Road traffic estimation systems can rely nowadays on an increasing number and variety of sensors and data sources to provide better coverage and accuracy, from standard static detectors to, more recently, location traces obtained possibly from individual drivers’ smartphones. Motivated by privacy concerns raised by such systems, this paper discusses a methodology for estimating the macroscopic ...

We propose a novel alternative to case-control sampling for the estimation of individual-level risk in spatial epidemiology. Our approach uses weighted estimating equations to estimate regression parameters in the intensity function of an inhomogeneous spatial point process, when information on risk-factors is available at the individual level for cases, but only at a spatially aggregated level...

The following chapter tackles the nonparametric identification and the state estimation for uncertain chaotic systems by the dynamic neural network approach. The developed algorithms consider the presence of additive noise in the state, for the case of identification, and in the measurable output, for the state estimation case. Mathematical model of the chaotic system is considered unknown, onl...

In this paper we propose a dimension reduction method to estimate the directions in a multiple-index regression based on information extraction. This extends the recent work of Yin and Cook (2005) who introduced the method and used it to estimate the direction in a single-index regression. While a formal extension seems conceptually straightforward, there is a fundamentally new aspect of our ex...

During early stages of product development process, a vast amount of knowledge and information is generated. However, most of it is subjective (imprecise) in nature and remains unutilized. This paper presents a formal structure for capturing this information and knowledge and utilizing it in reliability improvement estimation. The information is extracted as improvement indices from various des...

We consider an input x generated by an unknown stationary ergodic source X that enters a signal processing system J , resulting in w = J(x). We observe w through a noisy channel, y = z(w); our goal is to estimate x from y, J , and knowledge of fY |W . This is universal estimation, because fX is unknown. We provide a formulation that describes a trade-off between information complexity and noise...

We study the problem of using i.i.d. samples from an unknown multivariate probability distribution p to estimate the mutual information of p. This problem has recently received attention in two settings: (1) where p is assumed to be Gaussian and (2) where p is assumed only to lie in a large nonparametric smoothness class. Estimators proposed for the Gaussian case converge in high dimensions whe...

in synthetic aperture radar (sar) imagery, ship-sea contrast can be significantly improved when the polarimetric information is used, compared with information from a single channel sar. the constant false alarm rate (cfar) detection algorithm based on the alpha-stable (as) distribution model is a descent method for ship detection in sea. the most important step in this method is the parameter ...

We investigate kernel density estimation where the kernel function varies from point to point. Density estimation in the input space means to find a set of coordinates on a statistical manifold. This novel perspective helps to combine efforts from information geometry and machine learning to spawn a family of density estimators. We present example models with simulations. We discuss the princip...