In this paper, we provide a detailed analysis of the global convergence properties of an extensively studied and extremely effective fixed-point algorithm for the Kullback–Leibler approximation of spectral densities, proposed by Pavon and Ferrante in [1]. Our main result states that the algorithm globally converges to one of its fixed points. Index Terms Approximation of spectral densities, spe...

We develop a new framework to calibrate stochastic volatility option pricing models to an arbitrary prescribed set of prices of liquidly traded options. Our approach produces an arbitrage-free stochastic volatility di usion process that minimizes the distance to a prior di usion model. We use the notion of relative entropy (also known under the name of Kullback-Leibler distance) to quantify the...

The Bayesian variable selection method proposed in the paper is based on the evaluation of the Kullback-Leibler distance between the full (or encompassing) model and the submodels. The implementation of the method does not require a separate prior modeling on the submodels since the corresponding parameters for the submodels are deened as the Kullback-Leibler projections of the full model param...

Reply to " Comments on Kullback-Leibler and renormal-ized entropies: Applications to electroencephalograms of epilepsy patients ". Abstract Kopitzki et al (preceeding comment) claim that the relationship between Renormalized and Kullback-Leibler entropies has already been given in their previous papers. Moreover, they argue that the first can give more useful information for e.g. localizing the...

This paper demonstrates the performance of two possible CAT selection strategies for cognitive diagnosis. One is based on Shannon entropy and the other is based on Kullback-Leibler information. The performances of these two test construction methods are compared with random item selection. The cognitive diagnosis model used in this study is a simplified version of the Fusion model. Item banks a...

This paper proposes a novel online algorithm for nonnegative matrix factorization (NMF) based on the generalized Kullback-Leibler (KL) divergence criterion, aimed to overcome the high computation problem of large-scale data brought about by conventional batch NMF algorithms. It features stable updating the factors alternately for each new-coming observation, and provides an efficient solution f...

This paper presents a new similarity measure based on Rao distance for color texture classification or retrieval. Textures are characterized by a joint model of complex wavelet coefficients. This model is based on a Gaussian Copula in order to consider the dependency between color components. Then, a closed form of Rao distance is computed to measure the difference between two Gaussian Copula b...