Maximizing complex likelihoods via directed stochastic searching algorithm

研究成果: Article同行評審

摘要

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.

原文English
頁(從 - 到)4281-4296
頁數16
期刊Communications in Statistics - Theory and Methods
43
發行號20
DOIs
出版狀態Published - 2014 十月 13

All Science Journal Classification (ASJC) codes

  • 統計與概率

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