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Статті в журналах з теми "Multivariate stationary process"
MBEKE, Kévin Stanislas, and Ouagnina Hili. "Estimation of a stationary multivariate ARFIMA process." Afrika Statistika 13, no. 3 (October 1, 2018): 1717–32. http://dx.doi.org/10.16929/as/1717.130.
Повний текст джерелаCheng, R., and M. Pourahmadi. "The mixing rate of a stationary multivariate process." Journal of Theoretical Probability 6, no. 3 (July 1993): 603–17. http://dx.doi.org/10.1007/bf01066720.
Повний текст джерелаLatour, Alain. "The Multivariate Ginar(p) Process." Advances in Applied Probability 29, no. 1 (March 1997): 228–48. http://dx.doi.org/10.2307/1427868.
Повний текст джерелаLatour, Alain. "The Multivariate Ginar(p) Process." Advances in Applied Probability 29, no. 01 (March 1997): 228–48. http://dx.doi.org/10.1017/s0001867800027865.
Повний текст джерелаSun, Ying, Ning Su, and Yue Wu. "Multivariate stationary non-Gaussian process simulation for wind pressure fields." Earthquake Engineering and Engineering Vibration 15, no. 4 (November 18, 2016): 729–42. http://dx.doi.org/10.1007/s11803-016-0361-x.
Повний текст джерелаBorovkov, K., and G. Last. "On Rice's Formula for Stationary Multivariate Piecewise Smooth Processes." Journal of Applied Probability 49, no. 02 (June 2012): 351–63. http://dx.doi.org/10.1017/s002190020000913x.
Повний текст джерелаZhang, Zhengjun, and Richard L. Smith. "The behavior of multivariate maxima of moving maxima processes." Journal of Applied Probability 41, no. 4 (December 2004): 1113–23. http://dx.doi.org/10.1239/jap/1101840556.
Повний текст джерелаZhang, Zhengjun, and Richard L. Smith. "The behavior of multivariate maxima of moving maxima processes." Journal of Applied Probability 41, no. 04 (December 2004): 1113–23. http://dx.doi.org/10.1017/s0021900200020878.
Повний текст джерелаBorovkov, K., and G. Last. "On Rice's Formula for Stationary Multivariate Piecewise Smooth Processes." Journal of Applied Probability 49, no. 2 (June 2012): 351–63. http://dx.doi.org/10.1239/jap/1339878791.
Повний текст джерелаGordy, Michael B. "Finite-Dimensional Distributions of a Square-Root Diffusion." Journal of Applied Probability 51, no. 4 (December 2014): 930–42. http://dx.doi.org/10.1239/jap/1421763319.
Повний текст джерелаДисертації з теми "Multivariate stationary process"
Biron, Matthieu Etienne. "Prediction and estimation for multivariate stationary time series models." Thesis, Imperial College London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341888.
Повний текст джерелаBoulin, Alexis. "Partitionnement des variables de séries temporelles multivariées selon la dépendance de leurs extrêmes." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ5039.
Повний текст джерелаIn a wide range of applications, from climate science to finance, extreme events with a non-negligible probability can occur, leading to disastrous consequences. Extremes in climatic events such as wind, temperature, and precipitation can profoundly impact humans and ecosystems, resulting in events like floods, landslides, or heatwaves. When the focus is on studying variables measured over time at numerous specific locations, such as the previously mentioned variables, partitioning these variables becomes essential to summarize and visualize spatial trends, which is crucial in the study of extreme events. This thesis explores several models and methods for partitioning the variables of a multivariate stationary process, focusing on extreme dependencies.Chapter 1 introduces the concepts of modeling dependence through copulas, which are fundamental for extreme dependence. The notion of regular variation, essential for studying extremes, is introduced, and weakly dependent processes are discussed. Partitioning is examined through the paradigms of separation-proximity and model-based clustering. Non-asymptotic analysis is also addressed to evaluate our methods in fixed dimensions.Chapter 2 study the dependence between maximum values is crucial for risk analysis. Using the extreme value copula function and the madogram, this chapter focuses on non-parametric estimation with missing data. A functional central limit theorem is established, demonstrating the convergence of the madogram to a tight Gaussian process. Formulas for asymptotic variance are presented, illustrated by a numerical study.Chapter 3 proposes asymptotically independent block (AI-block) models for partitioning variables, defining clusters based on the independence of maxima. An algorithm is introduced to recover clusters without specifying their number in advance. Theoretical efficiency of the algorithm is demonstrated, and a data-driven parameter selection method is proposed. The method is applied to neuroscience and environmental data, showcasing its potential.Chapter 4 adapts partitioning techniques to analyze composite extreme events in European climate data. Sub-regions with dependencies in extreme precipitation and wind speed are identified using ERA5 data from 1979 to 2022. The obtained clusters are spatially concentrated, offering a deep understanding of the regional distribution of extremes. The proposed methods efficiently reduce data size while extracting critical information on extreme events.Chapter 5 proposes a new estimation method for matrices in a latent factor linear model, where each component of a random vector is expressed by a linear equation with factors and noise. Unlike classical approaches based on joint normality, we assume factors are distributed according to standard Fréchet distributions, allowing a better description of extreme dependence. An estimation method is proposed, ensuring a unique solution under certain conditions. An adaptive upper bound for the estimator is provided, adaptable to dimension and the number of factors
Частини книг з теми "Multivariate stationary process"
Masry, Elias. "Multivariate Probability Density and Regression Functions Estimation of Continuous-Time Stationary Processes from Discrete-Time Data." In Stochastic Processes and Related Topics, 297–314. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-2030-5_17.
Повний текст джерелаDorndorf, Alexander, Boris Kargoll, Jens-André Paffenholz, and Hamza Alkhatib. "Bayesian Robust Multivariate Time Series Analysis in Nonlinear Regression Models with Vector Autoregressive and t-Distributed Errors." In International Association of Geodesy Symposia. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/1345_2023_210.
Повний текст джерелаMerlevède, Florence, Magda Peligrad, and Sergey Utev. "Gaussian Approximation under Asymptotic Negative Dependence." In Functional Gaussian Approximation for Dependent Structures, 277–302. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198826941.003.0009.
Повний текст джерелаArnold, Stevan J. "Evolution of Multiple Traits on a Stationary Adaptive Landscape." In Evolutionary Quantitative Genetics, 236–60. Oxford University PressOxford, 2023. http://dx.doi.org/10.1093/oso/9780192859389.003.0014.
Повний текст джерелаFranses, Philip Hans. "Periodic Cointegration." In Periodicity and Stochastic Trends In Economic Time Series, 177–210. Oxford University PressOxford, 1996. http://dx.doi.org/10.1093/oso/9780198774532.003.0009.
Повний текст джерелаТези доповідей конференцій з теми "Multivariate stationary process"
Stefanakos, Christos N., and Konstandinos A. Belibassakis. "Nonstationary Stochastic Modelling of Multivariate Long-Term Wind and Wave Data." In ASME 2005 24th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2005. http://dx.doi.org/10.1115/omae2005-67461.
Повний текст джерелаWang, Junzhe, Shyam Kareepadath Sajeev, Evren Ozbayoglu, Silvio Baldino, Yaxin Liu, and Haorong Jing. "Reducing NPT Using a Novel Approach to Real-Time Drilling Data Analysis." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215028-ms.
Повний текст джерелаJosupeit, Judith. "Does Pinocchio get Cybersickness? The Mitigating Effect of a Virtual Nose on Cybersickness." In AHFE 2023 Hawaii Edition. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004445.
Повний текст джерелаYang, Yingnan, Qingling Zhu, and Jianyong Chen. "VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/590.
Повний текст джерелаTopchii, M., A. Bondarev, and A. Degterev. "New Approach for the Probabilistic Assessment of Organic Matter in the Source Rocks of the Bazhenov Formation for Estimation of Shale Hydrocarbons Resources." In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216937-ms.
Повний текст джерелаЗвіти організацій з теми "Multivariate stationary process"
Miamee, A. G., and M. Pourahmadi. Degenerate Multivariate Stationary Processes: Basicity, Past and Future, and Autoregressive Representation. Fort Belvoir, VA: Defense Technical Information Center, May 1985. http://dx.doi.org/10.21236/ada158879.
Повний текст джерелаMiamee, A. G. On Determining the Predictor of Non-Full-Rank Multivariate Stationary Random Processes. Fort Belvoir, VA: Defense Technical Information Center, March 1985. http://dx.doi.org/10.21236/ada159165.
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