An Efficient Stochastic Approximation Algorithm and Model Selection for a Class of Nonlinear Random Effect Models with Incomplete Responses
A stochastic approximation expectation maximum (EM) algorithm is proposed to obtain Maximum likelihood (ML) estimation of nonlinear random effect models in which the manifest variables are distributed as a reproductive dispersion model (RDM) and may be missing with ignorable missingness mechanism in this paper. A method composed of simulation step as well as stochastic approximation step is used to obtain the conditional expectation, whereas the M-step is executed via the method of conditional maximization. The most attractive point of this approach is that it is novel and non-trivial, which can be used to obtain the ML estimates and the estimation of standard errors simultaneously. Moreover, A model selection criterion is developed. Empirical results are used to illustrate the usefulness of the methodologies proposed above.
L. Dai and Y. Z. Fu, "An Efficient Stochastic Approximation Algorithm and Model Selection for a Class of Nonlinear Random Effect Models with Incomplete Responses", Applied Mechanics and Materials, Vols. 110-116, pp. 2670-2676, 2012