341 citations · 421 across the 7 of their papers we have counts for
7 papers
A Scalable Gradient-Based Optimization Framework for Sparse Minimum-Variance Portfolio Selection
Sarat Moka, Matias Quiroz, Vali Asimit +1
Portfolio optimization involves selecting asset weights to minimize a risk-reward objective, such as the portfolio variance in the classical minimum-variance framework. Sparse port…
Screening methods for linear errors-in-variables models in high dimensions
Linh Nghiem, Francis K. C. Hui, Samuel Mueller +1
Microarray studies, in order to identify genes associated with an outcome of interest, usually produce noisy measurements for a large number of gene expression features from a smal…
svReg: Structural Varying-coefficient regression to differentiate how regional brain atrophy affects motor impairment for Huntington disease severity groups
Rakheon Kim, Samuel Mueller, Tanya P. Garcia
For Huntington disease, identification of brain regions related to motor impairment can be useful for developing interventions to alleviate the motor symptom, the major symptom of…
Bootstrapping F test for testing Random Effects in Linear Mixed Models
P. Y. O'Shaughnessy, Francis Hui, Samuel Muller +1
Recently Hui et al. (2018) use F tests for testing a subset of random effect, demonstrating its computational simplicity and exactness when the first two moment of the random effec…
Robust estimation of precision matrices under cellwise contamination
Garth Tarr, Samuel Müller, Neville C. Weber
There is a great need for robust techniques in data mining and machine learning contexts where many standard techniques such as principal component analysis and linear discriminant…
Model Selection in Linear Mixed Models
Samuel Müller, J. L. Scealy, A. H. Welsh
Linear mixed effects models are highly flexible in handling a broad range of data types and are therefore widely used in applications. A key part in the analysis of data is model s…