Many statistical methods for truncated data rely on the independence assumption regarding the truncation variable. In many application studies, however, the dependence between a variable X of interest and its truncation variable L plays a fundamental role in modeling data structure. For...

Using an empirical likelihood approach, we show that generalized linear models can still be consistently estimated even if dependent variables are not missing at random, and derive a Hausman test by comparing this estimator to the standard one.

We present symmetry tests for bifurcating autoregressive (BAR) processes when some data are missing. BAR processes typically model cell division data. Each cell can be of one of two types odd or even. The goal of this paper is to study the possible asymmetry between odd and even cells in a...