The estimated covariance matrix of the parameter estimates is computed as the inverse Hessian matrix, and for unconstrained problems it should be positive definite. If the final parameter estimates ...

The estimation of the covariance matrix of stock returns is required for portfolio optimization. The standard statistical method is to gather a history of past stock returns and compute their sample ...

This repository contains MATLAB, Python, and R code for the paper E. Raninen and E. Ollila, "Coupled Regularized Sample Covariance Matrix Estimator for Multiple Classes," in IEEE Transactions on ...

This valuable study shows a surprising scale-invariance of the covariance spectrum of large-scale recordings in the zebrafish brain in vivo. A solid analysis demonstrates that a Euclidean random ...

Optimization Problem,Average Classification Accuracy,Classification Accuracy,Covariance Matrix,Facial Expressions,Gradient Ascent,Linear Discriminant Analysis,Objective Function,1D Vector,2D ...

We propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent ...

Wavelet Transform,1D Convolution,1D Convolutional Neural Network ... Convolutional Neural Network,Coupling Coefficient,Covariance Matrix,Covariate Values,Deep Belief Network,Denoising,Dense ...