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Related Experiment Video

Updated: Jun 4, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Profile Likelihood and Incomplete Data.

¹Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20892-7510, USA.

International Statistical Review = Revue Internationale De Statistique

|February 1, 2011

Summary

This summary is machine-generated.

This study introduces a profile likelihood approach for statistical evidence with incomplete data. It avoids untestable assumptions, offering a reliable interpretation of observed data for missing or censored information.

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Area of Science:

Statistics
Statistical Inference
Data Analysis

Background:

The law of likelihood defines statistical evidence using likelihood functions and ratios.
The likelihood paradigm interprets evidence objectively, distinct from error probabilities or beliefs.
Incomplete data (non-response, censoring) challenges standard likelihood methods, often requiring untestable assumptions.

Purpose of the Study:

To propose a profile likelihood approach for statistical evidence with incomplete data.
To avoid imposing untestable assumptions common in standard methods for incomplete data.
To provide a framework for objective interpretation of statistical evidence from incomplete datasets.

Main Methods:

Developed a profile likelihood approach for incomplete data.
Utilized partial identification principles.
Applied the method to statistical problems with missing and censored data.

Main Results:

The proposed profile likelihood approach effectively represents statistical evidence with incomplete data.
The method avoids reliance on untestable assumptions about data incompleteness mechanisms.
Numerical examples with real data demonstrate the approach's feasibility and interpretability.

Conclusions:

The profile likelihood approach offers a robust method for statistical inference with incomplete data.
This framework enhances the objective interpretation of evidence when data are missing or censored.
The approach provides a viable alternative to methods requiring external or untestable assumptions.