
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 email) to
schedule a meeting so that we can discuss exactly
what you would like to do with the paper you chose.
Papers
 Athey, S., Tibshirani, J. and Wager, S. (2019). Generalized
random forests. The Annals of Statistics, 47(2), 1148  1178.
DOI: 10.1214/18AOS1709
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 kernelbased 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, 79247.
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), 96118. DOI: 10.1214/15STS534
Wood, S. (2010). Statistical inference for noisy nonlinear ecological dynamic systems.
Nature, 466(26), 11021104, 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 statespace models. Statistical Inference,
30:3, 328351. DOI: 10.1214/14STS511
This review paper discusses
methods for
parameter estimation (either MLE or Bayesian)
for nonlinear statespace 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).

