SIEVE ESTIMATION OF A CLASS OF PARTIALLY LINEAR TRANSFORMATION MODELS WITH INTERVAL-CENSORED COMPETING RISKS DATA

We consider a class of partially linear transformation models with interval -censored competing risks data. Under a semiparametric generalized odds rate spec-ification for the cause-specific cumulative incidence function, we obtain optimal estimators of the large number of parametric and nonparametric model compo-nents by maximizing the likelihood function over a joint B-spline and Bernstein polynomial spanned sieve space. Our specification considers a relatively simpler finite-dimensional parameter space, approximating the infinite-dimensional param-eter space as n & RARR; & INFIN;. This allows us to study the almost sure consistency and rate of convergence for all parameters, and the asymptotic distributions and efficiency of the finite-dimensional components. We study the finite-sample performance of our method using simulation studies under a variety of scenarios. Furthermore, we illustrate our methodology by applying it to a data set on HIV-infected individuals from sub-Saharan Africa.