Estimating clonal heterogeneity and interexperiment variability with the bifurcating autoregressive model for cell lineage data☆
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A law of large numbers result for a bifurcating process with an infinite moving average representation
2012, Statistics and Probability LettersCitation Excerpt :Some general themes include autoregressive and autoregressive moving average structures, maximum likelihood and robust inference procedures, and asymptotic results. See, for example, the following references: Huggins and Marschner (1991), Marschner (1992), Huggins and Staudte (1994), Huggins (1995, 1996a,b), Staudte et al. (1996), Staudte et al. (1997), Bui and Huggins (1998, 1999), Huggins and Basawa (1999, 2000), Basawa and Zhou (2004), Zhou and Basawa (2005a,b), Guyon (2007), Hwang and Basawa (2009), and Hwang et al. (2009). Many of the applications in the aforementioned literature rely on asymptotic distributions.
Trait Variability of Cancer Cells Quantified by High-Content Automated Microscopy of Single Cells
2009, Methods in EnzymologyCitation Excerpt :Metrics of this similarity or differences between siblings are obtained either by determining the correlation between sibling GR (Fig. 2.7A and B) or by plotting the difference between the IMT of sibling pairs (Fig. 2.7C). Although not yet applied to our datasets, a very promising approach to quantify the variance of proliferation metrics within cell lines is the bifurcating autoregression model (Staude et al., 1997). The model accounts for cells progressing through a standard cell cycle and can be used to quantify heterogeneity in the population using bifurcating data structures such as progeny trees.
Estimation of replicative senescence via a population dynamics model of cells in culture
2001, Experimental GerontologyMinimum Hellinger distance estimation for supercritical Galton-Watson processes
2000, Statistics and Probability LettersCitation Excerpt :Incidentally, this assumption may seem too strong. However, in many practical applications they are met (see, for instance, Staudte, 1992; Huggins, 1994,1996; Staudte et al., 1997). It is possible to modify this assumption by basing the inference on sparsely sampled family tree.
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This research was supported by a grant from the Australian Research Council in 1994.