Matías' suggested papers for STAT548

How it works: Please choose a paper from the following list. Look at the paper first and let me know if you would like to work on that paper so that I can mark it as unavaliable on this website. Papers that are overstriken are no longer available. Contact me (in person or by e-mail) to schedule a meeting so that we can discuss exactly what you would like to do with the paper you chose.


  • Athey, S., Tibshirani, J. and Wager, S. (2019). Generalized random forests. The Annals of Statistics, 47(2), 1148 - 1178. DOI: 10.1214/18-AOS1709

    This paper proposes a new way of computing local estimators, where random forests are used to identify "neighbour" points in the training set, instead of the usual kernel-based concept of "local". This approach appears to neatly circumvent the curse of dimensionality, and can in principle be applied to a large class of estimators.

  • Fan, J., Li, Q. and Wang, Y. (2017). Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions. Journal of the Royal Statistical Society. Series B, Statistical methodology, 79-247. DOI: 10.1111/rssb.12166

    This paper shows that, in theory, one can consistently and robustly estimate a regression function when the errors have an asymmetric distribution. However, the devil is in the methodological and practical details. If you pick this paper, you will focus on running numerical experiments checking the proposed method in realistic settings.

  • Fasiolo, M., Pya, N. and Wood, S.N. (2016). A comparison of inferential methods for highly nonlinear state space models in ecology and epidemiology. Statistical Science, 31(1), 96-118. DOI: 10.1214/15-STS534

    Wood, S. (2010). Statistical inference for noisy nonlinear ecological dynamic systems. Nature, 466(26), 1102-1104, DOI: 10.1038/nature09319

    These two papers should be read together. They argue that the usual way to perform inference for dynamic systems when they are "almost chaotic" may intrinsically be unreliable, and propose synthetic likelihood as a feasible alternative.

  • Kantas, N., Doucet, A., Singh, S.S., Maciejowski, J. and Chopin, N. (2015). On particle methods for parameter estimation in state-space models. Statistical Inference, 30:3, 328-351. DOI: 10.1214/14-STS511

    This review paper discusses methods for parameter estimation (either MLE or Bayesian) for non-linear state-space models (aka Hidden Markov models) using sequential Monte Carlo (particle methods). These algorithms are very useful when working with complex models (e.g. models using phylogenetic and transmission trees).