Covariances play a fundamental role in the theory of time series and they are critical quantities that are needed in both spectral and time domain analysis. Estimation of covariance matrices is needed in the construction of confidence regions for unknown parameters, hypothesis testing, principal component analysis, prediction, discriminant analysis among others. In this paper we consider both l...

in this paper, an unsupervised method for change detection in the multitemporal multipolarization synthetic aperture radar (sar) imagery is proposed. a matrix distance measure, named symmetric revised wishart is used as a test statistic in order to assess the similarity of two multitemporal multilook polarimetric sar data and a variance-based thresholding algorithm is applied to the test statis...

In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through deviations of transformed random process find an efficient estimate Hurst exponent eigenvalue regression covariance matrix. The results simulations experiments shown that performance proposed estimator was in bias but variance get increase as signal change from short long me...

An extension to the Dual Response approach to Robust Parameter Design for the case of multiple responses is proposed. The methodology provides unbiased estimates of the process covariance matrix and of the vector of expected values using parameter estimates from a multivariate regression fit. There are no restrictions on the types of experimental designs that can be used, apart from their abili...

The presence of multicollinearity in regression data is no exception in real life examples. Instead of applying ordinary regression methods, biased regression techniques such as Principal Component Regression and Ridge Regression have been developed to cope with such data sets. In this paper we consider Partial Least Squares (PLS) regression by means of the SIMPLS algorithm. Because the SIMPLS ...