Nonlinear filtering and optimal quality control.
Nonlinear filtering for LIDAR signal processing.
Nonlinear filtering for observations on a random vector field along a random path. Application to atmospheric turbulent velocities
To filter perturbed local measurements on a random medium, a dynamic model jointly with an observation transfer equation are needed. Some media given by PDE could have a local probabilistic representation by a Lagrangian stochastic process with mean-field interactions. In this case, we define the acquisition process of locally homogeneous medium along a random path by a Lagrangian Markov process conditioned to be in a domain following the path and conditioned to the observations. The nonlinear...
Nonlinear filtering for reflecting diffusions in random environments via nonparametric estimation.
Nonlinear filtering of oscillatory measurements in cardiovascular applications.
Nonlinear Regression and Curved Exponential Families. Improvement of the Approximation to the Asymptotic Distribution.
Non-negative linear processes
Conditions under which the linear process is non-negative are investigated in the paper. In the definition of the linear process a strict white noise is used. Explicit results are presented also for the models AR(1) and AR(2).
Nonnegative multivariate processes
Nonparametric adaptive estimation for pure jump Lévy processes
This paper is concerned with nonparametric estimation of the Lévy density of a pure jump Lévy process. The sample path is observed at n discrete instants with fixed sampling interval. We construct a collection of estimators obtained by deconvolution methods and deduced from appropriate estimators of the characteristic function and its first derivative. We obtain a bound for the -risk, under general assumptions on the model. Then we propose a penalty function that allows to build an adaptive estimator....
Nonparametric analysis of blocked ordered categories data: Some examples revisited.
Non-parametric approximation of non-anticipativity constraints in scenario-based multistage stochastic programming
We propose two methods to solve multistage stochastic programs when only a (large) finite set of scenarios is available. The usual scenario tree construction to represent non-anticipativity constraints is replaced by alternative discretization schemes coming from non-parametric estimation ideas. In the first method, a penalty term is added to the objective so as to enforce the closeness between decision variables and the Nadaraya–Watson estimation of their conditional expectation. A numerical application...
Nonparametric bivariate estimation for successive survival times.
Several aspects of the analysis of two successive survival times are considered. All the analyses take into account the dependent censoring on the second time induced by the first. Three nonparametric methods are described, implemented and applied to the data coming from a multicentre clinical trial for HIV-infected patients. Visser's and Wang and Wells methods propose an estimator for the bivariate survival function while Gómez and Serrat's method presents a conditional approach for the second...
Nonparametric Curve Estimation with Bernstein Estimates.
Nonparametric density estimators based on nonstationary absolutely regular random sequences.
Nonparametric Estimation of Distribution Functions.
Nonparametric estimation of probability density functions based on orthogonal expansions.
Nonparametric estimation of the density of the alternative hypothesis in a multiple testing setup. Application to local false discovery rate estimation
In a multiple testing context, we consider a semiparametric mixture model with two components where one component is known and corresponds to the distribution of p-values under the null hypothesis and the other component f is nonparametric and stands for the distribution under the alternative hypothesis. Motivated by the issue of local false discovery rate estimation, we focus here on the estimation of the nonparametric unknown component f in the mixture, relying on a preliminary estimator of the...
Nonparametric estimation of the derivatives of the stationary density for stationary processes
In this article, our aim is to estimate the successive derivatives of the stationary density f of a strictly stationary and β-mixing process (Xt)t≥0. This process is observed at discrete times t = 0,Δ,...,nΔ. The sampling interval Δ can be fixed or small. We use a penalized least-square approach to compute adaptive estimators. If the derivative f(j)belongs to the Besov space B 2 , ∞ α , then our estimator converges at rate (nΔ)−α/(2α+2j+1). Then we consider a diffusion with known diffusion coefficient....
Nonparametric estimation of the jump rate for non-homogeneous marked renewal processes
This paper is devoted to the nonparametric estimation of the jump rate and the cumulative rate for a general class of non-homogeneous marked renewal processes, defined on a separable metric space. In our framework, the estimation needs only one observation of the process within a long time. Our approach is based on a generalization of the multiplicative intensity model, introduced by Aalen in the seventies. We provide consistent estimators of these two functions, under some assumptions related to...
Nonparametric estimation of the ratios of derivatives of a multivariate distribution density from dependent observations.