To estimate unknown population parameters based on \({\varvec{y}}\), a vector of multivariate outcomes having nonignorable item nonresponse that directly depends on \({\varvec{y}}\), we propose an innovative inverse propensity weighting approach when the joint distribution of \({\varvec{y}}\) and associated covariate \({\varvec{x}}\) is nonparametric and the nonresponse probability conditional on \({\varvec{y}}\) and \({\varvec{x}}\) has a parametric form. To deal with the identifiability issue, we utilize a nonresponse instrument \({\varvec{z}}\), an auxiliary variable related to \({\varvec{y}}\) but not related to the nonresponse probability conditional on \({\varvec{y}}\) and \({\varvec{x}}\). We utilize a modified generalized method of moments to obtain estimators of the parameters in the nonresponse probability. Simulation results are presented and an application is illustrated in a real data set.

为了估计基于\({\varvec{y}}\)的未知总体参数，我们提出了一种创新的逆倾向当\({\varvec{y}}\)和相关协变量\({\varvec{x}}\)的联合分布是非参数且无响应概率以\({\varvec{y}}\为条件时的加权方法)和\({\varvec{x}}\)具有参数形式。为了处理可识别性问题，我们使用了一个无响应工具\({\varvec{z}}\) ，一个与\({\varvec{y}}\)相关的辅助变量但与以\({\varvec{y}}\)和\({\varvec{x}}\)为条件的无响应概率无关。我们利用改进的广义矩方法来获得无响应概率中参数的估计量。给出了模拟结果，并在真实数据集中说明了一个应用程序。