Likelihood Estimation for Censored Random Vectors
针对一类广泛的删失问题，构建了有效的似然函数，证明其最大化估计量具有一致性和渐近正态性，推广了Tobit模型等标准估计方法。
ABSTRACT This article shows how to construct a likelihood for a general class of censoring problems. This likelihood is proven to be valid, i.e. its maximizer is consistent and the respective root-n estimator is asymptotically efficient and normally distributed under regularity conditions. The method generalizes ordinary maximum likelihood estimation as well as several standard estimators for censoring problems (e.g. tobit type I–tobit type V).