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Maximizing Complex Likelihoods via Directed Stochastic Searching Algorithm

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  • Sheng-Mao Chang

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.

Suggested Citation

  • Sheng-Mao Chang, 2014. "Maximizing Complex Likelihoods via Directed Stochastic Searching Algorithm," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(20), pages 4281-4296, October.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:20:p:4281-4296
    DOI: 10.1080/03610926.2012.724507
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