Structural Analysis for the First Order Autoregressive Stochastic Models

Autoregressive Gaussian Random Vectors of First Order

Modified Maximum Likelihood Estimation in First-Order Autoregressive Moving Average Models with some Non-Normal Residuals

When modeling time series data using autoregressive-moving average processes, it is a common practice to presume that the residuals are normally distributed. However, sometimes we encounter non-normal residuals and asymmetry of data marginal distribution. Despite widespread use of pure autoregressive processes for modeling non-normal time series, the autoregressive-moving average models have le...

Bernoulli matrix approach for matrix differential models of first-order

The current paper contributes a novel framework for solving a class of linear matrix differential equations. To do so, the operational matrix of the derivative based on the shifted Bernoulli polynomials together with the collocation method are exploited to reduce the main problem to system of linear matrix equations. An error estimation of presented method is provided. Numerical experiments are...

Higher-order Autoregressive Models for Dynamic Textures

Dynamic textured sequences are characterized by the interactions between many particles or objects in the scene. Based on earlier work the images of the sequence are interpreted as the output of a linear autoregressive process driven by white Gaussian noise. We extend earlier work by increasing the amount temporal information included when learning the motion in the scene, allowing the models t...

Order selection for vector autoregressive models

Order-selection criteria for vector autoregressive (AR) modeling are discussed. The performance of an order-selection criterion is optimal if the model of the selected order is the most accurate model in the considered set of estimated models: here vector AR models. Suboptimal performance can be a result of underfit or overfit. The Akaike information criterion (AIC) is an asymptotically unbiase...