Difference between revisions of "Sean G. Carver's Research Interests"
From Sean_Carver
| Line 9: | Line 9: | ||
::This statistic is an interpretable way of quantifying the similarity of models. | ::This statistic is an interpretable way of quantifying the similarity of models. | ||
::Student collaborator (just graduated) Rebeca Berger. | ::Student collaborator (just graduated) Rebeca Berger. | ||
| + | |||
| + | * '''Motor Control:''' specifically continuation tapping. In an effort to perform system identification of the internal clock for motor control, we found that the inter-tap intervals are fit equally well by Normal and Inverse Gaussian distributions, both were better than the Laplace distribution. Contrary to this finding, the only report in the literature we could find, a review paper, reported that inter-tap intervals have Laplace distribution. | ||
| + | ::Student Collaborators: Daniel Scanlan, Wasim Ashshowaf, Alexander Spinos | ||
Revision as of 01:16, 14 May 2017
Much of my current research involves projects related to the statistical analysis of models using simulated data.
- These projects involve, among other things, computing the number of samples needed to reject an alternative model with the likelihood ratio test, in favor of a true model that produces the data.
- Baseball: how many innings must be played by model of the Baltimore Orioles (fitted from actual Orioles home games) to reject the model that the New York Yankees are playing.
- This statistic is an interpretable way of quantifying the similarity of models.
- Student collaborator (just graduated) Rebeca Berger.
- Motor Control: specifically continuation tapping. In an effort to perform system identification of the internal clock for motor control, we found that the inter-tap intervals are fit equally well by Normal and Inverse Gaussian distributions, both were better than the Laplace distribution. Contrary to this finding, the only report in the literature we could find, a review paper, reported that inter-tap intervals have Laplace distribution.
- Student Collaborators: Daniel Scanlan, Wasim Ashshowaf, Alexander Spinos
