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Autores principales: Heinzel, Carola Sophia, Schweinsberg, Jason
Formato: Preprint
Publicado: 2025
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Acceso en línea:https://arxiv.org/abs/2508.16110
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author Heinzel, Carola Sophia
Schweinsberg, Jason
author_facet Heinzel, Carola Sophia
Schweinsberg, Jason
contents The problem of estimating the growth rate of a birth and death processes based on the coalescence times of a sample of $n$ individuals has been considered by several authors (\cite{stadler2009incomplete, williams2022life, mitchell2022clonal, Johnson2023}). This problem has applications, for example, to cancer research, when one is interested in determining the growth rate of a clone. Recently, \cite{Johnson2023} proposed an analytical method for estimating the growth rate using the theory of coalescent point processes, which has comparable accuracy to more computationally intensive methods when the sample size $n$ is large. We use a similar approach to obtain an estimate of the growth rate that is not based on the assumption that $n$ is large. We demonstrate, through simulations using the R package \texttt{cloneRate}, that our estimator of the growth rate performs well in comparison with previous approaches when $n$ is small.
format Preprint
id arxiv_https___arxiv_org_abs_2508_16110
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Estimating the growth rate of a birth and death process using data from a small sample
Heinzel, Carola Sophia
Schweinsberg, Jason
Methodology
Probability
The problem of estimating the growth rate of a birth and death processes based on the coalescence times of a sample of $n$ individuals has been considered by several authors (\cite{stadler2009incomplete, williams2022life, mitchell2022clonal, Johnson2023}). This problem has applications, for example, to cancer research, when one is interested in determining the growth rate of a clone. Recently, \cite{Johnson2023} proposed an analytical method for estimating the growth rate using the theory of coalescent point processes, which has comparable accuracy to more computationally intensive methods when the sample size $n$ is large. We use a similar approach to obtain an estimate of the growth rate that is not based on the assumption that $n$ is large. We demonstrate, through simulations using the R package \texttt{cloneRate}, that our estimator of the growth rate performs well in comparison with previous approaches when $n$ is small.
title Estimating the growth rate of a birth and death process using data from a small sample
topic Methodology
Probability
url https://arxiv.org/abs/2508.16110