On the total variation regularized estimator over a class of tree graphs

[author] Miller, Kenneth S.K. S. (1981). On the inverse of the sum of matrices. Mathematics Magazine 54 67–72

1981

Earlier work this paper cites.

[author] Mammen, EnnoE. and van de Geer, SaraS. (1997). Locally adaptive regression splines. The Annals of Statistics 25 387–413

1997

Earlier work this paper cites.

[author] Donoho, David LD. L. and Johnstone, Iain MI. M. (1998). Minimax estimation via wavelet shrinkage. The Annals of Statistics 26 879–921. 10.1214/aos/1024691081

1998

Earlier work this paper cites.

[author] Laurent, BB. and Massart, PP. (2000). Adaptive estimation of a quadratic functional by model selection. The Annals of Statistics 28 1302–1338

2000

Earlier work this paper cites.

[author] Tibshirani, RobertR., Saunders, MichaelM., Rosset, SaharonS., Zhu, JiJ. and Knight, KeithK. (2005). Sparsity and smoothness via the fused lasso. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67 91–108

2005

Earlier work this paper cites.

[author] Zhao, PengP. and Yu, BinB. (2006). On model selection consistency of Lasso. The Journal of Machine Learning Research 7 2541–2563

2006

Earlier work this paper cites.

[author] Friedman, JeromeJ., Hastie, TrevorT., Höfling, HolgerH. and Tibshirani, RobertR. (2007). Pathwise coordinate optimization. The Annals of Applied Statistics 1 302–332

2007

Earlier work this paper cites.

[author] Jacobs, David PD. P., Machado, Catia M SC. M. S., Pereira, Elaine CE. C. and Trevisan, VilmarV. (2008). Computing the inverse of a tree’s incidence matrix. Congr. Numerantium 189 169–176

2008

Earlier work this paper cites.

[author] Tibshirani, Ryan JR. J. and Taylor, JonathanJ. (2011). The solution path of the generalized LASSO. The Annals of Statistics 39 1335–1371

2011

Cited alongside, same era.

[author] Bapat, Ravindra B.R. B. (2014). Graphs and Matrices. Springer, London

2014

Cited alongside, same era.

[author] Rojas, Cristian RC. R. and Wahlberg, BoB. (2014). On change point detection using the fused lasso method. ArXiv ID 1401.5408v1

2014

Cited alongside, same era.

[author] Jia, JinzhuJ. and Rohe, KarlK. (2015). Preconditioning the Lasso for sign consistency. Electronic Journal of Statistics 9 1150–1172

2015

Cited alongside, same era.

[author] Rojas, Cristian RC. R. and Wahlberg, BoB. (2015). How to monitor and mitigate staircasing in l1 trend filtering. ICASSP 3946–3950

2015

Cited alongside, same era.

[author] Sadhanala, VeeranjaneyuluV., Wang, Yu-XiangY.-X. and Tibshirani, Ryan J.R. J. (2016). Total variation classes beyond 1d: minimax rates, and the limitations of linear smoothers. NIPS

2016

Later among the works it cites.

[author] van de Geer, SaraS. (2016). Estimation and testing under sparsity 2159. Springer

2016

Later among the works it cites.

[author] Dalalyan, Arnak SA. S., Hebiri, MohamedM. and Lederer, JohannesJ. (2017). On the prediction performance of the Lasso. Bernoulli 23 552–581

2017

Later among the works it cites.

[author] Guntuboyina, AdityanandA., Lieu, DonovanD., Chatterjee, SabyasachiS. and Sen, BodhisattvaB. (2017). Spatial adaptation in trend filtering. ArXiv ID 1702.05113

2017

Later among the works it cites.

[author] Lin, KevinK., Sharpnack, JamesJ., Rinaldo, AlessandroA. and Tibshirani, Ryan JR. J. (2017). A sharp error analysis for the fused lasso, with application to approximate changepoint screening. Neural Information Processing Systems (NIPS) 3 42