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Root-n estimability of some missing data models.

Ao Yuan1, Jinfeng Xu, Gang Zheng

  • 1Howard University, Washington, DC 20059, USA.

Journal of Multivariate Analysis
|April 17, 2012
PubMed
Summary
This summary is machine-generated.

This study assesses root-n estimability in missing data models like interval censoring and genetic association. It identifies non-estimable parameters and derives information bounds for estimable ones, crucial for statistical analysis.

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Area of Science:

  • Statistics
  • Biostatistics
  • Statistical modeling

Background:

  • Missing data models present challenges in parameter estimation.
  • Root-n estimability is crucial for reliable statistical inference.
  • Non-Gaussian limiting distributions complicate analysis.

Purpose of the Study:

  • To assess root-n estimability in four specific missing data models.
  • To identify non-root-n estimable parameters.
  • To derive information bounds and efficient information for parameters.

Main Methods:

  • Analysis of two-point interval censoring, double censoring, and interval truncation.
  • Investigation of a case-control genetic association model.
  • Derivation of information bounds and asymptotic efficient information.

Main Results:

  • Identified non-root-n estimable parameters in interval censoring and truncation models.
  • Derived information bounds for estimable parameters where applicable.
  • Computed asymptotic efficient information for Cox regression and genetic association models.

Conclusions:

  • Preliminary assessment of root-n estimability is vital for missing data models.
  • The study provides insights into parameter estimability and efficiency in complex data scenarios.
  • Findings are applicable to survival data, genetic association studies, and censored data analysis.