The normalized expectation-maximization (N-EM) algorithm

Although the $\textit{expectation-maximization}$ (EM) algorithm is a powerful optimization tool in statistics, it can only be applied to missing/incomplete data problems or to problems with a latent-variable structure.

It is well known that the introduction of latent variables (or the data augmentation) is an art; i.e., it could only be done case by case.

In this paper, we propose a new algorithm, a so-called $\textit{normalized EM}$ (N-EM) algorithm, for a class of log-likelihood functions with integrals.

As an extension of the original EM algorithm, the N-EM algorithm inherits all advantages of EM-type algorithms and consists of three steps: normalization step (N-step), expectation step (E-step) and maximization step (M-step), where the N-step is to construct a $\textit{normalized density function}$ (ndf), the E-step is to compute a well-established surrogate $Q$-function and the M-step is to maximize the $Q$-function as in the original EM algorithm.

The ascent property, the best choice of the ndf, and those N-EM algorithms with a difficult M-step are also explored.

By multiple real applications, we have shown that the N-EM algorithm can solve some problems which cannot be addressed by the EM algorithm.

Next, for problems to which the EM can be applied (often case by case), the N-EM algorithm can be employed in a unified framework.

Numerical experiments are performed and convergence properties are also established.