Maximizing complex likelihoods via directed stochastic searching algorithm

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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 closedform representation in both the simulations and the real cases. Finally, the limitations and the consequences when multiple solutions exist are addressed.

Original languageEnglish
Pages (from-to)4281-4296
Number of pages16
JournalCommunications in Statistics - Theory and Methods
Issue number20
Publication statusPublished - 2014 Oct 13

All Science Journal Classification (ASJC) codes

  • Statistics and Probability


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