SEMI-NONPARAMETRIC INTERVAL-CENSORED MIXED PROPORTIONAL HAZARD MODELS: IDENTIFICATION AND CONSISTENCY RESULTS
提出用正交勒让德多项式半非参数地估计单位区间上的分布，应用于区间删失混合比例风险模型，推导了非参数识别条件，并证明了在弱条件下M估计量的一致性。
In this paper I propose estimating distributions on the unit interval semi-nonparametrically using orthonormal Legendre polynomials. This approach will be applied to the interval-censored mixed proportional hazard (ICMPH) model, where the distribution of the unobserved heterogeneity is modeled semi-nonparametrically. Various conditions for the nonparametric identification of the ICMPH model are derived. I will prove general consistency results for M -estimators of (partly) non-euclidean parameters under weak and easy-to-verify conditions and specialize these results to sieve estimators. Special attention is paid to the case where the support of the covariates is finite.