Laplacian spectral characterization of roses

A rose graph is a graph consisting of cycles that all meet in one vertex. We show that except for two specific examples, these rose graphs are determined by the Laplacian spectrum, thus proving a conjecture posed by Lui and Huang [F.J. Liu and Q.X. Huang, Laplacian spectral characterization of 3-rose graphs, Linear Algebra Appl. 439 (2013), 2914--2920]. We also show that if two rose graphs have a so-called universal Laplacian matrix with the same spectrum, then they must be isomorphic. In memory of Horst Sachs (1927-2016), we show the specific case of the latter result for the adjacency matrix by using Sachs' theorem and a new result on the number of matchings in the disjoint union of paths

Robust variable screening for regression using factor profiling

Sure Independence Screening is a fast procedure for variable selection in ultra-high dimensional regression analysis. Unfortunately, its performance greatly deteriorates with increasing dependence among the predictors. To solve this issue, Factor Profiled Sure Independence Screening (FPSIS) models the correlation structure of the predictor variables, assuming that it can be represented by a few latent factors. The correlations can then be profiled out by projecting the data onto the orthogonal complement of the subspace spanned by these factors. However, neither of these methods can handle the presence of outliers in the data. Therefore, we propose a robust screening method which uses a least trimmed squares method to estimate the latent factors and the factor profiled variables. Variable screening is then performed on factor profiled variables by using regression MM-estimators. Different types of outliers in this model and their roles in variable screening are studied. Both simulation studies and a real data analysis show that the proposed robust procedure has good performance on clean data and outperforms the two nonrobust methods on contaminated data

Eigenvectors of random matrices: A survey

Eigenvectors of large matrices (and graphs) play an essential role in combinatorics and theoretical computer science. The goal of this survey is to provide an up-to-date account on properties of eigenvectors when the matrix (or graph) is random.Comment: 64 pages, 1 figure; added Section 7 on localized eigenvector