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Nonparametric estimation of the fragmentation kernel based on a partial differential equation stationary distribution approximation

Author

Listed:
  • Van Ha Hoang
  • Thanh Mai Pham Ngoc
  • Vincent Rivoirard
  • Viet Chi Tran

Abstract

We consider a stochastic individual‐based model in continuous time to describe a size‐structured population for cell divisions. This model is motivated by the detection of cellular aging in biology. We here address the problem of nonparametric estimation of the kernel ruling the divisions based on the eigenvalue problem related to the asymptotic behavior in large population. This inverse problem involves a multiplicative deconvolution operator. Using Fourier techniques we derive a nonparametric estimator whose consistency is studied. The main difficulty comes from the nonstandard equations connecting the Fourier transforms of the kernel and the parameters of the model. A numerical study is carried out and we pay special attention to the derivation of bandwidths by using resampling.

Suggested Citation

  • Van Ha Hoang & Thanh Mai Pham Ngoc & Vincent Rivoirard & Viet Chi Tran, 2022. "Nonparametric estimation of the fragmentation kernel based on a partial differential equation stationary distribution approximation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 4-43, March.
    • Handle: RePEc:bla:scjsta:v:49:y:2022:i:1:p:4-43
    DOI: 10.1111/sjos.12504
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    References listed on IDEAS

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    1. Claire Lacour & Pascal Massart & Vincent Rivoirard, 2017. "Estimator Selection: a New Method with Applications to Kernel Density Estimation," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(2), pages 298-335, August.
    2. Fabienne Comte & Adeline Samson & Julien J Stirnemann, 2014. "Deconvolution Estimation of Onset of Pregnancy with Replicate Observations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 325-345, June.
    3. Hoffmann, Marc & Olivier, Adélaïde, 2016. "Nonparametric estimation of the division rate of an age dependent branching process," Stochastic Processes and their Applications, Elsevier, vol. 126(5), pages 1433-1471.
    4. F. Comte & C. Lacour, 2011. "Data‐driven density estimation in the presence of additive noise with unknown distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 601-627, September.
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