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Inferences for multiple interval type-I censoring scheme.

Shubham Agnihotri1, Sanjay Kumar Singh1, Umesh Singh1

  • 1Department of Statistics, Institute of Science, Banaras Hindu University, Varanasi, India.

Journal of Applied Statistics
|December 19, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new multiple interval type-I censoring scheme for Weibull distributions. It proposes new estimators and compares them to existing methods, offering insights into parameter estimation for reliability analysis.

Keywords:
Maximum likelihood estimatorsWeibull distributionmaximum product of spacing estimatorsmultiple interval censoring schemetype-I censoring scheme

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Area of Science:

  • Statistics
  • Reliability Engineering
  • Survival Analysis

Background:

  • Censoring schemes are crucial in survival analysis for handling incomplete data.
  • The Weibull distribution is widely used for modeling lifetime data.
  • Existing estimation methods may have limitations under specific censoring conditions.

Purpose of the Study:

  • To introduce a novel censoring scheme: the multiple interval type-I censoring scheme.
  • To develop and evaluate new statistical estimators for Weibull distribution parameters under this scheme.
  • To compare the performance of proposed estimators against established methods like maximum likelihood estimation.

Main Methods:

  • Development of maximum product of spacing (MPS) estimators for Weibull parameters.
  • Application of Bayes estimators under a squared error loss function.
  • Comparison with maximum likelihood estimators (MLEs).
  • Analysis of asymptotic confidence and credible intervals.

Main Results:

  • The proposed maximum product of spacing estimators are introduced for the Weibull distribution under the new censoring scheme.
  • Bayes estimators for shape and scale parameters are derived.
  • Performance comparison with maximum likelihood estimators is conducted.
  • Asymptotic intervals for parameters are discussed.

Conclusions:

  • The new multiple interval type-I censoring scheme provides a framework for analyzing incomplete lifetime data.
  • The proposed MPS and Bayes estimators offer viable alternatives for parameter estimation.
  • The methodology is validated using a real-world dataset on insulating fluid breakdown times.