Functional magnetic resonance imaging (fMRI) time series analysis and statistical inferences about the effect of a cognitive task on the regional cerebral blood flow (rCBF) are largely based on the linear model. However, this method requires that the error vector is a gaussian variable with an identity correlation matrix. When this assumption cannot be accepted, statistical inferences can be ma...

I N recent years, statistical process control (SPC) for autocorrelated processes has received a great deal of attention, due in part to the increasing prevalence of autocorrelation in process inspection data. With improvements in measurement and data collection technology, processes can be sampled at higher rates, which often leads to data autocorrelation. It is well known that the run length p...

introduction in 1950s, only 30% of the world's population lived in urban areas. by 2000, that proportion increasedup to 47%, and by 2050 the estimated number will be around 72%. urban sprawl, an undesirable type of urban growth, is one of the major concerns ofthe city planners and administrators. understanding urban patterns, dynamic processes, and their relationships is a primary objectiv...

Autocorrelation in animal movements can be both a serious nuisance to analysis and a source of valuable information about the scale and patterns of animal behavior, depending on the question and the techniques employed. In this paper we present an approach to analyzing the patterns of autocorrelation in animal movements that provides a detailed picture of seasonal variability in the scale and p...

In many spatial data applications, the events at a location are highly influenced by the events at neighboring locations. In fact, this natural inclination of a variable to exhibit similar values as a function of distance between the spatial locations at which it is being measured is known as spatial dependence. Spatial autocorrelation is used to measure this spatial dependence. If the variable...

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatia...

The impact of among-environment heteroscedasticity and genetic autocorrelation on the analysis of phenotypic plasticity is examined. Among-environment heteroscedasticity occurs when genotypic variances differ among environments. Genetic autocorrelation arises whenever the responses of a genotype to different environments are more or less similar than expected for observations randomly associate...