Source extraction and dimensionality reduction are important in analyzing high dimensional and complex financial time series that are neither Gaussian distributed nor stationary. Independent component analysis (ICA) method can be used to factorize the data into a linear combination of...

This paper introduces a model-free decomposition of S&P 500 forward market index returns in terms of realized and implied dispersion, downside, and tail risk using option portfolios. The decomposition lends itself by construction to learn about the different sources of risk in the market return,...

An important problem of the statistical analysis of time series is to detect change-points in the mean structure. Since this problem is a one-dimensional version of the higher dimensional problem of detecting edges in images, we study detection rules which benefit from results obtained in image...

An iterative technique for the removal of artifacts caused by the near field effects of a coded aperture imaging system is presented. The technique, we we call z-Clean, first locates high energy sources within a three dimensional field of view using a least squares method and then removes the...

This article presents a local entropy based image reconstruction algorithm that performs quite well in cases where there is distortion in an image. If the "wanted" image information is still available but distributed over two or more distinct images, the algorithm can collect the required...

An important problem of the statistical analysis of time series is to detect change-points in the mean structure. Since this problem is a one-dimensional version of the higher dimensional problem of detecting edges in images, we study detection rules which benefit from results obtained in image...