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A Rank-Based Estimate for Cell Lineage Data

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Jeffrey Terpstra



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The presence of aberrant observations (i.e. outliers) in cell lineage data is quite common. As such, it is desirable to have an outlier-resistant estimation procedure as an alternative to least squares estimation (maximum likelihood estimation under normality). In this work, we consider rankbased estimates of the parameters of a first order bifurcating autoregressive [BAR(1)] model. The BAR(1) model was proposed by Cowan and Staudte (1986) for cell lineage data. In it, each line of descendents follows a first order autoregressive [AR(1)] model and allows sister cells from the same mother to be correlated. Real examples and a simulation study are performed in order to examine the behavior of these rank-based estimation procedures. More specifically, we compute finite sample relative efficiencies with respect to least squares estimate. The results indicate that the rank-based estimation procedures are more efficient when outlying observations are present.