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7 papers · 1 filter
An Orthogonally Equivariant Estimator of the Covariance Matrix in High Dimensions and for Small Sample Sizes
Samprit Banerjee, Stefano Monni
We introduce an estimation method of covariance matrices in a high-dimensional setting, i.e., when the dimension of the matrix, , is larger than the sample size . Specifically, we…
Statistical Inference for Data-adaptive Doubly Robust Estimators with Survival Outcomes
Iván Díaz
The consistency of doubly robust estimators relies on consistent estimation of at least one of two nuisance regression parameters. In moderate to large dimensions, the use of flexi…
On the number of principal components in high dimensions
Sungkyu Jung, Myung Hee Lee, Jeongyoun Ahn
We consider the problem of how many components to retain in the application of principal component analysis when the dimension is much higher than the number of observations. To es…
A universal negative group delay filter for the prediction of band-limited signals
Henning U. Voss
A filter for universal real-time prediction of band-limited signals is presented. The filter consists of multiple time-delayed feedback terms in order to accomplish anticipatory co…
Doubly Robust Inference for Targeted Minimum Loss Based Estimation in Randomized Trials with Missing Outcome Data
Iván Díaz, Mark J. van der Laan
Missing outcome data is one of the principal threats to the validity of treatment effect estimates from randomized trials. The outcome distributions of participants with missing an…
SPARTan: Scalable PARAFAC2 for Large & Sparse Data
Ioakeim Perros, Evangelos E. Papalexakis, Fei Wang +4
In exploratory tensor mining, a common problem is how to analyze a set of variables across a set of subjects whose observations do not align naturally. For example, when modeling m…