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We estimate the general influence functions for spatio-temporal Hawkes processes using a tensor recovery approach by formulating the location dependent influence function that captures the influence of historical events as a tensor kernel.
Some mathematical notes on three-mode factor analysis
Ledyard R Tucker · 1966
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Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition
J Douglas Carroll and Jih-Jie Chang · 1970
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Foundations of the parafac procedure: Models and conditions for an” explanatory” multimodal factor analysis
Richard A Harshman et al · 1970
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Space-time point-process models for earthquake occurrences
Yosihiko Ogata · 1998
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Stephen Boyd and Lieven Vandenberghe · 2004
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Concentration inequalities and martingale inequalities: a survey
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Adaptive estimation for hawkes processes; application to genome analysis
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Tensor completion and low-n-rank tensor recovery via convex optimization
Silvia Gandy, Benjamin Recht, and Isao Yamada · 2011
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Factorization strategies for third-order tensors
Misha E Kilmer and Carla D Martin · 2011
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Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion
Vladimir Koltchinskii, Karim Lounici, Alexandre B Tsybakov, et al · 2011
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Tensor completion for estimating missing values in visual data
Ji Liu, Przemyslaw Musialski, Peter Wonka, and Jieping Ye · 2012
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Hankel matrix rank minimization with applications to system identification and realization
Maryam Fazel, Ting Kei Pong, Defeng Sun, and Paul Tseng · 2013
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Accelerating maximum likelihood estimation for hawkes point processes
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Critical reflexivity in financial markets: a hawkes process analysis
Stephen J Hardiman, Nicolas Bercot, and Jean-Philippe Bouchaud · 2013
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Most tensor problems are np-hard
Christopher J Hillar and Lek-Heng Lim · 2013
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Yang Cao and Yao Xie · 2015
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Poisson matrix recovery and completion
Y. Cao and Y. Xie · 2016
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Tideh: Time-dependent hawkes process for predicting retweet dynamics
Ryota Kobayashi and Renaud Lambiotte · 2016
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Exact tensor completion using t-svd
Zemin Zhang and Shuchin Aeron · 2016
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Efficient tensor completion for color image and video recovery: Low-rank tensor train
Johann A Bengua, Ho N Phien, Hoang Duong Tuan, and Minh N Do · 2017
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An estimation procedure for the hawkes process
Matthias Kirchner · 2017
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A new convex relaxation for tensor completion
Bernardino Romera-Paredes and Massimiliano Pontil · 2013
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Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes
Ke Zhou, Hongyuan Zha, and Le Song · 2013
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Fast multivariate spatio-temporal analysis via low rank tensor learning
Mohammad Taha Bahadori, Qi Rose Yu, and Yan Liu · 2014
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Robust low-rank tensor recovery: Models and algorithms
Donald Goldfarb and Zhiwei Qin · 2014
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Tensor-based formulation and nuclear norm regularization for multienergy computed tomography
Oguz Semerci, Ning Hao, Misha E Kilmer, and Eric L Miller · 2014
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Novel methods for multilinear data completion and de-noising based on tensor-svd
Zemin Zhang, Gregory Ely, Shuchin Aeron, Ning Hao, and Misha Kilmer · 2014
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Hawkes process model with a time-dependent background rate and its application to high-frequency financial data
Takahiro Omi, Yoshito Hirata, and Kazuyuki Aihara · 2017
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A nonparametric estimation procedure for the hawkes process: comparison with maximum likelihood estimation
Matthias Kirchner and A Bercher · 2018
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Nonconvex low-rank tensor completion from noisy data
Changxiao Cai, Gen Li, H Vincent Poor, and Yuxin Chen · 2019
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A semiparametric spatiotemporal hawkes-type point process model with periodic background for crime data
Jiancang Zhuang and Jorge Mateu · 2019
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Sparse and low-rank multivariate hawkes processes
Emmanuel Bacry, Martin Bompaire, Stéphane Gax’́iffas, and Jean-Francois Muzy · 2020
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Convex recovery of marked spatio-temporal point processes
Anatoli Juditsky, Arkadi Nemirovski, Liyan Xie, and Yao Xie · 2020
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