ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1311.4175
  4. Cited By
Regularized estimation in sparse high-dimensional time series models

Regularized estimation in sparse high-dimensional time series models

17 November 2013
Sumanta Basu
George Michailidis
    AI4TS
ArXivPDFHTML

Papers citing "Regularized estimation in sparse high-dimensional time series models"

27 / 27 papers shown
Title
Transfer Learning for High-dimensional Reduced Rank Time Series Models
Transfer Learning for High-dimensional Reduced Rank Time Series Models
Mingliang Ma Abolfazl Safikhani
AI4TS
40
0
0
22 Apr 2025
Lag selection and estimation of stable parameters for multiple
  autoregressive processes through convex programming
Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming
Somnath Chakraborty
Johannes Lederer
R. Sachs
23
0
0
03 Mar 2023
fnets: An R Package for Network Estimation and Forecasting via
  Factor-Adjusted VAR Modelling
fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling
Dominic Owens
Haeran Cho
M. Barigozzi
32
1
0
27 Jan 2023
Criteria for Classifying Forecasting Methods
Criteria for Classifying Forecasting Methods
Tim Januschowski
Jan Gasthaus
Bernie Wang
David Salinas
Valentin Flunkert
Michael Bohlke-Schneider
Laurent Callot
AI4TS
16
173
0
07 Dec 2022
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm
Xiudi Li
Abolfazl Safikhani
Ali Shojaie
29
2
0
14 Oct 2022
Benign Overfitting in Time Series Linear Models with Over-Parameterization
Benign Overfitting in Time Series Linear Models with Over-Parameterization
Shogo H. Nakakita
Masaaki Imaizumi
AI4TS
16
5
0
18 Apr 2022
A generalized likelihood based Bayesian approach for scalable joint
  regression and covariance selection in high dimensions
A generalized likelihood based Bayesian approach for scalable joint regression and covariance selection in high dimensions
Srijata Samanta
Kshitij Khare
George Michailidis
18
7
0
14 Jan 2022
High-Dimensional Knockoffs Inference for Time Series Data
High-Dimensional Knockoffs Inference for Time Series Data
Chien-Ming Chi
Yingying Fan
C. Ing
Jinchi Lv
AI4TS
24
6
0
18 Dec 2021
Efficient and passive learning of networked dynamical systems driven by
  non-white exogenous inputs
Efficient and passive learning of networked dynamical systems driven by non-white exogenous inputs
Harish Doddi
Deepjyoti Deka
Saurav Talukdar
M. Salapaka
23
6
0
02 Oct 2021
A Bernstein-type Inequality for High Dimensional Linear Processes with
  Applications to Robust Estimation of Time Series Regressions
A Bernstein-type Inequality for High Dimensional Linear Processes with Applications to Robust Estimation of Time Series Regressions
Linbo Liu
Danna Zhang
AI4TS
34
1
0
21 Sep 2021
Graphical models for nonstationary time series
Graphical models for nonstationary time series
Sumanta Basu
S. Subba Rao
68
6
0
17 Sep 2021
Sparse principal component analysis for high-dimensional stationary time
  series
Sparse principal component analysis for high-dimensional stationary time series
Kou Fujimori
Yuichi Goto
Y. Liu
M. Taniguchi
23
2
0
01 Sep 2021
Wavelet eigenvalue regression in high dimensions
Wavelet eigenvalue regression in high dimensions
P. Abry
B. C. Boniece
G. Didier
H. Wendt
19
4
0
09 Aug 2021
Multiple Change Point Detection in Structured VAR Models: the VARDetect
  R Package
Multiple Change Point Detection in Structured VAR Models: the VARDetect R Package
Peiliang Bai
Yue Bai
Abolfazl Safikhani
George Michailidis
28
1
0
23 May 2021
Granger Causality: A Review and Recent Advances
Granger Causality: A Review and Recent Advances
Ali Shojaie
E. Fox
CML
AI4TS
30
257
0
05 May 2021
Lasso Inference for High-Dimensional Time Series
Lasso Inference for High-Dimensional Time Series
R. Adámek
Stephan Smeekes
Ines Wilms
AI4TS
20
33
0
21 Jul 2020
Statistical Inference for Networks of High-Dimensional Point Processes
Statistical Inference for Networks of High-Dimensional Point Processes
Xu Wang
Mladen Kolar
Ali Shojaie
20
12
0
15 Jul 2020
Online Debiasing for Adaptively Collected High-dimensional Data with
  Applications to Time Series Analysis
Online Debiasing for Adaptively Collected High-dimensional Data with Applications to Time Series Analysis
Y. Deshpande
Adel Javanmard
M. Mehrabi
AI4TS
28
31
0
04 Nov 2019
High-Dimensional Bernoulli Autoregressive Process with Long-Range
  Dependence
High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence
Parthe Pandit
Mojtaba Sahraee-Ardakan
Arash A. Amini
S. Rangan
A. Fletcher
21
0
0
19 Mar 2019
Foundations of Sequence-to-Sequence Modeling for Time Series
Foundations of Sequence-to-Sequence Modeling for Time Series
Vitaly Kuznetsov
Zelda E. Mariet
AI4TS
BDL
17
56
0
09 May 2018
Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian
  Vector Autoregressive Processes
Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian Vector Autoregressive Processes
Amin Jalali
Rebecca Willett
51
4
0
26 Feb 2018
SILVar: Single Index Latent Variable Models
SILVar: Single Index Latent Variable Models
Jonathan Mei
José M. F. Moura
15
24
0
09 May 2017
Granger Causality in Multi-variate Time Series using a Time Ordered
  Restricted Vector Autoregressive Model
Granger Causality in Multi-variate Time Series using a Time Ordered Restricted Vector Autoregressive Model
Elsa Siggiridou
D. Kugiumtzis
CML
11
97
0
11 Nov 2015
Signal Processing on Graphs: Causal Modeling of Unstructured Data
Signal Processing on Graphs: Causal Modeling of Unstructured Data
Jonathan Mei
José M. F. Moura
CML
AI4TS
25
191
0
28 Feb 2015
Estimation of Large Covariance and Precision Matrices from Temporally
  Dependent Observations
Estimation of Large Covariance and Precision Matrices from Temporally Dependent Observations
Hai Shu
B. Nan
29
20
0
16 Dec 2014
Discovering Graphical Granger Causality Using the Truncating Lasso
  Penalty
Discovering Graphical Granger Causality Using the Truncating Lasso Penalty
Ali Shojaie
George Michailidis
CML
68
214
0
03 Jul 2010
Thresholded Lasso for high dimensional variable selection and
  statistical estimation
Thresholded Lasso for high dimensional variable selection and statistical estimation
Shuheng Zhou
110
50
0
08 Feb 2010
1