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Related Experiment Video

Updated: Jun 16, 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

Estimation and inference based on Neumann series approximation to locally efficient score in missing data problems.

¹Division of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL 60532.

Scandinavian Journal of Statistics, Theory and Applications

|February 18, 2010

Summary

This summary is machine-generated.

This study introduces a new iterative method for estimating missing data, improving upon complex Neumann series approximations. The approach yields a doubly-robust, locally-efficient estimator for various regression models with missing at random data.

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Last Updated: Jun 16, 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

Area of Science:

Statistics
Biostatistics
Econometrics

Background:

Semiparametric efficient estimation for missing data is complex.
Existing methods like Neumann series lack clear statistical property analysis.
Efficient scores often require solving integral equations, not closed forms.

Purpose of the Study:

To reformulate successive approximation for efficient scores in missing data problems.
To study the statistical properties of estimators derived from this reformulation.
To develop a doubly-robust, locally-efficient estimator.

Main Methods:

Reformulation of Neumann series into a simple iterative algorithm.
Analysis of statistical properties of the proposed iterative estimator.
Application to parametric, marginal, and Cox regression models with missing at random data.

Main Results:

A doubly-robust, locally-efficient estimator is achievable through the new algorithm.
The proposed method offers a more tractable approach to efficient estimation.
The approach is validated through simulation studies and a real data example.

Conclusions:

The iterative reformulation provides a robust and efficient method for handling missing data.
This approach enhances statistical inference in regression models with missing values.
The findings are applicable across various statistical modeling scenarios.