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Abstract Details
Activity Number:
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525
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #301243 |
Title:
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An Empirical Study of a Rank-Based Estimate for Cell Lineage Data
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Author(s):
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Tamer Elbayoumi*+ and Jeffrey Terpstra
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Companies:
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Western Michigan University and Western Michigan University
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Address:
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Department of Statistics, Kalamazoo, MI, 49008,
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Keywords:
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Rank-Based Estimates ;
Least Squares Estimate ;
Bifurcating Autoregressive ;
Cell Lineage Data ;
Sample Relative Efficiency ;
Aberrant Observations
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Abstract:
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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 rank-based 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.
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