Under what circumstances can a static voting mechanism aggregate dispersed information of committee members? I argue that whenever the voters are able to cast multiple votes, the quality of the joint decision increases. However, voting mechanisms are intrinsically additive ways of aggregating private information. This, naturally, is not a binding constraint if the private information is conditi...

Spatial heteroskedasticity may arise jointly with spatial autocorrelation in lattice data collected from agricultural trials and environmental studies. This leads to spatial clustering not only in the level but also in the variation of the data, the latter of which may be very important, for example, in constructing prediction intervals. This article introduces a spatial stochastic volatility (...

Identifiability of sensor gain faults is studied and a novel approach to fault detection and diagnosis of a class of nonlinear systems is presented. Sensor gain faults are divided into two classes: the conditionally identifiable faults and the conditionally detectable faults. An algorithm is proposed to obtain the number and location of the conditionally identifiable faults. Asymptotic estimati...

This paper presents a novel conditionally suboptimal filtering algorithm on estimation problems that arise in discrete nonlinear time-varying stochastic difference systems. The suboptimal state estimate is formed by summing of conditionally nonlinear filtering estimates that their weights depend only on time instants, in contrast to conditionally optimal filtering, the proposed conditionally su...

Representations of branching Markov processes and their measurevalued limits in terms of countable systems of particles are constructed for models with spatially varying birth and death rates. Each particle has a location and a “level,” but unlike earlier constructions, the levels change with time. In fact, death of a particle occurs only when the level of the particle crosses a specified level...

We propose to use gradient distribution specification for image enhancement. The specified gradient distribution is learned from natural-scene image datasets. This enhances image quality based on two facts: First, the specified distribution is independent of image content. Second, the distance between the learned distribution and the empirical distribution of a given image correlates with subje...

We give a precise characterization of two important classes of conditionally positive definite (CPD) kernels in terms of integral transforms of infinitely divisible distributions. In particular, we show that for any stationary CPD kernel A(x, y) = f(x − y), f is the log-characteristic function of a uniquely determined infinitely divisible distribution; further, for any additive CPD kernel A(x, ...

Ito’s construction of Markovian solutions to stochastic equations driven by a Lévy noise is extended to nonlinear distribution dependent integrands aiming at the effective construction of linear and nonlinear Markov semigroups and the corresponding processes with a given pseudo-differential generator. It is shown that a conditionally positive integro-differential operator (of the Lévy-Khintchin...