This paper describes a method of matrix decomposition which retains the ability of factor analytic techniques to summarize data in terms of a relatively low number of coordinates; but at the same time, does not sacrifice the useful analysis of variance heuristic of partitioning data matrices into independent sources of variation which are relatively simple to interpret. The basic model is essen...

This paper gives an overview of results and developments in the area of pricing and hedging contingent claims in an incomplete market by means of a quadratic criterion. We first present the approach of risk-minimization in the case where the underlying discounted price process X is a local martingale. We then discuss the extension to local risk-minimization when X is a semimartingale and explai...

This paper describes some of the basic applications of the algebraic theory of canonical decomposition to the analysis of data. The notions of structured data and symmetry studies are discussed and applied to demonstrate their role in well known principles of analysis of variance and their applicability in more general experimental settings.

Artificially generated functional magnetic resonance imaging (fMRI) data drawn from a block-based visual stimulation paradigm were analyzed by the stochastic neuromorphic extended BS Infomax algorithm [1] implementing spatial independent component analysis (ICA) [2]. Variance estimate based on bootstrap resampling [3] was employed as model selection criterion and reliability assessment of ICA d...

Estimation of Distribution Algorithms are based on statistical estimates. We show that when combining classical tools from statistics, namely bias/variance decomposition, reweighting and quasirandomization, we can strongly improve the convergence rate. All modifications are easy, compliant with most algorithms, and experimentally very efficient in particular in the parallel case (large offsprin...

For a large class of vanilla contingent claims, we establish an explicit Föllmer-Schweizer decomposition when the underlying is an exponential of an additive process. This allows to provide an efficient algorithm for solving the mean variance hedging problem. Applications to models derived from the electricity market are performed.

In view of increasing application of sensitivity assessment (SA) to environmental simulation models, a relatively short, informal introduction to aims and methods of SA is given. Their variety, motivation and scope are illustrated byoutlines of a broad selection of approaches.Methods based on derivatives, algebraic analysis, sparse sampling, variance decomposition, Fourier analysis and binary c...

Imposing structure on the Smith form of an (integer) periodicity matrix N = U V leads to e cient m-D DFT implementations. For resampling matrices, i.e. non-singular rational matrices, we introduce Smith form decomposition algorithms to generate matrices whose diagonal elements exhibit minimum variance and U matrices with minimum norm. Such structure simpli es non-uniform m-D lter bank design.

BACKGROUND We compared patterns of genetic and environmental influences on variation in liability for asthma, hay fever and eczema with those for symptoms of the same diseases, and determined how common sets of genes and environmental factors contribute to the relationship between diseases and symptoms among Norwegian twins. METHODS Analyses were based on self-reported asthma, hay fever and e...

MAXIMUM LIKELIHOOD ESTIMATOR OF Drops out of computation of likelihood ratio. DISTRIBUTION OF CONSTRAINT Under normal errors the Euclidean inner product turns out to be practically normal with variance: CONSTRAINTS Five or more orthogonality relations in the form of the Euclidean inner product determine the absolute conic. Rectifying transformation is obtained by Cholesky decomposition. Active ...