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Related Experiment Video

Updated: Jun 14, 2026

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

Inference after variable selection using restricted permutation methods.

Rui Wang¹, Stephen W Lagakos

¹Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.

The Canadian Journal of Statistics = Revue Canadienne De Statistique

|April 7, 2010

Summary

This summary is machine-generated.

Researchers developed new statistical methods for analyzing data after variable selection, avoiding distorted inferences when using the same dataset for both selection and analysis. This improves the reliability of findings in observational studies.

Related Experiment Videos

Last Updated: Jun 14, 2026

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

Area of Science:

Statistics
Biostatistics
Data Analysis

Background:

Variable selection is common in analyzing multiple covariates and a response variable.
Standard inference methods can yield distorted results when applied to the same dataset used for variable selection.

Purpose of the Study:

To develop and validate statistical methods for inference on marginal associations after variable selection using the same dataset.
To provide reliable testing and interval estimation for model parameters.

Main Methods:

Development of novel testing and interval estimation methods for marginal association parameters.
Theoretical justification and simulation studies to assess performance.
Comparison with a sample-splitting approach.

Main Results:

The proposed methods provide valid inferences for parameters reflecting marginal associations after variable selection.
Simulations demonstrate the performance of the new methods compared to sample-splitting.
The methods are effective even when using the same dataset for selection and inference.

Conclusions:

The developed methods offer a robust solution for statistical inference in the presence of variable selection.
These techniques enhance the accuracy of findings from observational studies, as illustrated by an AIDS study example.