Maximizing Complex Likelihoods via Directed Stochastic Searching Algorithm

Sheng-Mao Chang

Communications in Statistics - Theory and Methods, 2014, vol. 43, issue 20, 4281-4296

Abstract: In this article, a directed stochastic searching algorithm is defined. It is a root or optimal parameter searching algorithm with stochastic searching directions. This algorithm is especially relevant when the objective function is complex or is observed with errors. We prove that the resulting roots or estimators have well-controlled biases under certain conditions. We examine the proposed method by finding the maximum likelihood estimates for which the corresponding likelihood function has or does not have a closed-form representation in both the simulations and the real cases. Finally, the limitations and the consequences when multiple solutions exist are addressed.

Date: 2014
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DOI: 10.1080/03610926.2012.724507

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