identifier
- Title
-
Extended Fiducial Inference: Toward an Automated Process of Statistical InferenceWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-31Full publication date if available
- Authors
-
Faming Liang, Sehwan Kim, Yan SunList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.21622Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2407.21622Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2407.21622Direct OA link when available
- Concepts
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Fiducial marker, Fiducial inference, Inference, Statistical inference, Computer science, Process (computing), Artificial intelligence, Frequentist inference, Machine learning, Bayesian inference, Statistics, Mathematics, Bayesian probability, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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