Second Order Accurate Inference for Nonparametric Estimating Equations Models

A paper by Francesco Bravo proposing and evaluating new inference methods for nonparametric estimating equations models. The work includes simulation studies and a real data example to illustrate the finite sample properties of the proposed test statistics and bootstrap method. The paper is available as a 14.1 MB PDF file under a CC-BY-4.0 license.

Use Cases

• Testing simple hypotheses about unknown infinite dimensional parameters based on the proposed local Generalised Empirical Likelihood (GEL) test statistics.
• Testing for the correct specification of a chosen nonparametric estimating equations model using the modified GEL test statistics.
• Implementing a local efficient bootstrap method to achieve second order accurate inferences for model specification testing.
• Evaluating the finite sample performance of second order accurate inference methods through simulation studies.

Strengths

• The paper proposes a new local efficient bootstrap method that delivers second order accurate inferences.
• The work includes simulation studies and a real data example to demonstrate the applicability of the proposed methods.
• The full paper is available as a 14.1 MB PDF, providing detailed methodological exposition.

Limitations

• The dataset is a single PDF document; the underlying simulation or example data is not provided as a separate, structured dataset.
• Column-level documentation is absent; field semantics must be inferred from the paper's text.
• Row count is unknown, which may limit suitability assessment for direct data analysis.

Provenance

Source

figshare, authored by Francesco Bravo.

Collection Method

Theoretical development and simulation.

Freshness

Last updated 2026-05-06 05:11:37; freshness should be verified.