Over-the-air computation (AirComp) is widely used for model aggregation in wireless distributed learning. Although it enhances communication efficiency, we believe the AirComp aggregation has limited effectiveness due to the difference between its target problem and that of distributed learning. In this paper, we develop a rigorous formulation for optimal model aggregation in wireless distributed learning. Using this formulation, we show that AirComp aggregation generally assumes a mismatched statistical model for local parameters. We then propose a statistical framework for model aggregation, called global unknown estimation (GUE). It captures the statistical relation between the local and global model parameters, allowing to interpret model aggregation as an inference task. We validate the efficiency of GUE through numerical experiments. Our results show that, in the low SNR regime, GUE can reduce the required power for model aggregation by approximately 15 dB compared to AirComp aggregation. Remarkably, this gain is obtained without additional computational overhead