# Research

My research is focused on variable selection methods in functional regression models.

“What is functional data?”

A bunch of curves.

“Huh?”

The major difference is that your usual flavor of statistics is about dealing
with data **points**; with functional data, you now have data **curves**
instead.

You’ve seen a scatter plot before, yes? In a scatter plot, each dot is basically an instantaneous snapshot of each individual data point. Let’s say you’re looking weight vs. age. In this instance, every subject only has one data point for those things; e.g., at this very moment, I only have one weight and one age, and at this very moment, you only have one weight and one age.

Still with me? Good.

Imagine tracking something that continuously changes with time, space,
frequency, or some other continuum. Each dot is still an instantaneous snapshot,
but each subject has their own bunch of dots instead of just one. In our
example above, we could look at weight vs. age over time—rather than just
our weight and age right now. Now, since we’re tracking things over time, the
individual points on their own aren’t necessarily interesting, but they can
ideally be considered as having arisen from an underlying smooth curve or
*function of time*, and *that* is what we are interested in. That’s functional
data.

As an simple example, the equivalent `iris`

or `mtcars`

data set for
illustrating functional data is the average daily temperature at 35 weather
stations across Canada,^{1} shown in the plot below (keep me far away from any
place named “Uranium City”):

## Coursework

Below is a list (roughly ordered by recency) of pretty much every class I took
in graduate school.^{2} No guarantees on me remembering anything prior to… last
week, probably.

Just kidding.

Or maybe not.

You’ve been warned.

- Measurement Error & Statistical Inference
- Nonparametric Regression & Smoothing
- Big Data: A Statistical Perspective
- Applied Nonparametric Statistics
- Advanced Statistical Inference
- Causal Inference
- Breakthroughs in Statistics
- Bayesian Inference & Analysis
- Computation for Statistical Research
- Advanced Probability
- Applied Multivariate Statistical Analysis
- Applied Spatial Statistics
- Categorical Data Analysis
- Statistical Consulting
- Applied Least Squares
- Design of Experiments
- Theory of Sampling Applied to Survey Design
- Applied Longitudinal Data Analysis
- Experimental Statistics for Biological Sciences
- Linear Models & Variance Components
- Statistical Theory I-II