Due to the model-plant mismatch in practical applications of a nonlinear model, it is necessary to estimate both states and model parameters online. However, when the number of uncertain parameters is large, it is difficult to estimate all the parameters due to a lack of information in measurements. Under this condition, model prediction can be inaccurate although the current states are accurately estimated. To improve the accuracy of both state estimation and prediction, we propose a moving horizon estimation combined with a parameter subset selection scheme. In the proposed MHE framework, a subset of estimable parameters is selected within each horizon. Then, only the selected parameters are estimated along with the state variables. The proposed method is illustrated with the numerical example of a fed-batch bioreactor. The result shows that the proposed method improves the accuracy of model prediction, compared to the conventional MHE, while maintaining the state estimation performance.