Documents  62M15 | enregistrements trouvés : 33

amplitude et phase du spectre croisé # co-spectre # cohérence # corrélation croisée # covariance croisée # densité spectrale # densité spectrale croisée # mélangeance # processus multivarié # processus stationnaire # rapport signal sur bruit # spectre quadratique

62M15

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loi des grands nombres # mesure spectrale # processus harmonisable # produit # série temporelle # théorème de Fubini

60F15 ; 60G10 ; 62M15

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- vi; 291 p.
ISBN 978-3-0346-0197-9

Operator theory: advances and applications , 0205

Localisation : Collection 1er étage

EDP # opérateur pseudo-différentiel # analyse complexe

30G30 ; 31A30 ; 32A25 ; 33C05 ; 42B10 ; 42B15 ; 42B35 ; 44A35 ; 46F12 ; 47 ; 35 ; 58J32 ; 58J40 ; 62M15 ; 65R10 ; 65T60 ; 92C55 ; 30E25 ; 31A10 ; 32A07 ; 32A26 ; 32A30 ; 32A40 ; 42C40 ; 45E05 ; 46F05 ; 65T50

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ISBN 978-0-12-256420-8

Localisation : Colloque 1er étage (TULS)

TIMSAC-78 # ajuster une auto-régression à temps continu à des données di # algorithme de Levinson # analyse de série temporelle # biologie # bruit # donnée astronomique # donnée de réflexion sismique image # déconvolution d'entropie minimum # estimation spectrale # filtre à réponse impulsive infinie passe-bas # fonction d'impédance de la terre # modèle ARIMA # méthode treillis # problème de diffusion inverse géophysique # prévision robuste # signal magnétique # signal à bande limitée estimation optimale # signal électronique # série temporelle stationnaire ou non # traitement de signal # transformée de Fourier TIMSAC-78 # ajuster une auto-régression à temps continu à des données di # algorithme de Levinson # analyse de série temporelle # biologie # bruit # donnée astronomique # donnée de réflexion sismique image # déconvolution d'entropie minimum # estimation spectrale # filtre à réponse impulsive infinie passe-bas # fonction d'impédance de la terre # modèle ARIMA # méthode treillis # problème de diffusion inverse géophysique # prévision robuste # ...

62-06 ; 62M10 ; 62M15

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ISBN 978-2-8028-0033-0

Travaux et recherches , 0001

Localisation : Colloque 1er étage (ROUE)

analyse de série temporelle # analyse harmonique qualitative # approximation des moindres carrés # efficacité asymptotique # fonction d'autocorrelation partielle # modèle paramétrique # mouvement brownien # méthode libre de distribution # méthode non paramétrique # probabilité de mauvais choix # processus stochastique # propriété statistique # prédiction bayesienne # solution de modèle linéaire # statistique de rang # système ARMA # sélection de modèle # test d'autorégression # test de modèle non linéaire # théorie de l'économie analyse de série temporelle # analyse harmonique qualitative # approximation des moindres carrés # efficacité asymptotique # fonction d'autocorrelation partielle # modèle paramétrique # mouvement brownien # méthode libre de distribution # méthode non paramétrique # probabilité de mauvais choix # processus stochastique # propriété statistique # prédiction bayesienne # solution de modèle linéaire # statistique de rang # système ARMA # sélection de ...

62-06 ; 62M10 ; 62M15 ; 90A20

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Research talks;Probability and Statistics

In this talk we address generalisation of stationary Hawkes processes in order to allow for a time-evolutive second-order analysis. A formal derivation of a time-frequency analysis via a time-varying Bartlett spectrum is given by introduction of the new class of locally stationary Hawkes process. This model is most appropriate for the analysis of (potentially very) long stretches of observed self-exciting point processes, as introduced in the stationary case by A. Hawkes (1971), in one dimension (temporal) or in a higher dimensional (i.e. spatial) context. Motivated by the concept of locally stationary autoregressive processes, we apply however inherently different techniques to describe and capture the time-varying dynamics of self-exciting point processes in the frequency domain. In particular we derive a stationary approximation of the Laplace transform of a locally stationary Hawkes process. This allows us to define a local intensity function and a local Bartlett spectrum which can be used to compute approximations of first and second order moments of the process. We will also present some insightful simulation studies and propose and discuss preliminary asymptotic results on how to estimate the first and second order structure of the process. Joint work with François Roueff and Laure Sansonnet

Keywords: locally stationary processes; Hawkes processes; Bartlett spectrum; time frequency analysis; point processes In this talk we address generalisation of stationary Hawkes processes in order to allow for a time-evolutive second-order analysis. A formal derivation of a time-frequency analysis via a time-varying Bartlett spectrum is given by introduction of the new class of locally stationary Hawkes process. This model is most appropriate for the analysis of (potentially very) long stretches of observed self-exciting point processes, as introduced in the ...

46N30 ; 60G55 ; 62M15

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Research talks;Probability and Statistics

Consider a non-linear function $G(X_t)$ where $X_t$ is a stationary Gaussian sequence with long-range dependence. The usual reduction principle states that the partial sums of $G(X_t)$ behave asymptotically like the partial sums of the first term in the expansion of $G$ in Hermite polynomials. In the context of the wavelet estimation of the long-range dependence parameter, one replaces the partial sums of $G(X_t)$ by the wavelet scalogram, namely the partial sum of squares of the wavelet coefficients. Is there a reduction principle in the wavelet setting, namely is the asymptotic behavior of the scalogram for $G(X_t)$ the same as that for the first term in the expansion of $G$ in Hermite polynomial? The answer is negative in general. This paper provides a minimal growth condition on the scales of the wavelet coefficients which ensures that the reduction principle also holds for the scalogram. The results are applied to testing the hypothesis that the long-range dependence parameter takes a specific value. Joint work with François Roueff and Murad S. Taqqu

Keywords: long-range dependence; long memory; self-similarity; wavelet transform; estimation; hypothesis
testing Consider a non-linear function $G(X_t)$ where $X_t$ is a stationary Gaussian sequence with long-range dependence. The usual reduction principle states that the partial sums of $G(X_t)$ behave asymptotically like the partial sums of the first term in the expansion of $G$ in Hermite polynomials. In the context of the wavelet estimation of the long-range dependence parameter, one replaces the partial sums of $G(X_t)$ by the wavelet scalogram, ...

42C40 ; 60G18 ; 62M15 ; 60G20 ; 60G22

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Research talks;Probability and Statistics

Arctic sea-ice extent has been of considerable interest to scientists in recent years, mainly due to its decreasing trend over the past 20 years. In this talk, I propose a hierarchical spatio-temporal generalized linear model (GLM) for binary Arctic-sea-ice data, where data dependencies are introduced through a latent, dynamic, spatio-temporal mixed-effects model. By using a fixed number of spatial basis functions, the resulting model achieves both dimension reduction and non-stationarity for spatial fields at different time points. An EM algorithm is used to estimate model parameters, and an MCMC algorithm is developed to obtain the predictive distribution of the latent spatio-temporal process. The methodology is applied to spatial, binary, Arctic-sea-ice data for each September over the past 20 years, and several posterior summaries are computed to detect changes of Arctic sea-ice cover. The fully Bayesian version is under development awill be discussed. Arctic sea-ice extent has been of considerable interest to scientists in recent years, mainly due to its decreasing trend over the past 20 years. In this talk, I propose a hierarchical spatio-temporal generalized linear model (GLM) for binary Arctic-sea-ice data, where data dependencies are introduced through a latent, dynamic, spatio-temporal mixed-effects model. By using a fixed number of spatial basis functions, the resulting model achieves ...

62M30 ; 62M10 ; 62M15

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Research talks;Probability and Statistics

This talk develops a new test for local white noise which also doubles as a test for the lack of aliasing in a locally stationary wavelet process. We compare and contrast our new test with the aliasing test for stationary time series due to Hinich and co-authors. We show that the test is robust to mismatch of analysis and synthesis wavelet. We demonstrate the effectiveness of the test on some simulated examples and on an example from wind energy.

42C40 ; 60G10 ; 62M10 ; 62M15

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- 594 p.
ISBN 978-0-521-68508-5

Cambridge series in statistical and probalistic mathematics

Localisation : Ouvrage RdC (PERC)

transformation # ondelettes # série chronologique # analyse # théorie du signal # analyse spectrale # filtre # transformation orthogonale

62M10 ; 42C40 ; 62-01 ; 62P35 ; 94A12 ; 62M15

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- xii; 500 p.
ISBN 978-0-03-076975-7

International series in decision processes

Localisation : Ouvrage RdC (BRIL)

série chronologique # analyse de fréquence # transformée de Fourier # estimation des spectres de puissance # série déterministe # spectre de séries à valeur vectorielle # relation linéaire invariante dans le temps # composante principale # analyse canonique

62M10 ; 62M15 ; 62-02 ; 01A75

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ISBN 978-0-8162-1104-3

Holden-day series in time series analysis and digital processing

Localisation : Disparu

analyse spectrale # construction de modele # modele lineaire non stationnair e # modele lineaire stationnaire # modele stochastique # serie chronologique

62F10 ; 62Jxx ; 62M10 ; 62M15 ; 65B10

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- 220 p.
ISBN 978-0-85264-241-2

Localisation : Ouvrage RdC (KEND)

62M10 ; 62M15

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- 323 p.
ISBN 978-0-387-90918-9

Lecture notes in statistics , 0021

Localisation : Ouvrage RdC (TONG)

analyse de données de flux de rivière # analyse de données écologiques # analyse de série temporelle non linéaire # analyse des nombres de taches solaires # extension floue # identification # modèle de seuils

62M10 ; 62M15

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- 569 p.
ISBN 978-0-19-853023-7

Localisation : Ouvrage RdC (COOL)

statistique # informatique # réseau de neurones # intelligence artificielle # mathématiques appliquées à la biologie # apprentissage # théorie de l'information # mécanique statistique

82C32 ; 62M15 ; 68Qxx ; 68Txx ; 82-02 ; 92Bxx

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- 704 p.
ISBN 978-0-471-02900-7

Wiley series in probability and mathematical statistics

Localisation : Ouvrage RdC (ANDE)

62M10 ; 62M15

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- 366 p.
ISBN 978-0-12-419250-8

Probability and mathematical statistics , 0002

Localisation : Ouvrage RdC (KOOP)

62M15

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- 190 p.

Dover books on engineering and engineering physics

Localisation : Ouvrage RdC (BLAC)

analyse spectrale # covariance des estimations # contrôle stochastique # degré de liberté # filtrage # filtre # lissage par groupe # mesure des spectres de puissance # système stochastique # séparation # transformation de Fourier # variabilité

62M15 ; 93E10 ; 93E11 ; 93E14

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- 890 p.
ISBN 978-0-12-564922-3

Probability and mathematical statistics

Localisation : Ouvrage RdC (PRIE)

analyse de processus à spectres mélangés # analyse de série temporelle # analyse harmonique généralisée # analyse spectrale # controle # estimation dans le domaine de fréquence # estimation dans le domaine du temps # filtrage # non-stationarité et non-linéarité # pratique de l'analyse spectrale # processus aléatoire stationnaire # processus multivariés et multidimensionnels # prédiction # série multivariée # série univariée # théorie des probabilités analyse de processus à spectres mélangés # analyse de série temporelle # analyse harmonique généralisée # analyse spectrale # controle # estimation dans le domaine de fréquence # estimation dans le domaine du temps # filtrage # non-stationarité et non-linéarité # pratique de l'analyse spectrale # processus aléatoire stationnaire # processus multivariés et multidimensionnels # prédiction # série multivariée # série univariée # théorie des ...

62M10 ; 62M15 ; 62M20

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