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Assessing the significance of global and local correlations under spatial autocorrelation;a nonparametric approach
Geostatistics Monte-Carlo methods Resampling Spatial autocorrelation Spatial statistics Variogram
2015/8/21
In this paper we present a method to assess the significance of the correlation coefficient when at least one of the variables is spatially autocorrelated. The standard test assumes independence of th...
A Directional Gradient-Curvature Method for Gap Filling of Gridded Environmental Spatial Data with Potentially Anisotropic Correlations
correlation anisotropy spatial interpolation stochastic estimation optimization simulation
2013/4/27
We introduce the Directional Gradient-Curvature (DGC) method, a novel approach for filling gaps in gridded environmental data. DGC is based on an objective function that measures the distance between ...
Quantile correlations and quantile autoregressive modeling
Autocorrelation function Box-Jenkins method Quantile correlation Quantile partial correlation Quantile autoregressive model
2012/11/23
In this paper, we propose two important measures, quantile correlation (QCOR) and quantile partial correlation (QPCOR). We then apply them to quantile autoregressive (QAR) models, and introduce two va...
Metric distances derived from cosine similarity and Pearson and Spearman correlations
Metric distances derived cosine similarity Pearson and Spearman correlations
2012/9/17
We investigate two classes of transformations of cosine similarity and Pearson and Spearman correlations into metric distances, utilising the simple tool of metric-preserving functions. The rst class...
A Bayesian Analysis of the Correlations Among Sunspot Cycles
Bayesian Analysis Correlations Among Sunspot Cycles
2012/9/18
Sunspot numbers form a comprehensive, long-duration proxy of so-lar activity and have been used numerous times to empirically investigate the properties of the solar cycle. A number of correlations ha...
Detection of correlations
Sparse covariance matrix minimax detection Bayesian detection scan statistic
2011/7/5
We consider the hypothesis testing problem of deciding whether an observed high-dimensional normal vector has independent components or, alternatively, if it has a small subset of correlated component...
Enhancing hyperspectral image unmixing with spatial correlations
Bayesian inference Monte Carlo methods spectral unmixing hyperspectral images Markovrandom fields Potts-Markov model
2010/3/10
This paper describes a new algorithm for hyperspectral image unmixing. Most of the unmixing
algorithms proposed in the literature do not take into account the possible spatial correlations
between t...