Statistical study of asymmetry in cell lineage data

Benoîte de Saporta, Anne Gégout-Petit and Laurence Marsalle

Computational Statistics & Data Analysis, 2014, vol. 69, issue C, 15-39

Abstract: A rigorous methodology is proposed to study cell division data consisting in several observed genealogical trees of possibly different shapes. The procedure takes into account missing observations, data from different trees, as well as the dependence structure within genealogical trees. Its main new feature is the joint use of all available information from several data sets instead of single data set estimation, to avoid the drawbacks of low accuracy for estimators or low power for tests on small single trees. The data is modeled by an asymmetric bifurcating autoregressive process and possibly missing observations are taken into account by modeling the genealogies with a two-type Galton–Watson process. Least-squares estimators of the unknown parameters of the processes are given and symmetry tests are derived. Results are applied on real data of Escherichia coli division and an empirical study of the convergence rates of the estimators and power of the tests is conducted on simulated data.

Keywords: Multiple data sets estimation; Cell division data; Least-squares estimation; Missing data; Wald’s test; Replicated trees; Asymptotic normality; Bifurcating autoregressive model; Two-type Galton–Watson model (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:69:y:2014:i:c:p:15-39

DOI: 10.1016/j.csda.2013.07.025

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