双阶段障碍模型：预测零膨胀结果

The two-stage hurdle model addresses the challenge of zero-inflated data, which is prevalent in various fields such as healthcare, economics, and social sciences. Traditional regression models often struggle with zero-inflated outcomes because they fail to effectively differentiate between the mechanisms generating zero and non-zero values. The two-stage hurdle model resolves this issue by splitting the data generation process into two stages: the first stage predicts the probability of observing a zero value, while the second stage conducts regression analysis conditioned on non-zero values. This approach allows for a more accurate capture of the data's characteristics, thereby enhancing predictive accuracy and the interpretability of the model. By applying the two-stage hurdle model, researchers can gain better insights into and predictions of zero-inflated phenomena, providing more effective decision support in relevant fields.