The significance of land cover as an environmental variable has made land use change an important subject in global environmental change and sustainable development. Modeling land use change has attracted considerable attention. Currently, empirical estimation models using statistical techniques are one of mostly used spatial models to simulate land use pattern and its changes. Empirical estima...

Spatial autocorrelation (SAC), defined as the positive association between sample similarity and spatial proximity, is pervasive in ecological data. Several methods exist for the incorporation of SAC into statistical models, but not until recently have these methods been applied to species distribution data. When incorporated into species distribution models, SAC has been found to significantly...

Wireless channels are characterized by highly dynamic time-varying nature often manifesting non-stationary behavior. In this paper we consider the state of a wireless channel in terms of the piecewise covariance stationary signal-to-noise ratio (SNR) process and parameterize it using the probability distribution function of the received SNR and lag-1 autocorrelation coefficient of the correspon...

The random quantum Ashkin-Teller chain is studied numerically by means of time-dependent Density-Matrix Renormalization Group. The critical lines are estimated as the location of the peaks of the integrated autocorrelation times, computed from spin-spin and polarization-polarization autocorrelation functions. Disorder fluctuations of magnetization and polarization are observed to be maximum on ...

Given the recent trend towards validating the neuroimaging statistical methods, we compared the most popular functional magnetic resonance imaging (fMRI) analysis softwares: AFNI, FSL and SPM, with regard to temporal autocorrelation modelling. We used both resting state and task-based fMRI data, altogether 10 datasets containing 780 scans corresponding to different scanning sequences and differ...

A novel statistical image model is proposed to facilitate the design and analysis of image processing algorithms. A mean-removed image neighborhood is modeled as a scaled segment of a hypothetical texture source, characterized as a 2-D stationary zero-mean unit-variance random field, specified by its autocorrelation function. Assuming that statistically similar image neighborhoods are derived f...

When the input to a quantizer is a sampled time series represented by x1, x2, x3, . . . , the quantization noise is a time series represented by ν1, ν2, ν3, . . . Suppose that the input time series is stationary and that its statistics satisfy the conditions for multivariable QT II (it would be sufficient that two-variable QT II conditions were satisfied for x1 and x2, x1 and x3, x1 and x4, and...

Local high-order autocorrelation features proposed by Otsu have been successfully applied to face recognition and many other pattern recognition problems. These features are invariant under translation, but not invariant under scale and rotation. We construct scale and rotation invariant features from the local high-order autocorrelation features. x 1 Introduction In pattern recognition problem...