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Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

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Jackknife empirical likelihood method for multiply robust estimation with missing data.

Sixia Chen¹, David Haziza²

¹Department of Biostatistics and Epidemiology, University of Oklahoma, Health Sciences Center, Oklahoma City, Oklahoma 73104, USA.

Computational Statistics & Data Analysis

|December 18, 2018

Summary

This summary is machine-generated.

A new jackknife empirical likelihood method provides reliable confidence intervals for statistical estimates with missing data. This approach is validated through simulations and real-world survey data analysis.

Keywords:

Double robustness Imputation Nonresponse model

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Area of Science:

Statistics
Biostatistics
Data Science

Background:

Missing data presents significant challenges in statistical analysis, potentially biasing results.
Multiply robust estimators offer flexibility but require accurate confidence intervals.
Empirical likelihood methods provide a non-parametric approach to inference.

Purpose of the Study:

To propose a novel jackknife empirical likelihood (JEL) method for confidence intervals.
To address the challenge of constructing confidence intervals for multiply robust estimators in the presence of missing data.
To evaluate the performance of the proposed JEL method.

Main Methods:

Development of a jackknife empirical likelihood ratio statistic.
Theoretical analysis of the asymptotic properties of the JEL ratio under mild regularity conditions.
Simulation studies to compare the proposed method with existing approaches.
Application to real-world data from the 2016 National Health Interview Survey.

Main Results:

The proposed jackknife empirical likelihood ratio converges to a standard chi-square distribution under mild conditions.
Simulation results demonstrate the validity and benefits of the JEL method.
The method is successfully applied to a large-scale health survey dataset.

Conclusions:

The novel jackknife empirical likelihood method offers a statistically sound approach for confidence intervals with missing data.
The method is computationally feasible and performs well in practice.
This work contributes to robust statistical inference in the presence of incomplete observations.