Stahel–Donoho estimation for high-dimensional data

Methods for regression analysis in high-dimensional data

By evolving science, knowledge and technology, new and precise methods for measuring, collecting and recording information have been innovated, which have resulted in the appearance and development of high-dimensional data. The high-dimensional data set, i.e., a data set in which the number of explanatory variables is much larger than the number of observations, cannot be easily analyzed by ...

Stahel-Donoho estimation for high-dimensional data

We discuss two recently proposed adaptations of the well-known StahelDonoho estimator of multivariate location and scatter for high-dimensional data. The first adaptation adjusts the calculation of the outlyingness of the observations while the second adaptation allows to give separate weights to each of the components of an observation. Both adaptations address the possibility that in higher d...

Model Averaging Estimation for Sparse High-dimensional Data

Fast covariance estimation for high-dimensional functional data

We propose two fast covariance smoothing methods and associated software that scale up linearly with the number of observations per function. Most available methods and software cannot smooth covariance matrices of dimension J > 500; a recently introduced sandwich smoother is an exception but is not adapted to smooth covariance matrices of large dimensions, such as J = 10, 000. We introduce two...

DensEst: Density Estimation for Data Mining in High Dimensional Spaces

Subspace clustering and frequent itemset mining via “stepby-step” algorithms that search the subspace/pattern lattice in a top-down or bottom-up fashion do not scale to large high dimensional data bases. Recent “jump” algorithms directly choose candidate subspace regions or patterns. Their scalability and quality depend heavily on the rating of these candidates as mislead jumps incur poor resul...