  Overweight and obesity in adults are known to be associated with risks of
metabolic and cardiovascular diseases. Because obesity is an epidemic,
increasingly affecting children, it is important to understand if this
condition persists from early life to childhood and if different patterns of
obesity growth can be detected. Our motivation starts from a study of obesity
over time in children from South Eastern Asia. Our main focus is on clustering
obesity patterns after adjusting for the effect of baseline information.
Specifically, we consider a joint model for height and weight patterns taken
every 6 months from birth. We propose a novel model that facilitates clustering
by combining a vector autoregressive sampling model with a dependent logit
stick-breaking prior. Simulation studies show the superiority of the model to
capture patterns, compared to other alternatives. We apply the model to the
motivating dataset, and discuss the main features of the detected clusters. We
also compare alternative models with ours in terms of predictive performances.
