What are the 5 most influential statistics papers of the aughts (2000-2010)?
Post date: Jul 25, 2013 4:10:52 PM
Jeff Leek, over at Simply Statistics asks an interesting question: What are the 5 most influential statistics papers of 2000-2010? And there are many papers are proposed to be the candidates of the 5 most influential statistics, I will list them here for our potential use to present.
Donoho, David (2006). Compressed sensing. IEEE Transactions on Information Theory. 52, 1289-1306.
Greenshtein, Eitan and Ritov, Ya’Acov. (2004). Persistence in high-dimensional linear predictor selection and the virtue of overparametrization. Bernoulli, 10, 971-988.
Meinshausen, Nicolai and Buhlmann, Peter. (2006). High-dimensional graphs and variable selection with the lasso. The Annals of Statistics, 34, 1436-1462.
Efron, Bradley and Hastie, Trevor and Johnstone, Iain and Tibshirani, Robert. (2004). Least angle regression. The Annals of statistics, 32, 407-499.
Hofmann, Thomas and Scholkopf, Bernhard and Smola, Alexander J. (2008). Kernel methods in machine learning. The Annals of Statistics. 1171–1220.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. the Journal of machine Learning research, 3, 993-1022.
Breiman, Leo. “Random forests.” Machine learning 45.1 (2001): 5-32.(9469 citations)
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). “Gene selection for cancer classification using support vector machines”. Machine learning, 46(1-3), 389-422. (3345 Citations)
Muller, K. R., Mika, S., Ratsch, G., Tsuda, K., & Scholkopf, B. (2001). “An introduction to kernel-based learning algorithms”. Neural Networks, IEEE Transactions on, 12(2), 181-201. (2440 Citations)
Friedman, J. H. (2002). “Stochastic gradient boosting”. Computational Statistics & Data Analysis, 38(4), 367-378. (682 citations)
Tyler, D., Critchley, F., Dumbgen, L., and Oja, H. and Tyler, D. (2009) Invariant coordinate selection (with discussion). Journal of Royal Statistical Society B, {71}, 549–592.
Claeskens, G. & Hjort, N.L. (2003). The Focussed Information Criterion, Journal of the American Statistical Association, 98, 900-916 (with discussion)/Hjort, N.L. & Claeskens, G. (2003). Frequentist model average estimators, Journal of the American Statistical Association, 98, 879-899 (with discussion).
Local false discovery rates: Efron, Bradley, et al. "Empirical Bayes analysis of a microarray experiment."Journal of the American statistical association 96.456 (2001): 1151-1160.
q-values: Storey, John D., and Robert Tibshirani. "Statistical significance for genomewide studies." Proceedings of the National Academy of Sciences 100.16 (2003): 9440-9445.
ABC (a Bayesian spin on the generalized method of moments): Beaumont, Mark A., Wenyang Zhang, and David J. Balding. "Approximate Bayesian computation in population genetics." Genetics 162.4 (2002): 2025-2035.
Andrieu, Christophe, Arnaud Doucet, and Roman Holenstein (2010). "Particle Markov chain Monte Carlo methods. (with discussion)" Journal of the Royal Statistical Society: Series B 72.3: 269-342. (in Andrieu et al. it is proved for the first time that the use of an unbiased approximation to the likelihood (for example via sequential Monte Carlo), embedded in an MCMC algorithm, result in exact Bayesian inference, strengthening considerably the (already widespread) use of SMC methods for Bayesian inference.)
Aït‐Sahalia, Yacine
(2002). "Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed‐form Approximation Approach." Econometrica 70.1: 223-262.(In Aït‐Sahalia a method for approximating the transition density of a diffusion process is given in closed-form, boosting, considerably (when applicable) the computational speed and the quality of the approximation.)
Marjoram P, Molitor J, Plagnol V, Tavare' S (2003) Markov chain Monte Carlo without likelihoods. Proceedings of the National Academy of Sciences 100(26):15,324–15,328 (Marjoram et al. offer the first MCMC algorithm for Approximate Bayesian Computation (ABC), the latter being an increasingly important methodological and computational tool.)
Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian
inference for latent Gaussian models by using integrated nested Laplace
approximations (with discussion). Journal of the royal statistical society: Series b, 71(2), 319-392.
Reference blot posts:
http://normaldeviate.wordpress.com/2013/07/23/the-five-jeff-leeks-challenge/
http://andrewgelman.com/2013/07/22/top-5-stat-papers-since-2000/