The Living Thing / Notebooks :

(Weighted) least squares fits

As used in, e.g. lasso regression.

Refs

BeLT17
Bellec, P. C., Lecué, G., & Tsybakov, A. B.(2017) Towards the study of least squares estimators with convex penalty. arXiv:1701.09120 [Math, Stat].
ChYi08
Chartrand, R., & Yin, W. (2008) Iteratively reweighted algorithms for compressive sensing. In IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008 (pp. 3869–3872). DOI.
ChSh16
Chatla, S. B., & Shmueli, G. (2016) Modeling Big Count Data: An IRLS Framework for CMP Regression and GAM. arXiv:1610.08244 [Stat].
CGWY12
Chen, X., Ge, D., Wang, Z., & Ye, Y. (2012) Complexity of unconstrained L_2-L_p. Mathematical Programming, 143(1–2), 371–383. DOI.
Frie02
Friedman, J. H.(2002) Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378. DOI.
FHHT07
Friedman, J., Hastie, T., Höfling, H., & Tibshirani, R. (2007) Pathwise coordinate optimization. The Annals of Applied Statistics, 1(2), 302–332. DOI.
FrHT10
Friedman, J., Hastie, T., & Tibshirani, R. (2010) Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1–22. DOI.
GaRC09
Gasso, G., Rakotomamonjy, A., & Canu, S. (2009) Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming. IEEE Transactions on Signal Processing, 57(12), 4686–4698. DOI.
KaLa10
Karampatziakis, N., & Langford, J. (2010) Online Importance Weight Aware Updates. arXiv:1011.1576 [Cs].
MaNT04
Madsen, K., Nielsen, H. B., & Tingleff, O. (2004) Methods for non-linear least squares problems.
PoKo97
Portnoy, S., & Koenker, R. (1997) The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators. Statistical Science, 12(4), 279–300. DOI.
RhGl15
Rhee, C.-H., & Glynn, P. W.(2015) Unbiased Estimation with Square Root Convergence for SDE Models. Operations Research, 63(5), 1026–1043. DOI.
RoZh07
Rosset, S., & Zhu, J. (2007) Piecewise linear regularized solution paths. The Annals of Statistics, 35(3), 1012–1030. DOI.
YuTo09
Yun, S., & Toh, K.-C. (2009) A coordinate gradient descent method for ℓ 1-regularized convex minimization. Computational Optimization and Applications, 48(2), 273–307. DOI.