Search results for "Autoregressive model"
showing 10 items of 120 documents
A Two-Dimensional Autoregressive Model for MIMO Wideband Mobile Radio Channels
2008
In this work, we propose the multichannel two- dimensional (2D) autoregressive (AR) model for multiple-input multiple-output (MIMO) wideband mobile wireless channels. The parameters of the proposed model can be estimated from the real- world measurement data. For this purpose, we suggest using a straightforward extension of the prediction error minimization (PEM) algorithm. We also address the problem of possible instability of the multichannel 2D AR model. A model stabilization procedure based on numerical optimization techniques is proposed. The performance of the multichannel 2D AR model has been evaluated based on the synthetic data generated using two different channel simulators.
Decomposing and Interpreting Spatial Effects in Spatio-Temporal Analysis: Evidences for Spatial Data Pooled Over Time
2017
Empirical applications using individual spatial data pooled over time usually neglect the fact that such data are not only spatially localized: they are also collected over time, i.e. temporally localized. So far, little effort has been devoted to proposing a global way for dealing with spatial data (cross-section) pooled over time, such as real estate transactions, business start-up, crime and so on. However, the spatial effect, in such a context, can be decomposed in two different components: a multidirectional spatial effect (same time period) and a unidirectional spatial effect (previous time period). Based on real estate literature, this chapter presents different spatio-temporal autor…
A Comprehensive Spatiotemporal Framework for Hedonic Pricing: Integrating the Comparable Sales Approach and Minimizing Spatial Omitted Variable Bias
2019
This paper develops a theoretical and methodological framework that integrates Hedonic Pricing (HP), grid comparable sales approach (CSA), and nearest neighbors into a general spatiotemporal specification. By explicitly providing a theoretical justification for introducing spatial (or spatiotemporal) econometrics to HP, this approach is not only relevant to house price forecasting and automated valuation models (AVM) but also to valuing environmental goods capitalized in housing and to all other fields employing house pricing models. The resulting econometric CSA and spatiotemporal Durbin models provide higher prediction accuracy and reliability to alternatives by reducing the spatially-del…
Assessment of Granger causality by nonlinear model identification: application to short-term cardiovascular variability.
2007
A method for assessing Granger causal relationships in bivariate time series, based on nonlinear autoregressive (NAR) and nonlinear autoregressive exogenous (NARX) models is presented. The method evaluates bilateral interactions between two time series by quantifying the predictability improvement (PI) of the output time series when the dynamics associated with the input time series are included, i.e., moving from NAR to NARX prediction. The NARX model identification was performed by the optimal parameter search (OPS) algorithm, and its results were compared to the least-squares method to determine the most appropriate method to be used for experimental data. The statistical significance of…
Mutual nonlinear prediction of cardiovascular variability series: Comparison between exogenous and autoregressive exogenous models
2007
A model-based approach to perform mutual nonlinear prediction of short cardiovascular variability series is presented. The approach is based on identifying exogenous (X) and autoregressive exogenous (ARX) models by K-nearest neighbors local linear approximation, and estimates the predictability of a series given the other as the squared correlation between original and predicted values of the series. The method was first tested on simulations reproducing different types of interaction between non-identical Henon maps, and then applied to heart rate (HR) and blood pressure (BP) variability series measured in healthy subjects at rest and after head-up tilt. Simulations showed that different c…
Comparative Economic Cycles
2008
The income cycles that have been experienced by six OECD countries over the past 24 years are analysed. The amplitude of the cycles relative to the level of aggregate income varies amongst the countries, as does the degree of the damping that affects the cycles. The study aims to reveal both of these characteristics. It also seeks to determine whether there exists a clear relationship between the degree of damping and the length of the cycles. In order to estimate the parameters of the cycles, the data have been subjected to the processes of detrending, anti-alias filtering and subsampling.
Linear and nonlinear parametric model identification to assess granger causality in short-term cardiovascular interactions
2008
We assessed directional relationships between short RR interval and systolic arterial pressure (SAP) variability series according to the concept of Granger causality. Causality was quantified as the predictability improvement (PI) of a time series obtained when samples of the other series were used for prediction, i.e. moving from autoregressive (AR) to AR exogenous (ARX) prediction. AR and ARX predictions were performed both by linear and nonlinear parametric models. The PIs of RR given SAP and of SAP given RR, measuring baroreflex and mechanical couplings, were calculated in 15 healthy subjects in the resting supine and upright tilt positions. Using nonlinear models we found a bilateral i…
Need of causal analysis for assessing phase relationships in closed loop interacting cardiovascular variability series
2003
The phase spectra obtained by the classical closed loop autoregressive model (2AR) and by an open loop autoregressive model (ARXAR) were compared to shed light on the need of introducing causality in the assessment of the delay between RR and arterial pressure oscillations. The reliability of the two approaches was tested in simulation and real data setting. In simulation, the coupling strength of a bivariate closed loop process was adjusted to obtain a range of working conditions from open to closed loop. In open loop condition, 2AR and ARXAR phases were comparable and in agreement with the imposed delay. In closed loop condition, ARXAR model returned the imposed delays, while 2AR showed a…
Modeling Spatial Data Pooled over Time: Schematic Representation and Monte Carlo Evidences
2015
The spatial autocorrelation issue is now well established, and it is almost impossible to deal with spatial data without considering this reality. In addition, recent developments have been devoted to developing methods that deal with spatial autocorrelation in panel data. However, little effort has been devoted to dealing with spatial data (cross-section) pooled over time. This paper endeavours to bridge the gap between the theoretical modeling development and the application based on spatial data pooled over time. The paper presents a schematic representation of how spatial links can be expressed, depending on the nature of the variable, when combining the spatial multidirectional relatio…
Upport vector machines for nonlinear kernel ARMA system identification.
2006
Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA 2k) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based syste…