Estimating the Division Kernel of a ...
Document type :
Pré-publication ou Document de travail
Title :
Estimating the Division Kernel of a Size-Structured Population
Author(s) :
English keyword(s) :
random size-structured population
nonparametric estimation
optimal rate
penalization
adaptive estimator
Goldenshluger-Lepski's method
division kernel
nonparametric estimation
optimal rate
penalization
adaptive estimator
Goldenshluger-Lepski's method
division kernel
HAL domain(s) :
Mathématiques [math]/Statistiques [math.ST]
Mathématiques [math]/Probabilités [math.PR]
Mathématiques [math]/Probabilités [math.PR]
English abstract : [en]
We consider a size-structured population describing the cell divisions. The cell population is described by an empirical measure and we observe the divisions in the continuous time interval [0, T ]. We address here the ...
Show more >We consider a size-structured population describing the cell divisions. The cell population is described by an empirical measure and we observe the divisions in the continuous time interval [0, T ]. We address here the problem of estimating the division kernel h (or fragmentation kernel) in case of complete data. An adaptive estimator of h is constructed based on a kernel function K with a fully data-driven bandwidth selection method. We obtain an oracle inequality and an exponential convergence rate, for which optimality is considered.Show less >
Show more >We consider a size-structured population describing the cell divisions. The cell population is described by an empirical measure and we observe the divisions in the continuous time interval [0, T ]. We address here the problem of estimating the division kernel h (or fragmentation kernel) in case of complete data. An adaptive estimator of h is constructed based on a kernel function K with a fully data-driven bandwidth selection method. We obtain an oracle inequality and an exponential convergence rate, for which optimality is considered.Show less >
Language :
Anglais
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