Comments on: Guest Post: ROB TIBSHIRANI

By: epanechnikov

epanechnikov — Mon, 25 Feb 2013 00:00:58 +0000

Reblogged this on Epanechnikov's Blog and commented: 9 Great Statistics papers published after 1970

By: Radu

Radu — Thu, 17 Jan 2013 23:37:22 +0000

I would add the bootstrap paper by Efron ’79 and the paper by Hastings ’70 where he introduces what we now call the Metropolis-Hastings sampler.

By: Guest Post: ROB TIBSHIRANI « wendyranstat

Guest Post: ROB TIBSHIRANI « wendyranstat — Sun, 13 Jan 2013 14:47:28 +0000

[...] Guest Post: ROB TIBSHIRANI. [...]

By: xi'an

xi'an — Thu, 10 Jan 2013 00:56:22 +0000

I started a PhD course on reading classics a few years ago, so that my students could learn to analyze and present papers, while getting a better vision of the broadness of the field. I will include Rob's suggestions that were not already in the intersection!

By: datanalytics » Los nueve artículos favoritos de Tibshirani

datanalytics » Los nueve artículos favoritos de Tibshirani — Tue, 08 Jan 2013 07:20:47 +0000

[...] Tibshirani ha seleccionado recientemente una lista de nueve artículos de estadística publicados desde 1970. Son [...]

By: Linkage (technical) « An Ergodic Walk

Linkage (technical) « An Ergodic Walk — Tue, 25 Dec 2012 02:53:24 +0000

[...] at Larry Wasserman’s blog, Rob Tibshirani suggests 9 Great Statistics papers published after 1970. You know, in case you were looking for some light reading over winter [...]

By: Tapen

Tapen — Mon, 24 Dec 2012 05:58:55 +0000

No time series? I would have thought Granger and Engle would feature somewhere.

By: Jason

Jason — Sun, 23 Dec 2012 19:26:47 +0000

In the statistics front:
1) Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society. Series B (Methodological), 192-236.
This paper contains many of the major ideas in the graphical models literature.
2) I second the lasso paper by Rob. Also around the same time, the basis pursuit paper appeared in the Applied Math/Signal Processing literature: Chen, S. S., Donoho, D. L., & Saunders, M. A. (1998). Atomic decomposition by basis pursuit. SIAM journal on scientific computing, 20(1), 33-61.

By: Umberto Picchini

Umberto Picchini — Sat, 22 Dec 2012 22:05:13 +0000

I would like to add:
1) J. Friedman (1991). Multivariate adaptive regression splines (with Discussion), The Annals of Statistics 19(1), pp 1-141.
2) the book by J. Pinheiro and D. Bates (2000), Mixed-effects models in S and S-Plus, Springer. It has been a very important reference for S and R users and pushed practical application of hierarchical modelling beyond NONMEM.
3) together with my suggestion (2) above we should add some article on mixed-effects modelling, but I am not sure which one would be best. Perhaps Laird, Nan M.; Ware, James H. (1982). “Random-Effects Models for Longitudinal Data”, Biometrics 38(4), 963-974.

By: Jonathan Goodman

Jonathan Goodman — Sat, 22 Dec 2012 14:21:16 +0000

I don’t agree with listing the work of Geman Geman, Gelfand and Smith. All they did was learn existing Monte Carlo methods. And they didn’t do very well at that. What they call “Gibbs sampler” had been used by computational physicists and chemists for decades under names like “heat bath” and “partial resampling”.