Variable screening for high dimensional time series

Variable Screening in High-dimensional Feature Space

Variable selection in high-dimensional space characterizes many contemporary problems in scientific discovery and decision making. Fan and Lv [8] introduced the concept of sure screening to reduce the dimensionality. This article first reviews the part of their ideas and results and then extends them to the likelihood based models. The techniques are then applied to disease classifications in c...

Modeling High Dimensional Time Series

This paper investigates the effectiveness of the recently proposed Gaussian Process Dynamical Model (GPDM) on high dimensional chaotic time series. The GPDM takes a Bayesian approach to modeling high-dimensional time series data, using the Gaussian process Latent Variable model (GPLVM) for nonlinear dimensionality reduction combined with a nonlinear dynamical model in latent space. The GPDM is ...

Faithful Variable Screening for High-dimensional Convex Regression By

We study the problem of variable selection in convex nonparametric regression. Under the assumption that the true regression function is convex and sparse, we develop a screening procedure to select a subset of variables that contains the relevant variables. Our approach is a two-stage quadratic programming method that estimates a sum of one-dimensional convex functions, followed by one-dimensi...

Compact modeling of high-dimensional time-series

Symbolic Analysis of High-Dimensional Time Series

In order to extract and to visualize qualitative information from a highdimensional time series, we apply ideas from symbolic dynamics. Counting certain ordinal patterns in the given series, we obtain a series of matrices whose entries are symbol frequencies. This matrix series is explored by simple methods from nominal statistics and information theory. The method is applied to detect and to v...