經驗概似(empirical likelihood)函數為一種不需知道母體分配的概似函數。進一步調整後的經驗概似函數能在小樣本數或估計的參數個數過多時，更快的達到大樣本近似常態性質。同樣地，Royall and Tsou (2003) 提出的有母數的強韌概似函數也提供了不需母體分配假設下完整的統計推論。 我們針對兩種概似函數做了通盤的比較，並說明上述有母數的強韌概似函數在各方面皆優於經驗概似函數。 ;Empirical likelihood is a distribution-free approach that allows one to construct likelihood functions without knowing the true underlying distribution. Modification has been proposed to ensure that the large sample property is better achieved when sample size is not large or when there are many parameters. Alternatively, one can employ the parametric robust likelihood procedure proposed by Royall and Tsou (2003) to make likelihood inference under model misspecification. We give a thorough comparison between the two model-independent robust likelihood approaches and show that the method by Royall and Tsou (2003) is superior to the empirical likelihood in terms of various performance benchmarks.
