Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models | Paper Citation Record

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This paper cites Temporal causal modeling with graphical granger methods.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Temporal causal modeling with graphical granger methods

Reference 1

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This paper cites Comparison of diﬀerent cortical connectivity estimators for high-resolution eeg recordings.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Comparison of diﬀerent cortical connectivity estimators for high-resolution eeg recordings

Reference 2

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This paper cites Bolasso: model consistent lasso estimation through the boostrap.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Bolasso: model consistent lasso estimation through the boostrap

Reference 3

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This paper cites Learning graphical models for stationary time series.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Learning graphical models for stationary time series

Reference 4

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This paper cites Optimizing the Union of Intersections LASSO ($UoI_{LASSO}$) and Vector Autoregressive ($UoI_{VAR}$) Algorithms for Improved Statistical Estimation at Scale.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Optimizing the Union of Intersections LASSO ($UoI_{LASSO}$) and Vector Autoregressive ($UoI_{VAR}$) Algorithms for Improved Statistical Estimation at Scale

Reference 5

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This paper cites Dynamic reconﬁguration of human brain networks during learning.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Dynamic reconﬁguration of human brain networks during learning

Reference 6

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This paper cites Regularized estimation in sparse high-dimensional time series models.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Regularized estimation in sparse high-dimensional time series models

Reference 7

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This paper cites Union of intersections (uoi) for interpretable data driven discovery and prediction.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Union of intersections (uoi) for interpretable data driven discovery and prediction

Reference 8

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This paper cites Distributed optimization and statistical learning via the alternating direction method of multipliers.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Distributed optimization and statistical learning via the alternating direction method of multipliers

Reference 9

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This paper cites Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection

Reference 10

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This paper cites Bagging predictors.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Bagging predictors

Reference 11

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This paper cites Multiple neural spike train data analysis: state-of-the-art and future challenges.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Multiple neural spike train data analysis: state-of-the-art and future challenges

Reference 12

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This paper cites Block length selection in the bootstrap for time series.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Block length selection in the bootstrap for time series

Reference 13

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This paper cites Springer, 1 edition, 2011.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Springer, 1 edition, 2011

Reference 14

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This paper cites The origin of extracellular ﬁelds and currents – eeg, ecog, lfp and spikes.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models The origin of extracellular ﬁelds and currents – eeg, ecog, lfp and spikes

Reference 15

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This paper cites Thirteen challenges in modelling plant diseases.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Thirteen challenges in modelling plant diseases

Reference 16

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This paper cites Causality and graphical models in time series analysis.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Causality and graphical models in time series analysis

Reference 17

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This paper cites The joint graphical lasso for inverse covariance estimation across multiple classes.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models The joint graphical lasso for inverse covariance estimation across multiple classes

Reference 18

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This paper cites Non-gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex.Journal of Neuroscience, 26(47):12206– 12218, 2006.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Non-gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex.Journal of Neuroscience, 26(47):12206– 12218, 2006

Reference 19

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This paper cites An application of vector time series techniques to macroeconomic forecasting.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models An application of vector time series techniques to macroeconomic forecasting

Reference 20

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This paper cites Sparse high-dimensional models in economics.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Sparse high-dimensional models in economics

Reference 21

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This paper cites The generalized dynamic factor model: one-sided estimation and forecasting.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models The generalized dynamic factor model: one-sided estimation and forecasting

Reference 22

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This paper cites Pathwise coordinate optimization.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Pathwise coordinate optimization

Reference 23

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This paper cites Regularization paths for generalized linear models via coordinate descent.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Regularization paths for generalized linear models via coordinate descent

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.968818Z

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Observation 1d17be51-1cfa-412c-b64b-fcc1cc0e98a7 · outbound

This paper cites Investigating causal relations by econometric models and cross-spectral methods.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Investigating causal relations by econometric models and cross-spectral methods

Reference 25

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This paper cites Joint estimation of multiple graphical models.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Joint estimation of multiple graphical models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.867741Z

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This paper cites Inferring high-dimensional poisson autoregressive models.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Inferring high-dimensional poisson autoregressive models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.812776Z

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Observation d6b3e4ef-8a92-48a5-99f6-e7f19ba3b438 · outbound

This paper cites A direct estimation of high dimensional stationary vector autoregressions.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models A direct estimation of high dimensional stationary vector autoregressions

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.782921Z

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This paper cites Machine learning for the geosciences: Challenges and opportunities.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Machine learning for the geosciences: Challenges and opportunities

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.749195Z

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This paper cites Identifying natural images from human brain activity.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Identifying natural images from human brain activity

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.689045Z

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This paper cites Bootstrap methods for time series, volume 30 of Time Series Analysis: Methods and Applications , chapter 1, pages 3–26.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Bootstrap methods for time series, volume 30 of Time Series Analysis: Methods and Applications , chapter 1, pages 3–26

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.613045Z

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Observation eddd5dba-2157-4295-b5f5-42f4de147b65 · outbound

This paper cites The jackknife and the bootstrap for general stationary observations.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models The jackknife and the bootstrap for general stationary observations

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.573794Z

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Observation e87c92fa-d188-49da-8bd7-90f564b2bcfc · outbound

This paper cites Moving blocks jackknife and bootstrap capture weak dependence.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Moving blocks jackknife and bootstrap capture weak dependence

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.531527Z

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This paper cites Springer, 1 edition, 2005.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Springer, 1 edition, 2005

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.474214Z

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Observation e4b9b005-0a21-486d-99f8-56e667664143 · outbound

This paper cites Sociology in the era of big data: The ascent of forensic social science.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Sociology in the era of big data: The ascent of forensic social science

Reference 35

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Observation 166f23c8-c6c6-4d75-a946-49da717f92f2 · outbound

This paper cites Stability selection.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Stability selection

Reference 36

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Observation 99be5a47-515f-4e3a-9908-fde3d2a0b1fc · outbound

This paper cites Investigating large-scale brain 15 dynamics using ﬁeld potential recordings: analysis and interpretation.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Investigating large-scale brain 15 dynamics using ﬁeld potential recordings: analysis and interpretation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.312051Z

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This paper cites Chichilnisky, and Eero Simoncelli.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Chichilnisky, and Eero Simoncelli

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.259154Z

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Observation ecb21283-764a-42a1-8d13-7269ffc092a1 · outbound

This paper cites Joint estimation of multiple graphical models from high dimensional time series.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Joint estimation of multiple graphical models from high dimensional time series

Reference 39

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This paper cites Macroeconomics and reality.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Macroeconomics and reality

Reference 40

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This paper cites Large Vector Auto Regressions.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Large Vector Auto Regressions

Reference 41

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This paper cites Forecasting using principal components from a large number of predictors.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Forecasting using principal components from a large number of predictors

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.107316Z

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Observation 2436e3ec-bf36-4fd2-a445-403b86c074d3 · outbound

This paper cites Anthropogenic global warming hypothesis: testing its robustness by granger causality analysis.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Anthropogenic global warming hypothesis: testing its robustness by granger causality analysis

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.079793Z

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Observation 417fd369-7ec0-43bc-85d4-d710a3430450 · outbound

This paper cites A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate eﬀects.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate eﬀects

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:27.041412Z

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Observation d1e27e03-9027-43c1-878f-0556b38af910 · outbound

This paper cites Analysis of ﬁnancial time series , volume 543.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Analysis of ﬁnancial time series , volume 543

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:26.991915Z

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Observation 8a6b128b-5907-4349-a878-03dafe66f071 · outbound

This paper cites Uoi-nmfcluster: A robust nonnegative matrix factorization algorithm for improved parts-based decomposition and reconstruction of noisy data.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Uoi-nmfcluster: A robust nonnegative matrix factorization algorithm for improved parts-based decomposition and reconstruction of noisy data

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:26.942610Z

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Observation 8af6fe45-39c9-498f-aea0-5e4f0bf02c81 · outbound

This paper cites Shrinkage tuning parameter selection with a diverging number of parameters.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Shrinkage tuning parameter selection with a diverging number of parameters

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:26.906261Z

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Observation cffd709a-b43c-4a3f-bbce-59211b7d79b7 · outbound

This paper cites Consistent tuning parameter selection in high dimensional sparse linear regression.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Consistent tuning parameter selection in high dimensional sparse linear regression

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:26.868992Z

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Observation bc1730c0-87e7-402b-9168-77fae76edfbe · outbound

This paper cites Nearly unbiased variable selection under minimax concave penalty.

Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models Nearly unbiased variable selection under minimax concave penalty

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:19:26.813258Z

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