Deterministic regression smoothness priors TVAR modelling
نویسندگان
چکیده
In this paper we propose a method for the estimation of time-varying autoregressive processes. The approach is essentially to regularize the heavily underdetermined unconstrained prediction equations with a smoothness priors type side constraint. The implementation of nonhomogenous smoothness properties is straightforward. The method is compared to the usual determistic regression approach (TVAR) in which the coefficient evolutions are constrained to a subspace. It is shown that the typical transient oscillations of TVAR can be avoided with the proposed method.
منابع مشابه
Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multiwavelets
A new time-varying autoregressive (TVAR) modelling approach is proposed for nonstationary signal processing and analysis, with application to EEG data modelling and power spectral estimation. In the new parametric modelling framework, the time-dependent coefficients of the TVAR model are represented using a novel multi-wavelet decomposition scheme. The timevarying modelling problem is then redu...
متن کاملThe Impact of Spatial Scales and Spatial Smoothing on the Outcome of Bayesian Spatial Model
Discretization of a geographical region is quite common in spatial analysis. There have been few studies into the impact of different geographical scales on the outcome of spatial models for different spatial patterns. This study aims to investigate the impact of spatial scales and spatial smoothing on the outcomes of modelling spatial point-based data. Given a spatial point-based dataset (such...
متن کاملBayesian Inference for Spatial Beta Generalized Linear Mixed Models
In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...
متن کاملAs-Rigid-As-Possible Stereo under Second Order Smoothness Priors
Imposing smoothness priors is a key idea of the top-ranked global stereo models. Recent progresses demonstrated the power of second order priors which are usually defined by either explicitly considering three-pixel neighborhoods, or implicitly using a so-called 3D-label for each pixel. In contrast to the traditional first-order priors which only prefer fronto-parallel surfaces, second-order pr...
متن کاملCondensation-Based Contour Tracking with Sobolev Smoothness Priors
This paper proposes a combination of contour deformation modelling in Sobolev spaces and the Condensation filter to track an object over a sequence of images. As Sobolev spaces are smoothness spaces this allows to control the smoothness of the contour deformation extending previous wavelet representations. We also introduce a probabilistic model for the wavelet deformation of the contour that i...
متن کامل