Research interests: 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.
Additional papers: 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.
Goals: 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.
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 reduction. I think understanding the whole paper is a tall order. Pick a part to understand.
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 this article. 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.
Equivalence between step selection functions and biased correlated random walks for statistical inference on animal movement. PLOS ONE, 10(4):1-12.
Spline-based nonparametric inference in general state-switching models. Statistica Neerlandica.