We also demonstrate that a similar random walk model that does not enforce successive correlations is unable to reproduce all features of the measured breakthrough curves.
Serial correlation among successive observations taken over a period of time reduces the statistical utility of captured imagery.
However, even in recent studies involving individuals tracked at a relatively high fix rate using global positioning system (GPS), movement information provided by serial correlation between successive relocations has largely been ignored: habitat use is most often estimated statically as if these relocations were unlinked [1] [3].
The detection of motion vectors relies on successive phase correlation operations applied to pairs of consecutive block partitioned frames of a video sequence.
For example, the average successive marker correlation of the segmented glioma data of Kotliarov et al. (2006) was 0.985.
It has been reported that the average successive probe correlation of the segmented data can be as high as 0.985 [ 9, 32].
These translocations were then shuffled to compute the distribution of the angles that was inherent to the network itself and not to the successive displacement correlation.
Burstiness refers to serial correlations in workload patterns such as correlation between successive arrivals of transactions and correlations in the resource consumption patterns at various system resources.
To account for the correlations between successive trials, we used a working correlation matrix with a first order autoregressive relationship (Liang and Zeger 1986).
We built a hierarchy of maximum entropy models to describe the group configurations, based on successive orders of correlations between the mice (one that relies only on individual behavior of the mice, one that adds pairwise dependencies between mice, third order, etc).
In the context of regression applied to signal processing, when the data is a time series, Land and Friedman, (1996) propose to encode the expected positive correlation between successive variables by choosing a regularization term that forces successive variables of the classifier to have similar weights.
