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Related Experiment Video

Updated: Jun 27, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

A New Mutual Information Estimator for Continuous Censored Variables.

Ima Bernada¹, Cécilia Samieri¹, Grégory Nuel²

¹Bordeaux Population Health, Institut National de la Santé et de la Recherche Médicale, 33000 Bordeaux, France.

Entropy (Basel, Switzerland)

|June 26, 2026

Summary

A new method corrects mutual information (MI) estimation for censored continuous data, reducing bias and improving accuracy. This approach enhances dependency analysis in complex datasets, crucial for statistical modeling.

Keywords:

censoring estimation mutual information simulations

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Area of Science:

Statistics
Information Theory
Biostatistics

Background:

Estimating dependency between variables is crucial in statistics.
Mutual Information (MI) quantifies shared information, capturing linear and non-linear dependencies.
Existing MI estimators often struggle with complex data types like censored continuous variables.

Purpose of the Study:

To develop and evaluate a novel method for estimating mutual information in the presence of continuous censored data.
To assess the effectiveness of this correction method on existing MI estimators.
To improve dependency estimation for real-world datasets with detection limits.

Main Methods:

Proposed a new MI estimation method by decomposing the MI formula into parts addressing censoring status and continuous data.
Applied this correction to existing MI estimators for continuous, mixed, and discrete-continuous data.
Evaluated performance using simulations with varying censoring rates, correlations, and sample sizes for censored log-normal variables.

Main Results:

The proposed correction method globally reduced bias in MI estimation.
Corrected estimators demonstrated improved convergence towards true MI values with increasing sample size.
Effectiveness was observed across different simulation scenarios and tested estimators.

Conclusions:

The developed correction method enhances the ability of existing MI estimators to handle continuous censored data.
This approach offers a valuable tool for more accurate dependency analysis in fields with censored measurements.
The findings support the use of this correction for improved statistical modeling with complex data.