An improved rate for non-negative definite consistent covariance matrix estimation with heterogeneous dependent data

Robust Covariance Matrix Estimation with Data-Dependent VAR Prewhitening Order

Automatic positive semi-definite HAC covariance matrix and GMM estimation

This paper proposes a new class of HAC covariance matrix estimators. The standard HAC estimation method re-weights estimators of the autocovariances. Here we initially smooth the data observations themselves using kernel function based weights. The resultant HAC covariance matrix estimator is the normalised outer product of the smoothed random vectors and is therefore automatically positive sem...

Spatial Heteroskedasticity and Autocorrelation Consistent Estimation of Covariance Matrix

This paper considers spatial heteroskedasticity and autocorrelation consistent (spatial HAC) estimation of covariance matrices of parameter estimators. We generalize the spatial HAC estimators introduced by Kelejian and Prucha (2007) to apply to linear and nonlinear spatial models with moment conditions. We establish its consistency, rate of convergence and asymptotic truncated mean squared err...

An Improved Non-negative Matrix Factorization Method for Masquerade Detection

A local-knowledge method for masquerade detection that uses a Non-negative Matrix Factorization (NMF) algorithm is here proposed. This method does not consider training data from other users to build a specific user profile but his own. It is used a normalization phase that helps improve a previous NMF-based method by Wang et.al. Comparisons with other local-knowledge methods like Wang’s, Hidde...

Improved Covariance Matrix Estimators for Weighted Analysis of Microarray Data

Empirical Bayes models have been shown to be powerful tools for identifying differentially expressed genes from gene expression microarray data. An example is the WAME model, where a global covariance matrix accounts for array-to-array correlations as well as differing variances between arrays. However, the existing method for estimating the covariance matrix is very computationally intensive a...