Let {Xi:-∞<i<∞}be a stationary sequence of random variables. Let Fn(x) be the corresponding empirical distribution function of X1,…,Xn,and let X=Σi=1 Xi/n be the sample mean. In this paper,we derive the asymptotic almost sure representation, the central limit theorem, a law of iterated logarithm, a Wiener process embedding and an invariant principle for Fn(X) under different Φ-mixing conditios. 設{xi :-∞<i<∞} 為一穩定的隨機變數序例，Fn(x)為對應X1，…，Xn的經驗累積分配函數，X為x1，…， Xn的樣本平均。此論文中，我們導出在不同Φ-混合情況下， Fn(X)的幾乎處處近似表現，中央極限理論，對數反覆律，Wiener隨機對應和不變原理等性質。
