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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Kernel Estimation of Rate Function for Recurrent Event Data.

Chin-Tsang Chiang1, Mei-Cheng Wang, Chiung-Yu Huang

  • 1Department of Mathematics, National Taiwan University.

Scandinavian Journal of Statistics, Theory and Applications
|September 25, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for estimating the rate function in recurrent event data analysis. The least squares method offers better accuracy, while the moment method is computationally faster for statistical modeling.

Keywords:
Poisson processbootstrapindependent censoringintensity functionkernel estimatorrate functionrecurrent events

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Recurrent event data analysis is crucial in many fields.
  • Estimating the rate function for recurrent events lacks rigorous statistical methods.
  • Existing smoothing techniques require further development and study.

Purpose of the Study:

  • To develop and evaluate statistical methods for estimating the rate function from recurrent event data.
  • To compare the moment and least squares estimation methods.
  • To propose bootstrap procedures for bandwidth selection and confidence interval approximation.

Main Methods:

  • The study employs moment and least squares methods for rate function estimation.
  • Independent censoring is assumed for the recurrent event process.
  • Bootstrap procedures are utilized for bandwidth selection and confidence interval estimation.

Main Results:

  • The least squares method avoids nicks at censoring times, unlike the moment method without resmoothing.
  • The least squares estimator demonstrates smaller asymptotic variance under regularity conditions.
  • The moment method is computationally more efficient due to condensed bootstrap data.

Conclusions:

  • Both moment and least squares methods provide viable approaches for rate function estimation.
  • The choice between methods depends on the balance between accuracy and computational efficiency.
  • The proposed bootstrap procedures are effective for practical implementation in recurrent event data analysis.