Model-order selection

Stoica, Petre; Selén, Y.

doi:10.1109/msp.2004.1311138

Cited by 986 publications

(145 citation statements)

References 22 publications

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138

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Order By: Relevance

“…The best frequency with its corresponding amplitudes B k and C k are then used as a starting point for the refined search, which is a standard nonlinear Marquardt iteration. The optimal order K for the model is chosen using the Bayesian information criterion (Stoica & Selén 2004).…”

Section: The Continuous Period Search Methodsmentioning

confidence: 99%

Spot activity of II Peg

et al. 2011

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We have studied the long-term spot activity of the RS CVn star II Peg by means of Doppler imaging based on spectroscopy and time series analysis of photometry. We present 28 Doppler imaging temperature maps spanning the years 1994-2010, of which 14 were calculated for the present study. The longitudinal spot distribution, derived from the surface temperature maps, is compared with epochs of the light curve minima, derived from photometric observations. We detect a longitudinal drift in the major spot structure during 1995-2003. After this there is a clear decrease in the activity level and no clear drift can be seen. We conclude that the variations could be caused by a cyclic behaviour of the underlying magnetic dynamo.

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Section: The Continuous Period Search Methodsmentioning

confidence: 99%

Spot activity of II Peg

et al. 2011

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“…It is also possible to choose Q automatically on a voxel basis by utilizing an order selection method, for example, the Bayesian Information Criterion [24]. This is, however, beyond the scope of this paper.…”

Section: Welpementioning

confidence: 99%

New approach to phase correction in multi-echoT2relaxometry

Björk

Stoica

2014

Journal of Magnetic Resonance

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“…One such tool is the Akaike information criterion (AIC). The AIC and its offshoots appear often in the ecological and biological sciences (Arnold, 2010), signal processing (Stoica & Sel, 2004), artificial intelligence (Zhao et al, 2008), and increasingly in astrophysics (Liddle, 2007;Wei, 2010). The AIC has also appeared in a peak extraction algorithm developed within the metabolomics community (Morohashi et al, 2007).…”

Section: The Akaike Information Criterionmentioning

confidence: 99%

Algorithm for systematic peak extraction from atomic pair distribution functions

2015

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The study presents an algorithm, ParSCAPE, for model-independent extraction of peak positions and intensities from atomic pair distribution functions (PDFs). It provides a statistically motivated method for determining parsimony of extracted peak models using the information-theoretic Akaike information criterion (AIC) applied to plausible models generated within an iterative framework of clustering and chi-square fitting. All parameters the algorithm uses are in principle known or estimable from experiment, though careful judgment must be applied when estimating the PDF baseline of nanostructured materials. ParSCAPE has been implemented in the Python program SrMise. Algorithm performance is examined on synchrotron X-ray PDFs of 16 bulk crystals and two nanoparticles using AIC-based multimodeling techniques, and particularly the impact of experimental uncertainties on extracted models. It is quite resistant to misidentification of spurious peaks coming from noise and termination effects, even in the absence of a constraining structural model. Structure solution from automatically extracted peaks using the Liga algorithm is demonstrated for 14 crystals and for C60. Special attention is given to the information content of the PDF, theory and practice of the AIC, as well as the algorithm's limitations.

show abstract

Model-order selection

Cited by 986 publications

References 22 publications

Spot activity of II Peg

Spot activity of II Peg

New approach to phase correction in multi-echoT2relaxometry

Algorithm for systematic peak extraction from atomic pair distribution functions

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