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October 3, 2025Open Access

Post-reduction inference for confidence sets of models

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This analysis reports on confidence sets of models in regression, highlighting the role of nuisance parameters.

Confidence sets of models avoid issues arising from using the same data for model assessment and reduction.
The paper illustrates how estimation of nuisance parameters affects information extraction using normal-theory linear regression.
This approach suggests that sample-splitting may perform as well as traditional co-sufficient or ancillary tests.
The findings extend the application to both canonical exponential-family models and regression models more generally.

Battey et al. (2025) studied this question.

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October 3, 2025Open Access

View Full Paper

Ask AI

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Authors

This analysis reports on confidence sets of models in regression, highlighting the role of nuisance parameters.

Confidence sets of models avoid issues arising from using the same data for model assessment and reduction.
The paper illustrates how estimation of nuisance parameters affects information extraction using normal-theory linear regression.
This approach suggests that sample-splitting may perform as well as traditional co-sufficient or ancillary tests.
The findings extend the application to both canonical exponential-family models and regression models more generally.

Battey et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark