 Linearly preconditioned nonlinear conjugate gradient acceleration of the PX-EM algorithmLin Zhou and Yayong Tang Computational Statistics & Data Analysis, 2021, vol. 155, issue C Abstract: The EM algorithm is a widely applicable algorithm for modal estimation but often criticized for its slow convergence. A new hybrid accelerator named APX-EM is proposed for speeding up the convergence of EM algorithm, which is based on both Linearly Preconditioned Nonlinear Conjugate Gradient (PNCG) and PX-EM algorithm. The intuitive idea is that, each step of the PX-EM algorithm can be viewed approximately as a generalized gradient just like the EM algorithm, then the linearly PNCG method can be used to accelerate the EM algorithm. Essentially, this method is an adjustment of the AEM algorithm, and it usually achieves a faster convergence rate than the AEM algorithm by sacrificing a little simplicity. The convergence of the APX-EM algorithm, includes a global convergence result for this method under suitable conditions, is discussed. This method is illustrated for factor analysis and a random-effects model. Keywords: PX-EM algorithm; AEM algorithm; Factor analysis; Random effects; Generalized gradient; Linearly PNCG algorithm (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:155:y:2021:i:c:s016794732030147x DOI: 10.1016/j.csda.2020.107056 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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