| Abstract: | We propose a semiparametric version of the EM algorithm under the semiparametric mixture model introduced by Anderson (1979, Biometrika , 66 , 17-26). It is shown that the sequence of proposed EM iterates, irrespective of the starting value, converges to the maximum semiparametric likelihood estimator of the vector of parameters in the semiparametric mixture model. The proposed EM algorithm preserves the appealing monotone convergence property of the standard EM algorithm and can be implemented by employing the standard logistic regression program. We present one example to demonstrate the performance of the proposed EM algorithm. |
