Simultaneous and temporal autoregressive network models

Fitting Conditional and Simultaneous Autoregressive Spatial Models in hglm

We present a new version (> 2.0) of the hglm package for fitting hierarchical generalized linear models (HGLMs) with spatially correlated random effects. CAR() and SAR() families for conditional and simultaneous autoregressive random effects were implemented. Eigen decomposition of the matrix describing the spatial structure (e.g., the neighborhood matrix) was used to transform the CAR/SAR rand...

Robust speaker recognition using spectro-temporal autoregressive models

Speaker recognition in noisy environments is challenging when there is a mis-match in the data used for enrollment and verification. In this paper, we propose a robust feature extraction scheme based on spectro-temporal modulation filtering using two-dimensional (2-D) autoregressive (AR) models. The first step is the AR modeling of the sub-band temporal envelopes by the application of the linea...

On the Stationarity of Autoregressive Neural Network Models

Spatial-Temporal Autoregressive Dynamic Model

Although a myriad of methods have been advanced to tackle spatial and temporal structures in data separately, it becomes difficult to analyze these data using classical linear regression models when spatial-temporal structures coexist, especially when the data size is relatively large. In this article, we demonstrate a simple to implement method to handle spatial-temporal structures simultaneou...

Bidirectional Texture Function Simultaneous Autoregressive Model

The Bidirectional Texture Function (BTF) is the recent most advanced representation of visual properties of surface materials. It specifies their altering appearance due to varying illumination and viewing conditions. Corresponding huge BTF measurements require a mathematical representation allowing simultaneously extremal compression as well as high visual fidelity. We present a novel Markovia...