Multiple robustness in factorized likelihood models
研究了在非参数或半参数模型中，当似然函数可分解为多个独立因子时，如何构造多重稳健估计函数，使得只要其中一个降维模型正确，估计量就具有无偏性。
We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors. We are interested in a finite-dimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires postulating several dimension-reducing models but which have mean zero at the true parameter value provided one of these models is correct.