  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.
