In a multiple linear regression model, there are instances where one has to update the regression parameters. In such models as new data become available, by adding one row to the design matrix, the least-squares estimates for the parameters must be updated to reflect the impact of the new data. We will modify two existing methods of calculating regression coefficients in multiple linear regres...

this research presents a numerical tool to estimate the green's function operator matrix of intraplate faults. having this matrix and its inverse, spatial distribution of fault slippage could be investigated through the inverse analysis of geodetic data. this information could be employed to predict the location of future powerful earthquakes. to implement fault sliding in fe calculations,...

We consider the problem of extracting joint and individual signals from multi-view data, that is, data collected different sources on matched samples. While existing methods for decomposition explore single matching by samples, we focus double-matched (matched both samples source features). Our motivating example is miRNA primary tumor normal tissues same subjects; measurements two are thus sub...

Covariance matrices are among the most difficult pieces of end-to-end cosmological analyses. In principle, for two-point functions, each component involves a four-point function, and resulting covariance often has hundreds thousands elements. We investigate various compression mechanisms capable vastly reducing size matrix in context cosmic shear statistics. This helps identify which its parts ...

Abstract A separable covariance model can describe the among-row and among-column correlations of a random matrix permits likelihood-based inference with very small sample size. However, if assumption separability is not met, data analysis may misrepresent important dependence patterns in data. As compromise between unstructured estimation, we decompose into component complementary ‘core’ matri...