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The nonparametric maximum likelihood estimator for middle-censored data.

Pao-Sheng Shen1

  • 1Department of Statistics, Tunghai University, Taichung 40704, Taiwan.

Journal of Statistical Planning and Inference
|April 15, 2020
PubMed
Summary
This summary is machine-generated.

This study addresses middle censoring in data analysis. Researchers found that the nonparametric maximum likelihood estimator (NPMLE) for distribution functions is achievable using specific algorithms and initial estimators, ensuring statistical consistency.

Keywords:
Middle censoringMixed interval censoringSelf-consistent

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • Middle censoring occurs when data is unobservable within specific intervals.
  • Accurate estimation of distribution functions is crucial in statistical modeling.

Purpose of the Study:

  • To explore methods for estimating distribution functions with interval-censored data.
  • To establish the consistency of the proposed nonparametric maximum likelihood estimator (NPMLE).

Main Methods:

  • Utilizing Turnbull's (1976) EM algorithm for NPMLE.
  • Employing self-consistent estimating equations (Jammalamadaka and Mangalam, 2003).
  • Establishing consistency based on asymptotic properties of self-consistent estimators (SCE) for mixed interval-censored data (Yu et al., 2000, Yu et al., 2001).

Main Results:

  • Demonstrated that NPMLE can be obtained using established algorithms with specific initial estimators.
  • Showcased the applicability of EM algorithm and self-consistent estimating equations.

Conclusions:

  • The NPMLE is a viable method for interval-censored data.
  • The consistency of NPMLE is supported by asymptotic properties of SCE in mixed interval-censored data settings.