m

F Nous contacter

0

Documents  62M30 | enregistrements trouvés : 13

O
     

-A +A

P Q

Déposez votre fichier ici pour le déplacer vers cet enregistrement.

Research talks;Combinatorics;Probability and Statistics

62M30 ; 60G55 ; 62C20 ; 05C38

... Lire [+]

Déposez votre fichier ici pour le déplacer vers cet enregistrement.

- pag. mult.

Localisation : Colloque 1er étage (MARS)

analyse statistique # donnée spaciale # donnée régionale # modèle du second ordre # champs de Gibbs latticiel # champ de Markov # processus spacial # modèle spaciale # auto-régression # géostatistique # simulation # prédiction # discontinuité statistique # application

62-06 ; 62H11 ; 62M30

... Lire [+]

Déposez votre fichier ici pour le déplacer vers cet enregistrement.

- 202 p.
ISBN 978-0-387-00136-4

Lecture notes in statistics , 0173

Localisation : Colloque 1er étage (AALB)

statistique # statistique spatiale # simulation # génération aléatoire # inférence bayesienne # processus spatial # chaîne de Markov # Monte Carlo # MCMC # analyse d'image # analyse de variance # géostatistique

62-02 ; 62-06 ; 62F15 ; 62H11 ; 62J10 ; 62M30 ; 62M40

... Lire [+]

Déposez votre fichier ici pour le déplacer vers cet enregistrement.

- xxiv; 446 p.
ISBN 978-3-642-33304-0

Lecture notes in mathematics , 2068

Localisation : Collection 1er étage

géométrie stochastique # analyse spatiale # champs aléatoires # statistiques spatiales

60D05 ; 52A22 ; 60G55 ; 60G60 ; 60G57 ; 60F05 ; 60F15 ; 60J25 ; 62M30 ; 65C40 ; 60-06 ; 00B25

... Lire [+]

Déposez votre fichier ici pour le déplacer vers cet enregistrement.

Research talks;Probability and Statistics

In this talk we introduce a class of statistics for spatial data that is observed on an irregular set of locations. Our aim is to obtain a unified framework for inference and the statistics we consider include both parametric and nonparametric estimators of the spatial covariance function, Whittle likelihood estimation, goodness of fit tests and a test for second order spatial stationarity. To ensure that the statistics are computationally feasible they are defined within the Fourier domain, and in most cases can be expressed as a quadratic form of a discrete Fourier-type transform of the spatial data. Evaluation of such statistic is computationally tractable, requiring $O(nb)$ operations, where $b$ are the number Fourier frequencies used in the definition of the statistic (which varies according to the application) and $n$ is the sample size. The asymptotic sampling properties of the statistics are derived using mixed spatial asymptotics, where the number of locations grows at a faster rate than the size of the spatial domain and under the assumption that the spatial random field is stationary and the irregular design of the locations are independent, identically distributed random variables. We show that there are quite intriguing differences in the behaviour of the statistic when the spatial process is Gaussian and non-Gaussian. In particular, the choice of the number of frequencies $b$ in the construction of the statistic depends on whether the spatial process is Gaussian or not. If time permits we describe how the results can also be used in variance estimation. And if we still have time some simulations and real data will be presented. In this talk we introduce a class of statistics for spatial data that is observed on an irregular set of locations. Our aim is to obtain a unified framework for inference and the statistics we consider include both parametric and nonparametric estimators of the spatial covariance function, Whittle likelihood estimation, goodness of fit tests and a test for second order spatial stationarity. To ensure that the statistics are computationally ...

62M10 ; 62M30 ; 62F12 ; 62G05

... Lire [+]

Déposez votre fichier ici pour le déplacer vers cet enregistrement.

Research talks

With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatiotemporal process models have become widely deployed statistical tools for researchers to better understand the complex nature of spatial and temporal variability. However, fitting hierarchical spatiotemporal models often involves expensive matrix computations with complexity increasing in cubic order for the number of spatial locations and temporal points. This renders such models unfeasible for large data sets. I will present a focused review of two methods for constructing well-defined highly scalable spatiotemporal stochastic processes. Both these processes can be used as ``priors" for spatiotemporal random fields. The first approach constructs a low-rank process operating on a lower-dimensional subspace. The second approach constructs a Nearest-Neighbor Gaussian Process (NNGP) that ensures sparse precision matrices for its finite realizations. Both processes can be exploited as a scalable prior embedded within a rich hierarchical modeling framework to deliver full Bayesian inference. These approaches can be described as model-based solutions for big spatiotemporal datasets. The models ensure that the algorithmic complexity has n floating point operations (flops), where n is the number of spatial locations (per iteration). We compare these methods and provide some insight into their methodological underpinnings. With the growing capabilities of Geographic Information Systems (GIS) and user-friendly software, statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points. Over the last decade, hierarchical spatiotemporal process models have become widely deployed statistical tools for researchers to better understand the complex nature of spatial and temporal ...

62P12 ; 62M30 ; 62F15

... Lire [+]

Déposez votre fichier ici pour le déplacer vers cet enregistrement.

Research talks

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

... Lire [+]

Déposez votre fichier ici pour le déplacer vers cet enregistrement.

- 430 p.
ISBN 978-2-7178-1632-7

Collection économie et statistiques avancées

Localisation : Ouvrage RdC (GOUR)

données spatio-temporelle qualitative # modèle Tobit # modèle de Poisson # modèle de durée # modèle de déséquilibre sur le marché # modèle dichotomique simple # modèle lag- linéaire # modélisation # méthode d'estimation # méthode du chi 2 minimum # système d'équations simultanées # variable discrète positive # variable latentetronquée # variable qualitative # économétrie

62-XX ; 62M10 ; 62M30 ; 62P05 ; 90-XX

... Lire [+]

Déposez votre fichier ici pour le déplacer vers cet enregistrement.

- 247 p.
ISBN 978-0-387-98629-6

Springer Series in Statistics

Localisation : Ouvrage RdC (STEI)

donnée statistique spatiale # interpolation # processus spatial # prédiction linaire # champ aléatoire # mesure gaussienne # prédiction avec paramètre estimé # analyse d'image

62M30 ; 32-02 ; 62M40

... Lire [+]

Déposez votre fichier ici pour le déplacer vers cet enregistrement.

- xvii; 308 p.
ISBN 978-3-540-79225-3

Mathématiques & applications , 0063

Localisation : Collection 1er étage

analyse spatiale # méthode statistique # distribution # modèle latticiel # variogramme # auto-régression # champ de Gibbs-Markov # processus ponctuel # algorithme MCMC

62M30 ; 62H11 ; 86A32 ; 62-02 ; 65C40 ; 65C05 ; 00A71

... Lire [+]

Déposez votre fichier ici pour le déplacer vers cet enregistrement.

- 282 p.
ISBN 978-0-387-95240-6

Lecture notes in statistics , 0159

Localisation : Ouvrage RdC (Spat)

statistique # statistique spactiale # traitement d'image # analyse d'image # processus stochastique # biométrie # modélisation graphique # distribution # robustesse # coordination spatiale # corps aléatoire # théroème limite

62-06 ; 62H11 ; 62M30 ; 60G60

... Lire [+]

Déposez votre fichier ici pour le déplacer vers cet enregistrement.

- 300 p.
ISBN 978-1-58488-265-7

Monographs on statistics and applied probability , 0100

Localisation : Ouvrage RdC (MOLL)

statistique # processu ponctuel # analyse spatiale # méthode de Monte-Carlo # chaîne de Markov # processus ponctuel de Poisson # processus de Cox # algorithme de Metropolis-Hastings # inférence # simulation # MCMC # théorie de la mesure

60-02 ; 60G55 ; 62M30 ; 65L05

... Lire [+]

Déposez votre fichier ici pour le déplacer vers cet enregistrement.

- 900 p.
ISBN 978-0-471-84336-8

Wiley series in probability and mathematical statistics

Localisation : Ouvrage RdC (CRESS)

analyse des données # donnée # méthode de statistique spaciale # statistique spaciale # traitement de données statistique spaciale

62-04 ; 62-07 ; 62M30 ; 68P05 ; 68Pxx

... Lire [+]

Z