We develop a robust Bayesian functional principal component analysis (FP...
Statistical models are an essential tool to model, forecast and understa...
Scientists and statisticians often want to learn about the complex
relat...
Ordinary differential equations (ODEs) are widely used to characterize t...
The regression of a functional response on a set of scalar predictors ca...
Random forests are a sensible non-parametric model to predict competing ...
Using representations of functional data can be more convenient and
bene...
Ordinary differential equations (ODE) have been widely used for modeling...
The selection of smoothing parameter is central to estimation of penaliz...
P-spline represents an unknown univariate function with uniform B-spline...
Massive data bring the big challenges of memory and computation for anal...
In recent years, there has been considerable innovation in the world of
...
Neural networks have excelled at regression and classification problems ...
We present a methodology for integrating functional data into deep dense...
The conventional historical functional linear model relates the current ...
In recent work Greenlaw et al. (Bioinformatics, 2017) have developed a
B...
In this article, we consider the problem of recovering the underlying
tr...
We study a scalar-on-function historical linear regression model which
a...
Brain decoding involves the determination of a subject's cognitive state...