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We present a new framework for learning Granger causality networks for multivariate categorical time series, based on the mixture transition distribution (MTD) model.
Testing for causality: a personal viewpoint
Clive WJ Granger · 1980
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Dual and primal-dual methods for solving strictly convex quadratic programs
Donald Goldfarb and Ashok Idnani · 1982
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A model for high-order Markov chains
Adrian E Raftery · 1985
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A method for finding projections onto the intersection of convex sets in hilbert spaces
James P Boyle and Richard L Dykstra · 1986
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Estimation in the mixture transition distribution model
Andre Berchtold · 2001
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A multivariate Markov chain model for categorical data sequences and its applications in demand predictions
Wai-Ki Ching, Eric S Fung, and Michael K Ng · 2002
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The mixture transition distribution model for high-order Markov chains and non-Gaussian time series
André Berchtold and Adrian E Raftery · 2002
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Convex optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Regression models for categorical time series
Benjamin Kedem and Konstantinos Fokianos · 2005
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An EM algorithm for estimation in the mixture transition distribution model
Sophie Lèbre and Pierre-Yves Bourguignon · 2008
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Efficient projections onto the l 1-ball for learning in high dimensions
John Duchi, Shai Shalev-Shwartz, Yoram Singer, and Tushar Chandra · 2008
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Carpediem: Optimizing the viterbi algorithm and applications to supervised sequential learning
Roberto Esposito and Daniele P Radicioni · 2009
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A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers
Sahand Negahban, Bin Yu, Martin J Wainwright, and Pradeep K Ravikumar · 2009
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Discovering graphical Granger causality using the truncating lasso penalty
Ali Shojaie and George Michailidis · 2010
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A new estimation method for multivariate Markov chain model with application in demand predictions
Dong-Mei Zhu and Wai-Ki Ching · 2010
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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 structure learning in generalized stochastic processes with latent factors
Mohammad Taha Bahadori, Yan Liu, and Eric P Xing · 2013
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quadprog R package. available online, 2013
BA Turlach and A Weingessel · 2013
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UCI machine learning repository, 2013
M. Lichman · 2013
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A new model for multivariate Markov chains
João Nicolau · 2014
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Proximal algorithms
Neal Parikh and Stephen P Boyd · 2014
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Daniele P Radicioni and Roberto Esposito · 2010
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Robust estimation of transition matrices in high dimensional heavy-tailed vector autoregressive processes
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Inference of high-dimensional autoregressive generalized linear models
E. C. Hall, G. Raskutti, and R. Willett · 2016
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Eric C Hall, Garvesh Raskutti, and Rebecca Willett · 2016
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