Search results for "temporal"
showing 10 items of 1095 documents
Detecting Spatio-Temporal Dependance in Spatial Data Pooled over Time
2015
International audience; This paper addresses the possible problem related to using strictly spatial modelling techniques for spatial data poole d over time. For these data, such as real estate, the spatial dimension is present, but subject to constraints related to temporal dimension. Three empirical examples are presented to investigate the impact of neglecting the temporal dimension in spatial analysis and to show how such an approach overestimates the pattern of spatial dependence, and overestimates the spatial autoregressive coefficient estimated. If generalized to all other empirical applications, this conclusion may have important considerations if one tries to measure the effect of e…
Locally weighted spatio-temporal minimum contrast for Log-Gaussian Cox Processes
2022
We propose a local version of the spatio-temporal log-Gaussian Cox processes (LGCPs) employing the Local Indicators of Spatio-Temporal Association (LISTA) functions into the minimum contrast procedure to obtain space as well as time-varying parameters. We resort to the joint minimum contrast method fitting method to estimate the set of second-order parameters for the class of spatio-temporal LGCPs. This approach has the advantage of being usable in the case of both separable and non-separable parametric specifications of the correlation function of the underlying Gaussian Random Field (GRF).
Hawkes processes on networks for crime data
2022
Motivated by the analysis of crime data in Bucaramanga (Colombia), we propose a spatio-temporal Hawkes point process model adapted to events living on linear networks. We first consider a non-parametric modelling strategy, for both the background and the triggering components, and then we include a parametric estimation of the background based on covariates, and a non-parametric one of the triggering effects. Our network model outperforms a planar version, improving the fitting of the self-exciting point process model.
Dimensionality reduction for large spatio-temporal datasets based on SVD
2009
Many models for spatio-temporal measurements Z(s; t) can be written as a sum of a systematic component and a residual component: Z = M + E. The approach presented here incorporates two Singular Value Decompositions (SVD). The first SVD is applied to the space-time data matrix Z with cross-validation to choose the number of smoothed singular vectors to use as temporal basis functions for modelling spatially varying temporal trend in the matrix M. The second SVD is applied to the spatio-temporal matrix E of residuals from the trend models fitted at each site; it represents spatially correlated short time scale temporal processes. The remaining stochastic structure is explained by simple autor…
A spatio-temporal model based on the SVD to analyze large spatio-temporal datasets
2009
A common problem in the analysis of space-time data is to compress a large dataset in order to extract the underlying trends. Empirical orthogonal function (EOF) analysis is a useful tool for examining both the temporal and the spatial variation in atmospherical and physical process and a convenient method of performing this is the Singular Value Decomposition (SVD). Many spatio-temporal models for measurements Z(s; t) at location s at time t, can be written as a sum of a systematic component and a residual component: Z = M+E, where Z, M and E are all T x N matrices. Our approach permits modeling of incomplete data matrices using an "EM-like" iterative algorithm for the SVD. We model the tr…
Spatiotemporal Dynamics of the Processing of Spoken Inflected and Derived Words:A Combined EEG and MEG Study
2011
The spatiotemporal dynamics of the neural processing of spoken morphologically complex words are still an open issue. In the current study, we investigated the time course and neural sources of spoken inflected and derived words using simultaneously recorded electroencephalography (EEG) and magnetoencephalography (MEG) responses. Ten participants (native speakers) listened to inflected, derived, and monomorphemic Finnish words and judged their acceptability. EEG and MEG responses were time-locked to both the stimulus onset and the critical point (suffix onset for complex words, uniqueness point for monomorphemic words). The ERP results showed that inflected words elicited a larger left-late…
Funktionelle Bildgebung der Lunge mit gasförmigem Kontrastmittel: ³Helium-Magnetresonanztomographie
2005
Current imaging methods of the lung concentrate on morphology as well as on the depiction of the pulmonary parenchyma. The need of an advanced and more subtle imaging technology compared to conventional radiography is met by computed topography as the method of choice. Nevertheless, computed tomography yields very limited functional information. This is to be derived from arterial blood gas analysis, spirometry and body plethysmography. These methods, however, lack the scope for regional allocation of any pathology. Magnetic resonance imaging of the lung has been advanced by the use of hyperpolarised (3)Helium as an inhaled gaseous contrast agent. The inhalation of the gas provides function…
Statistical Properties of Statistical Ensembles of Stock Returns
1999
We select n stocks traded in the New York Stock Exchange and we form a statistical ensemble of daily stock returns for each of the k trading days of our database from the stock price time series. We analyze each ensemble of stock returns by extracting its first four central moments. We observe that these moments are fluctuating in time and are stochastic processes themselves. We characterize the statistical properties of central moments by investigating their probability density function and temporal correlation properties.
Spatio‐temporal classification in point patterns under the presence of clutter
2019
We consider the problem of detection of features in the presence of clutter for spatio-temporal point patterns. In previous studies, related to the spatial context, Kth nearest-neighbor distances to classify points between clutter and features. In particular, a mixture of distributions whose parameters were estimated using an expectation-maximization algorithm. This paper extends this methodology to the spatio-temporal context by considering the properties of the spatio-temporal Kth nearest-neighbor distances. For this purpose, we make use of a couple of spatio-temporal distances, which are based on the Euclidean and the maximum norms. We show close forms for the probability distributions o…
Self-exciting point process modelling of crimes on linear networks
2022
Although there are recent developments for the analysis of first and second-order characteristics of point processes on networks, there are very few attempts in introducing models for network data. Motivated by the analysis of crime data in Bucaramanga (Colombia), we propose a spatiotemporal Hawkes point process model adapted to events living on linear networks. We first consider a non-parametric modelling strategy, for which we follow a non-parametric estimation of both the background and the triggering components. Then we consider a semi-parametric version, including a parametric estimation of the background based on covariates, and a non-parametric one of the triggering effects. Our mode…