Lu, XuewenWang, YanBandyopadhyay, DipankarBakoyannis, Giorgos2025-04-092025-04-092023Lu X, Wang Y, Bandyopadhyay D, Bakoyannis G. Sieve estimation of a class of partially linear transformation models with interval-censored competing risks data. Stat Sin. 2023;33(2):685-704. doi:10.5705/ss.202021.0051https://hdl.handle.net/1805/46929In this paper, we consider a class of partially linear transformation models with interval-censored competing risks data. Under a semiparametric generalized odds rate specification for the cause-specific cumulative incidence function, we obtain optimal estimators of the large number of parametric and nonparametric model components via 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 parameter space as n → ∞, thereby allowing 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 through simulation studies under a variety of scenarios. Furthermore, we illustrate our methodology via application to a dataset on HIV-infected individuals from sub-Saharan Africa.en-USPublisher PolicyBernstein polynomialsCompeting risksCumulative incidence functionInterval censoringPartially linear transformation modelSemiparametric efficiencyArticle