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Asymptotics for a Class of Dynamic Recurrent Event Models.

Edsel A Peña1

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Summary

This study presents asymptotic properties for dynamic recurrent event models, crucial for understanding complex event data. These findings aid in developing robust statistical methods for analyzing interventions and censoring in dynamic systems.

Keywords:
compensatorsconsistencycounting processesfull modelsmarginal modelsmartingalesrepair modelssum-quota accrualweak convergence

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Area of Science:

  • Statistics
  • Biostatistics
  • Reliability Engineering
  • Social Sciences

Background:

  • Recurrent event data analysis is complex due to factors like interventions, accumulating events, and dependent censoring.
  • Existing models often do not fully capture the dynamic interplay of these factors.
  • A general class of dynamic recurrent event models is needed to address these complexities.

Purpose of the Study:

  • To present the asymptotic properties (consistency and weak convergence) of estimators for a general class of dynamic recurrent event models.
  • To provide a theoretical foundation for statistical inference in these complex models.
  • To facilitate the development of advanced statistical procedures.

Main Methods:

  • The study focuses on theoretical statistical analysis.
  • Asymptotic properties of estimators are derived.
  • The analysis considers dynamic covariates, event-triggered interventions, accumulating event impacts, and informative censoring.

Main Results:

  • Established consistency and weak convergence for estimators in dynamic recurrent event models.
  • The presented properties are applicable to a broad class of models, including special cases from biostatistics, reliability, and social sciences.
  • The findings provide a basis for statistical inference in models accounting for interventions and dependent censoring.

Conclusions:

  • The derived asymptotic properties are essential for advancing the statistical analysis of dynamic recurrent event data.
  • These properties support the development of goodness-of-fit tests, confidence intervals, and hypothesis testing procedures.
  • The research offers a unified framework for analyzing complex recurrent event processes.