本文提出三個Wald檢定探討normal inverse Gaussian (簡稱 NIG) 分配之對稱性檢定問題。由於在NIG分配下的概似函數較為複雜，我們採用Karlis (2002) 在NIG分配下提出之EM 演算法求得最大概似估計值和計算Wald統計量。模擬結果顯示其中一個Wald檢定之模擬型I錯誤機率較接近名目水準，且當分配之不對稱程度增加時，其檢定力亦隨著增加。

In this paper, we propose three Wald tests for symmetry of a NIG distribution. Due to the compliexity of the likelihood, an EM type algorithm proposed by Karlis (2002) is used to find the maximum likelihood estimates of the NIG distribution. The conclusions from the simulation study are that one of the Wald tests is usually able to achieve the desired significance levels, and the testing power increases as the asymmetry increases.