A martingale approach to scan statistics

Scan statistics are commonly used in biology, medicine, engineering and other fields where interest is in the probability of observing clusters of events in a window at an unknown location. Due to the dependent nature of the number of events in a large number of overlapping window locations, even approximate solutions for the simplest scan statistics may require elaborate calculations. We propose a new martingale method which allows one to approximate the distribution for a wide variety of scan statistics, including some for which analytical results are computationally infeasible.     

A martingale approach to minimal surfaces

We provide a probabilistic approach to studying minimal surfaces in R³. After a discussion of the basic relationship between Brownian motion on a surface and minimality of the surface, we introduce a way of coupling Brownian motions on two minimal surfaces. This coupling is then used to study two classes of results in minimal surface theory, maximum principle-type results, such as weak and strong halfspace theorems and the maximum principle at infinity, and Liouville theorems.     

A new approach to optimum state estimation in optimum system control

We consider optimum system control, in which the controller has imperfect information on the availability of the state of the plant to control. The system is controlled via an estimation of the state. We propose a general estimation criterion, which should permit determination of the optimal estimator for a given dynamics and a given performance cost. This approach includes the separation theorem as a special case.     

A martingale approach to directed polymers in a random environment

The diffusive behavior for a system of directed polymers in a random environment was first rigorously discussed by Imbrie and Spencer, and then by Bolthausen. By means of some basic properties of martingales we extend some results due to Imbrie and Spencer concerning the asymptotic behaviour of the mean square displacement. We also obtain a Wiener process behaviour with probability one for this system. Bolthausen already used some martingale limit theorems to prove a central limit theorem for this system.Partly supported by AvH Foundation.     

Recursive state estimation for hybrid systems

The paper deals with recursive state estimation for hybrid systems. An unobservable state of such systems is changed both in a continuous and a discrete way. Fast and efficient online estimation of hybrid system state is desired in many application areas. The presented paper proposes to look at this problem via Bayesian filtering in the factorized (decomposed) form. General recursive solution is proposed as the probability density function, updated entry-wise. The paper summarizes general factorized filter specialized for (i) normal state-space models; (ii) multinomial state-space models with discrete observations; and (iii) hybrid systems. Illustrative experiments and comparison with one of the counterparts are provided.     

Recursive estimation: A unified approach to the identification estimation,and forecasting of hydrological systems

Mathematical models of hydrological and water-resource systems have been formulated in many different ways and with various levels of complexity. There are advantages to be gained, therefore, by trying to unify some of the more common models within a statistical framework which will allow for more objective methods of model calibration. In this paper, we consider the general class of linear, dynamic models, as applied to the characterisation of flow and dispersion behavior in rivers, and show how these can be unified within the context of recursive time-series analysis and estimation. This allows not only for more objective, data-based approaches to stochastic model structure identification, but also for improved statistical estimation and the development of both constant parameter and self-adaptive, Kalman-filter-based forecasting procedures. The unified approach presented in the paper is being applied successfully in other environmental areas, such as soil science, climatic data analysis, meterological forecasting, and plant physiology.     

A martingale approach to the convergence of the iterates of a transition function

Probability Theory and Related Fields -     

Oscillating solutions of scalar delay-differential equations with state dependence

This paper is concerned with the oscillatory behavior of the delay-differential equation X'(t)=F(t,x_t) including the equations x'(t)=-a(t)x(t-r(t,x(t))), [display math001] as special cases.We give conditions for the existence of a nonoscillatory solution of (1) and criteria for the oscillation of all solutions of (1), aiming at extending or generalizing to (1) some of the recent oscillation and nonoscillation results for delay equations of the form x'(t)=-a(t)x(t-p)).     

Minimax state estimation of continuous-discrete dynamic systems

Formulas are given for minimax estimation of the state vector of a linear continuous dynamic system in which the incoming information is processed and used at discrete time instants. The uncertainty region is constructed which may contain the system phase vector given the sampled observations. Some new approaches to experiment control geared to efficient estimation are described.Kiev University. Translated from Vychislitel'naya i Prikladnaya Matematika, No. 68, pp. 108–113, 1989.     

On-line state estimation for nonlinear dynamical systems

A problem of estimation of states of a dynamical object on the base of incomplete and inexact measurements is studied. The focus is on a set-based estimation of states required for construction of optimal guaranteeing feedbacks. In particular, the aim is to construct at every current time instant polyhedral approximations of sets of all states consistent with the obtained measurements. A problem of constructing estimates, associated with these approximations, is called an optimal observation problem. This note presents a numerical method for solving optimal observation problems for nonlinear dynamical systems. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Approximate counting:a martingale approach

Approximate counting is a probabilistic algorithm for keeping track of large numbers of events by means of a counter of limited range. In this paper we present an analysis of this algorithm using the elementary theory of martingales. The methods are also applicable to the analysis of the counter which occurs in the exponential back off protocol     

A market-based martingale valuation approach to optimum inventory control in a doubly stochastic jump-diffusion economy

We propose a novel market-based approach to optimum inventory control in a doubly stochastic jump-diffusion economy by modelling a commodity distributor’s inventory investment as a portfolio of forward commitments with explicit accounting of the jump-diffusion dynamics of demands, costs, and prices in open markets. We apply the robust real-asset martingale valuation methodology to derive a closed-form solution for the inventory value and a simple and intuitive optimality condition. Numerical analysis verifies this condition and demonstrates that the resulting optimum policy has robust properties in relation to the stylized effects.     

Weak convergence to a Markov process: The martingale approach

Summary In this article, we obtain some sufficient conditions for weak convergence of a sequence of processes {X _n } toX, whenX arises as a solution to a well posed martingale problem. These conditions are tailored for application to the case when the state space for the processesX _n ,X is infinite dimensional. The usefulness of these conditions is illustrated by deriving Donsker's invariance principle for Hilbert space valued random variables. Also, continuous dependence of Hilbert space valued diffusions on diffusion and drift coefficients is proved.Research supported by National Board for Higher Mathematics, Bombay, IndiaPart of the work was done at University of California, Santa Barbara, USA     

An approach via state estimation to differential games with information time lag

This paper deals with zero-sum two-person differential games in which one player has a deferred information on the state vector. This player mends this lack of information by using an adaptative deterministic extrapolation to estimate the plant state, and then, makes his decisions by means of the datas so obtained. An analysis of the phenomenon yields a criterion for optimizing the estimation which is based upon the Hamiltonian estimation of the perfect information game. A class of extrapolators is given by its dynamical equation. Then, the initial game is reduced to a new game containing pure time delay in the state and the controls.     

Optimal estimation of nonlinear state nonlinear observation systems

The concept of this work is that research on nonlinear modeling and estimation in a stochastic framework brings with it the study of the orthogonality structure of the probability densities involved. The connection is made by means of a probabilistic quantity, called the theta function, which under fairly broad integrability conditions defines the class of factorable random processes. These processes play a central role in the derivation of a recursive estimation scheme which is mathematically optimal and computationally attractive. The theory of factorable processes is simpler and its relevance to estimation practice is more direct than that of other sophisticated nonlinear approaches, such as martingales and Lie algebras.The author is indebted to Prof. D. R. Smith, University of California, San Diego, for helpful suggestions.     

A data dependent approach to density estimation

Summary A data dependent approach to density estimation is proposed here. The proposed method requires boundedness and some weak integrability condition on the unknown density, but not any assumption of smoothness. Applications to histogram, kernel, spline and orthogonal series methods are discussed.     

A new infinitesimal approach to robust estimation

Summary The infinitesimal robustness of the asymptotic variance of location M-estimators is investigated by means of the change-of-variance curve (CVC), which bears some resemblance to the influence curve (IC). It is proved that this CVC leads to a more stringent robustness property than the IC and that the Huber estimators are still optimal in this new sense.     

A quantile-copula approach to conditional density estimation

A new kernel-type estimator of the conditional density is proposed. It is based on an efficient quantile transformation of the data. The proposed estimator, which is based on the copula representation, turns out to have a remarkable product form. Its large-sample properties are considered and comparisons in terms of bias and variance are made with competitors based on nonparametric regression. A comparative simulation study is also provided.