Robust estimation of sparse vector autoregressive models

Robust Estimation in Linear Regression with Molticollinearity and Sparse Models

‎One of the factors affecting the statistical analysis of the data is the presence of outliers‎. ‎The methods which are not affected by the outliers are called robust methods‎. ‎Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers‎. ‎Besides outliers‎, ‎the linear dependency of regressor variables‎, ‎which is called multicollinearity...

Robust Estimation in Vector Autoregressive Models Based on a Robust Scale

A new class of robust estimates for vector autoregressive processes is proposed. The autoregressive coefficients and the covariance matrix of the innovations are estimated simultaneously by minimizing the determinant of the covariance matrix estimate, subject to a constraint on a robust scale of the Mahalanobis norms of the innovation residuals. By choosing as robust scale a τ− estimate, the re...

Vector Autoregressive Models

Sparse seasonal and periodic vector autoregressive modeling

Seasonal and periodic vector autoregressions are two common approaches to modeling vector time series exhibiting cyclical variations. The total number of parameters in these models increases rapidly with the dimension and order of the model, making it difficult to interpret the model and questioning the stability of the parameter estimates. To address these and other issues, two methodologies f...

Estimating Structured Vector Autoregressive Models

While considerable advances have been made in estimating high-dimensional structured models from independent data using Lasso-type models, limited progress has been made for settings when the samples are dependent. We consider estimating structured VAR (vector auto-regressive model), where the structure can be captured by any suitable norm, e.g., Lasso, group Lasso, order weighted Lasso, etc. I...