Search results
Results from the WOW.Com Content Network
The autocorrelation matrix is used in various digital signal processing algorithms. For a random vector = (, …,) containing random elements whose expected value and variance exist, the autocorrelation matrix is defined by [3]: p.190 [1]: p.334
Geary's C is a measure of spatial autocorrelation that attempts to determine if observations of the same variable are spatially autocorrelated globally (rather than at the neighborhood level). Spatial autocorrelation is more complex than autocorrelation because the correlation is multi-dimensional and bi-directional.
One version of this is to use covariance matrices as the multivariate measure of dispersion. Several authors have considered tests in this context, for both regression and grouped-data situations. [ 28 ] [ 29 ] Bartlett's test for heteroscedasticity between grouped data, used most commonly in the univariate case, has also been extended for the ...
Indicators of spatial association are statistics that evaluate the existence of clusters in the spatial arrangement of a given variable. For instance, if we are studying cancer rates among census tracts in a given city local clusters in the rates mean that there are areas that have higher or lower rates than is to be expected by chance alone; that is, the values occurring are above or below ...
In statistics, Moran's I is a measure of spatial autocorrelation developed by Patrick Alfred Pierce Moran. [ 1 ] [ 2 ] Spatial autocorrelation is characterized by a correlation in a signal among nearby locations in space.
Visual comparison of convolution, cross-correlation and autocorrelation.. A correlation function is a function that gives the statistical correlation between random variables, contingent on the spatial or temporal distance between those variables. [1]
The Breusch–Godfrey test is a test for autocorrelation in the errors in a regression model. It makes use of the residuals from the model being considered in a regression analysis, and a test statistic is derived from these. The null hypothesis is that there is no serial correlation of any order up to p. [3]
The autocorrelation technique is a method for estimating the dominating frequency in a complex signal, as well as its variance. Specifically, it calculates the first two moments of the power spectrum, namely the mean and variance. It is also known as the pulse-pair algorithm in radar theory.