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Simplicial Complex Networks
Universal approximation property of neural networks is one of the motivations to use these models in various real-world problems.
Two-stage Sampling, Prediction and Adaptive Regression via Correlation Screening (SPARCS)
In the first stage we collect a few ($n$) expensive samples $\{y_i,\mathbf x_i\}_{i=1}^n$, at the full dimension $p\gg n$ of $\mathbf X$, winnowing the number of variables down to a smaller dimension $l < p$ using a type of cross-correlation or regression coefficient screening.
Spectral Correlation Hub Screening of Multivariate Time Series
This property permits independent correlation analysis at each frequency, alleviating the computational and statistical challenges of high-dimensional time series.
Predictive Correlation Screening: Application to Two-stage Predictor Design in High Dimension
We introduce a new approach to variable selection, called Predictive Correlation Screening, for predictor design.
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