空間零膨脹計數資料常伴隨過多的零值與空間相依性，常為實務分析上帶來挑戰。半參數空間零膨脹模型因不需對反應變數作特定的分布假設，因此提供了一個具彈性且穩健的建模框架。然而，在此類半參數架構中進行變數選取，至今仍缺乏具備無分布假設且計算效率高的選模準則。本研究提出一套創新的變數選取方法，結合Lasso結構並建構於無分布假設的準則之上，能有效辨識影響事件強度的顯著解釋變數，而無需對整體反應變數進行完全機率建模。我們亦對該選模準則建立理論性質，證明其在不同空間相關程度下具有正確選模的一致性。模擬實驗結果顯示，所提方法在選模正確性與計算效率上皆優於現有方法。最後，本研究應用於台灣2016年日降雨極端事件資料，驗證所提方法在實務空間資料分析中的可行性與優越性。;Analyzing spatial zero-inflated count data often requires flexible modeling frameworks to address the challenges of excess zeros and spatial dependence. Semiparametric spatial zero-inflated models provide a robust alternative by avoiding strong distributional assumptions on the response variable. However, model selection within this semiparametric framework remains a critical and unresolved issue due to the lack of appropriate criteria that are both distribution-free and computationally feasible. In this work, we propose a novel variable selection approach for semiparametric spatial zero-inflated models based on a distribution-free criterion that incorporates the structure of Lasso regression. The proposed method allows for efficient identification of covariates associated with the frequency component without requiring a fully specified likelihood. We derive the theoretical properties of the proposed criterion and demonstrate its consistency in selecting the correct model under various spatial correlation settings. Extensive simulation studies show that our method outperforms existing approaches in terms of model selection accuracy and significantly reduces computational burden. An application to Taiwan’s 2016 daily extreme rainfall data further illustrates the practical utility and scalability of the proposed approach. The results suggest that our criterion offers a promising tool for high-dimensional variable selection in complex spatial settings with zero inflation.
