The distributed state estimation problem of the stochastic uncertain system with quantized measurements and packet dropouts is studied. A group of Bernoulli distributed random variables is employed to describe the phenomenon of packet dropouts, and the predictor of lost observation is used as the observation when a packet is lost. The error introduced by data quantization is described as a bounded uncertain parameter in the observation equation, and the uncertainty of the model is described by stochastic parameter perturbbation in the coefficient matrix. All measurements in the fixed time domain are used to construct a cost function, and the state estimation problem is modeled as a regularized least squares problem with uncertain parameters, by reducing a vector optimization problem to a scalar optimization problem of an unimodal function, a robust moving horizon local estimator is obtained. The stability of local estimator is studied, and a sufficient condition for the convergence of the square norm of estimation error is obtained. A distributed fusion estimator is presented based on the covariance intersection algorithm. Finally, simulation examples are given to demonstrate the effectiveness of the proposed method