The network autocorrelation model has been extensively used by researchers interested modeling social influence effects in social networks. The most common inferential method in the model is classical maximum likelihood estimation. This approach, however, has known problems such as negative bias of the network autocorrelation parameter and poor coverage of confidence intervals. In this paper, w...

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...

1. Individuals can show positive correlations in performance (e.g. growth and reproduction) through time beyond the effects of size or age. This ‘performance autocorrelation’ has been attributed previously to traits that differ among individuals or to extrinsic generators of environmental heterogeneity. 2. A model of mobile consumers on a dynamic resource showed that consumer foraging gave rise...

In functional magnetic resonance imaging statistical analysis there are problems with accounting for temporal autocorrelations when assessing change within voxels. Techniques to date have utilized temporal filtering strategies to either shape these autocorrelations or remove them. Shaping, or "coloring," attempts to negate the effects of not accurately knowing the intrinsic autocorrelations by ...

We have used aperiodically poled lithium niobate waveguides to perform intensity autocorrelation and frequency-resolved optical gating (FROG) measurements for ultraweak femtosecond pulses at 1.5 microm wavelength. The required pulse energies for intensity autocorrelation and FROG are as low as 52 aJ and 124 aJ, respectively. The corresponding sensitivities are 3.2 x 10(-7) mW(2) and 2.7 x 10(-6...

To estimate a time series model for multiple individuals, a multilevel model may be used. In this paper we compare two estimation methods for the autocorrelation in Multilevel AR(1) models, namely Maximum Likelihood Estimation (MLE) and Bayesian Markov Chain Monte Carlo. Furthermore, we examine the difference between modeling fixed and random individual parameters. To this end, we perform a sim...

Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pi...

Masami Fujiwara*, Bruce E. Kendall and Roger M. Nisbet Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA, USA Donald Bren School of Environmental Science and Management, University of California, Santa Barbara, CA, USA *Correspondence: E-mail: [email protected] Abstract It has long been recognized that variability in animal size is affected...

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...

In this paper, we analyze the autocorrelation properties of seven fundamental classes of stochastic sum-of-cisoids (SOC) simulation models for narrowband mobile Rayleigh fading channels. The purpose of this analysis is to determine which classes of SOC models are autocorrelation ergodic (AE), i.e., for which classes of simulation models the time autocorrelation function (TACF) equals the autoco...