An Efficient Stochastic Approximation Algorithm and Model Selection for a Class of Nonlinear Random Effect Models with Incomplete Responses

Abstract:

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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.

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Periodical:

Edited by:

Wu Fan

Pages:

2670-2676

DOI:

10.4028/www.scientific.net/AMM.110-116.2670

Citation:

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

Online since:

October 2011

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$35.00

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