I am interested in smoothing methods in regression (spline smoothing, kernel smoothing) and in the
closely related area of Functional Data Analysis (FDA). In FDA, each sampling unit provides information
about an entire function. Common FDA techniques include mixed effects models as in longitudinal data
analysis, and stochastic processes. I am also interested in hidden Markov models, especially in the
context of FDA.
I apply these methods to several areas: evolutionary biology, energy consumption, and animal movement.
I like to list papers that are related to my research interests, but take me a bit further afield, so
that we can both learn some things.
may be added from time to time, if I come across some. You are also welcome to suggest
a paper -
preferrably one that ties in with my research interests.
For some of the papers, I've tried to list some goals. These goals are just suggestions, and we can
discuss what you want to do. For instance, in some cases, I've written too many goals. For other papers
- we can discuss goals.
What I expect: Please read about my process and expectations and let me know if you have any questions or concerns.
R. Dennis Cook, Bing Li and Francesca Chiaromonte (2007)
Dimension Reduction in Regression without Matrix Inversion.
Biometrika, Vol. 94, No. 3 569-584
Goal: This paper seems to lay the groundwork for partial least squares-type dimension
think understanding the whole paper is a tall order. Pick a part to understand.
Kendall, David G. (1989)
A Survey of the Statistical Theory of Shape. Statist. Sci. Volume 4, Number 2, 87-99.
Goal: I've been wanting to understand the geometry used to describe shape, as discussed in
Can you explain it to me? I find this a difficult paper, but if you are into geometry maybe you will
have better luck. You can certainly restrict your work to one aspect of this paper, perhaps
considering a simple case.
NOT AVAILABLE. Duchesne, Fortin and Rivest (2015)
Equivalence between step selection functions and biased correlated random walks for statistical
inference on animal movement.
PLOS ONE, 10(4):1-12.
NOT AVAILABLE. Langrock, Adam, Leos-Barajas, Mews, Miller and Papastamatiou (2018).
Spline-based nonparametric inference in general state-switching models. Statistica