Estimating mixtures of normal distributions via empirical characteristic function
利用经验特征函数方法估计正态分布混合模型的参数，该方法数值稳定，在最大似然估计失效时仍表现良好，并通过纽约证券交易所月度超额收益数据进行了实证应用。
This paper uses the empirical characteristic function (ECF) procedure to estimate the parameters of mixtures of normal distributions. Since the characteristic function is uniformly bounded, the procedure gives estimates that are numerically stable. It is shown that, using Monte Carlo simulation, the finite sample properties of th ECF estimator are very good, even in the case where the popular maximum likelihood estimator fails to exist. An empirical application is illustrated using the monthl excess return of the Nyse value-weighted index.