Premium
On Hypotheses Testing for the Selection of Spatio‐Temporal Models
Author(s) -
Antunes Ana Mónica C.,
Rao Tata Subba
Publication year - 2006
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2006.00488.x
Subject(s) - autoregressive model , multivariate statistics , model selection , selection (genetic algorithm) , mathematics , sampling (signal processing) , star model , econometrics , statistical hypothesis testing , statistics , data mining , computer science , autoregressive integrated moving average , time series , artificial intelligence , filter (signal processing) , computer vision
. Several models have been proposed in recent years for analysing spatial data and also, to some extent, spatio‐temporal data. One of the important problems, namely the choice of an appropriate model for describing real data sets, remains unsolved. Here we consider the analysis of spatio‐temporal processes from which observations over space and time are available. We propose statistical tests for discriminating between space–time autoregressive processes and multivariate autoregressive processes. The sampling properties of the proposed tests are considered. We illustrate the methods with a real example. We use the above tests to find the best model to describe spatio‐temporal variations of hourly carbon monoxide measurements at four locations in London in January 2004.