In this paper, we consider the problem of estimating the covariance kernel and its eigenvalues and eigenfunctions from sparse, irregularly observed, noise corrupted and (possibly) correlated...
Consistency of restricted maximum likelihood estimators of principal components (2008)
In this paper we consider two closely related problems : estimation of eigenvalues and eigenfunctions of the covariance kernel of functional data based on (possibly) irregular measurements, and the...
In this paper, we consider the problem of estimating the eigenvalues and eigenfunctions of the covariance kernel (i.e., the functional principal components) from sparse and irregularly observed...
"Pre-conditioning" for feature selection and regression in high-dimensional problems (2007)
Paul, Debashis, Bair, Eric, Hastie, Trevor, Tibshirani, Robert
We consider regression problems where the number of predictors greatly exceeds the number of observations. We propose a method for variable selection that first estimates the regression function,...
Nonparametric estimation of principal components / (2005)
Paul, Debashis., Johnstone, Iain M. Advisor
Submitted to the Department of Statistics.