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Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors / Boris Kargoll in Journal of geodesy, vol 94 n° 5 (May 2020)
Titre : Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors Type de document : Article/Communication Auteurs : Boris Kargoll, Auteur ; Gaël Kermarrec, Auteur ; Hamza Alkhatib, Auteur ; Johannes Korte, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes descripteurs IGN] algorithme espérance-maximisation
[Termes descripteurs IGN] analyse vectorielle
[Termes descripteurs IGN] autorégression
[Termes descripteurs IGN] bruit blanc
[Termes descripteurs IGN] corrélation croisée normalisée
[Termes descripteurs IGN] erreur aléatoire
[Termes descripteurs IGN] méthode de Monte-Carlo
[Termes descripteurs IGN] modèle stochastique
[Termes descripteurs IGN] régression linéaire
[Termes descripteurs IGN] série temporelle
[Termes descripteurs IGN] station GPS
[Termes descripteurs IGN] valeur aberranteRésumé : (auteur) The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. https://doi.org/10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN). Numéro de notice : A2020-291 Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01376-6 date de publication en ligne : 10/05/2020 En ligne : https://doi.org/10.1007/s00190-020-01376-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95120
in Journal of geodesy > vol 94 n° 5 (May 2020)[article]
Titre : Volcano-seismic transfer learning and uncertainty quantification with bayesian neural networks Type de document : Article/Communication Auteurs : Angel Bueno, Auteur ; Carmen Benitez, Auteur ; Silvio De Angelis, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] classification bayesienne
[Termes descripteurs IGN] classification par réseau neuronal
[Termes descripteurs IGN] forme d'onde
[Termes descripteurs IGN] incertitude des données
[Termes descripteurs IGN] réseau bayesien
[Termes descripteurs IGN] réseau de neurones profond
[Termes descripteurs IGN] Russie
[Termes descripteurs IGN] séisme
[Termes descripteurs IGN] sismologie
[Termes descripteurs IGN] surveillance géologique
[Termes descripteurs IGN] volcanologie
[Termes descripteurs IGN] Washington (Etats-Unis ; état)Résumé : (auteur) Over the past few years, deep learning (DL) has emerged as an important tool in the fields of volcano and earthquake seismology. However, these methods have been applied without performing thorough analyses of the associated uncertainties. Here, we propose a solution to enhance volcano-seismic monitoring systems, through probabilistic Bayesian DL; we implement and demonstrate a workflow for waveform classification, rapid quantification of the associated uncertainty, and link these uncertainties to changes in volcanic unrest. Specifically, we introduce Bayesian neural networks (BNNs) to perform event identification, classification, and their estimated uncertainty on data gathered at two active volcanoes, Mount St. Helens, Washington, USA, and Bezymianny, Kamchatka, Russia. We demonstrate how BNNs achieve excellent performance (92.08%) in discriminating both the type of event and its origin when the two data sets are merged together, and no additional training information is provided. Finally, we demonstrate that the data representations learned by the BNNs are transferable across different eruptive periods. We also find that the estimated uncertainty is related to changes in the state of unrest at the volcanoes and propose that it could be used to gauge whether the learned models may be exported to other eruptive scenarios. Numéro de notice : A2020-094 Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2941494 date de publication en ligne : 07/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2941494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94657
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp[article]
Titre : Applied and computational statistics Type de document : Monographie Auteurs : Sorana D. Bolboacă, Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 104 p. ISBN/ISSN/EAN : 978-3-03928-177-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes descripteurs IGN] méthode de Monte-Carlo
[Termes descripteurs IGN] méthode du maximum de vraisemblance (estimation)
[Termes descripteurs IGN] probabilitéRésumé : (Editeur) Research without statistics is like water in the sand; the latter is necessary to reap the benefits of the former. This collection of articles is designed to bring together different approaches to applied statistics. The studies presented in this book are a tiny piece of what applied statistics means and how statistical methods find their usefulness in different fields of research from theoretical frames to practical applications such as genetics, computational chemistry, and experimental design. This book presents several applications of the statistics: A new continuous distribution with five parameters—the modified beta Gompertz distribution; A method to calculate the p-value associated with the Anderson–Darling statistic; An approach of repeated measurement designs; A validated model to predict statement mutations score; A new family of structural descriptors, called the extending characteristic polynomial (EChP) family, used to express the link between the structure of a compound and its properties. This collection brings together authors from Europe and Asia with a specific contribution to the knowledge in regards to theoretical and applied statistics. Note de contenu :
- The Modified Beta Gompertz Distribution: Theory and Applications
- Computation of Probability Associated with Anderson–Darling Statistic
- Optimal Repeated Measurements for Two Treatment Designs with Dependent Observations: The Case of Compound Symmetry
- A Model for Predicting Statement Mutation Scores
- Extending the Characteristic Polynomial for Characterization of C20 Fullerene CongenersNuméro de notice : 26298 Thématique : MATHEMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03928-177-0 date de publication en ligne : 30/01/2020 En ligne : https://doi.org/10.3390/books978-3-03928-177-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95014
Titre : A breakpoint detection in the mean model with heterogeneous variance on fixed time-intervals Type de document : Article/Communication Auteurs : Olivier Bock Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 1 - 13 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes descripteurs IGN] analyse de variance
[Termes descripteurs IGN] données GNSS
[Termes descripteurs IGN] inférence statistique
[Termes descripteurs IGN] méthode robuste
[Termes descripteurs IGN] points de rupture
[Termes descripteurs IGN] processus gaussien
[Termes descripteurs IGN] segmentation
[Termes descripteurs IGN] série temporelle
[Termes descripteurs IGN] variabilitéRésumé : (Auteur) This work is motivated by an application for the homogenization of global navigation satellite system (GNSS)-derived integrated water vapour series. Indeed, these series are affected by abrupt changes due to equipment changes or environmental effects. The detection and correction of the series from these changes are a crucial step before any use for climate studies. In addition to these abrupt changes, it has been observed in the series a non-stationary of the variability. We propose in this paper a new segmentation model that is a breakpoint detection in the mean model of a Gaussian process with heterogeneous variance on known time intervals. In this segmentation case, the dynamic programming algorithm used classically to infer the breakpoints cannot be applied anymore. We propose a procedure in two steps: we first estimate robustly the variances and then apply the classical inference by plugging these estimators. The performance of our proposed procedure is assessed through simulation experiments. An application to real GNSS data is presented. Numéro de notice : A2019-165 Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11222-019-09853-5 date de publication en ligne : 03/05/2019 En ligne : https://doi.org/10.1007/s11222-019-09853-5 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92571
in Statistics and Computing > vol 29 Online First (May 2019) . - pp 1 - 13[article]
Titre : Enhancing the predictability of least-squares collocation through the integration with least-squares-support vector machine Type de document : Article/Communication Auteurs : Hossam Talaat Elshambaky, Auteur Année de publication : 2019 Article en page(s) : pp 1 - 15 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes descripteurs IGN] classification par réseau neuronal
[Termes descripteurs IGN] classification par séparateurs à vaste marge
[Termes descripteurs IGN] collocation par moindres carrés
[Termes descripteurs IGN] covariance
[Termes descripteurs IGN] Egypte
[Termes descripteurs IGN] fonction de base radiale
[Termes descripteurs IGN] géoïde localRésumé : (Auteur) Least-squares collocation (LSC) is a crucial mathematical tool for solving many geodetic problems. It has the capability to adjust, filter, and predict unknown quantities that affect many geodetic applications. Hence, this study aims to enhance the predictability property of LSC through applying soft computing techniques in the stage of describing the covariance function. Soft computing techniques include the support vector machine (SVM), least-squares-support vector machine (LS-SVM), and artificial neural network (ANN). A real geodetic case study is used to predict a national geoid from the EGM2008 global geoid model in Egypt. A comparison study between parametric and soft computing techniques was performed to assess the LSC predictability accuracy. We found that the predictability accuracy increased when using soft computing techniques in the range of 10.2 %–27.7 % and 8.2 %–29.8 % based on the mean square error and the mean error terms, respectively, compared with the parametric models. The LS-SVM achieved the highest accuracy among the soft computing techniques. In addition, we found that the integration between the LS-SVM with LSC exhibits an accuracy of 20 % and 25 % higher than using LS-SVM independently as a predicting tool, based on the mean square error and mean error terms, respectively. Consequently, the LS-SVM integrated with LSC is recommended for enhanced predictability in geodetic applications. Numéro de notice : A2019-132 Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2018-0017 date de publication en ligne : 25/08/2018 En ligne : https://doi.org/10.1515/jag-2018-0017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92462
in Journal of applied geodesy > vol 13 n° 1 (January 2019) . - pp 1 - 15[article]PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalink
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