Data processing and source identification using lower dimensional hidden structure plays an essential role in many fields of applications, including image processing, neural networks, genome studies, signal processing and other areas where large datasets are often encountered. One of the common...

We suggest a new approach for forecasting energy demand at an intraday resolution. The demand in each intraday period is modeled using semiparametric regression smoothing to account for calendar and weather components. Residual serial dependence is captured by one of two multivariate stationary...

We present two deconvolution estimators for the density function of a random variable X that is measured with error. The first estimates the density of X from the set of independent replicate measurements W[subscript r,j], where W[subscript r,j]=X[subscript x]+U[subscript r,j] for r=1,...,n and...

Tech Report

Abstract Many statistical estimation techniques for high-dimensional or functional data are based on a preliminary dimension reduction step, which consists in projecting the sample X 1 ,..., X n onto the first D eigenvectors of the Principal Component Analysis (PCA) associated with the empirical...

We describe an algorithm to efficiently compute maximum entropy densities, i.e. densities maximizing the Shannon entropy - [image omitted]  under a set of constraints [image omitted] . Our method is based on an algorithm by Zellner and Highfield, which has been found not to converge under a...

Discretely rebalanced options arbitrage strategies in the presence of transaction costs have path dependent returns that are difficult to model analytically. I instead use a quasi-analytic procedure that combines the computational efficiency of analytical solutions with the flexibility of...