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dc.contributor.authorBacri, Timothee Raphael Ferdinand
dc.contributor.authorBerentsen, Geir Drage
dc.contributor.authorBulla, Jan
dc.contributor.authorStøve, Bård
dc.description.abstractA popular way to estimate the parameters of a hidden Markov model (HMM) is direct numerical maximization (DNM) of the (log-)likelihood function. The advantages of employing the TMB [Kristensen K, Nielsen A, Berg C, et al. TMB: automatic differentiation and Laplace approximation. J Stat Softw Articles. 2016;70(5):1–21] framework in R for this purpose were illustrated recently [Bacri T, Berentsen GD, Bulla J, et al. A gentle tutorial on accelerated parameter and confidence interval estimation for hidden Markov models using template model builder. Biom J. 2022 Oct;64(7):1260–1288]. In this paper, we present extensions of these results in two directions. First, we present a practical way to obtain uncertainty estimates in form of confidence intervals (CIs) for the so-called smoothing probabilities at moderate computational and programming effort via TMB. Our approach thus permits to avoid computer-intensive bootstrap methods. By means of several examples, we illustrate patterns present for the derived CIs. Secondly, we investigate the performance of popular optimizers available in R when estimating HMMs via DNM. Hereby, our focus lies on the potential benefits of employing TMB. Investigated criteria via a number of simulation studies are convergence speed, accuracy, and the impact of (poor) initial values. Our findings suggest that all optimizers considered benefit in terms of speed from using the gradient supplied by TMB. When supplying both gradient and Hessian from TMB, the number of iterations reduces, suggesting a more efficient convergence to the maximum of the log-likelihood. Last, we briefly point out potential advantages of a hybrid approach.en_US
dc.publisherTaylor & Francisen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleComputational issues in parameter estimation for hidden Markov models with template model builderen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.source.journalJournal of Statistical Computation and Simulationen_US
dc.identifier.citationJournal of Statistical Computation and Simulation. 2023, 93 (18), 3421-3457.en_US

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal