The empirical functions we obtain from functional data analysis are interpreted below as ecological responses of the phytoplankton community, incorporating growth and loss terms (e.g., grazing).
In functional data analysis (FDA), the covariance function is fundamental not only as a critical quantity for understanding elementary aspects of functional data but also as an indispensable ingredient for many advanced FDA methods.
The distribution of trees within each individual plot was investigated by second-order spatial analysis with Ripley's K d) function, and then evaluated across each study area with functional data analysis.
One approach to functional data analysis uses generalized additive models to estimate functions of predictor variables, adding together each effect to predict the response variable.
Her methodological interests include statistical learning, predictive modeling, genetic epidemiology, functional data analysis, and semiparametric analysis.
Other ongoing interests include functional data analysis, nonparametric regression, wavelet methods, statistical modeling, statistical computing, and statistical education.
Three-harmonic regression analysis and functional data analysis were used to assess the 24-h and sleep-associated patterns of plasma cortisol, respectively.
The list below is a (non-exhaustive) collection of papers concerned with developing or applying statistical methods for functional data analysis authored by group members.
Biostatistics faculty, students, and collaborators meet throughout the academic year to discuss statistical issues in areas of functional data analysis, clinical trials, and dose finding.
Functional data analysis also allows for a direct examination of day-of-week effects on ozone formation/destruction rates.
A Bayesian method for estimation of a hazard term structure is presented in a functional data analysis framework.
