Kernel methods for deconvolution have attractive features, and prevail in the literature. However, they have disadvantages, which include the fact that they are usually suitable only for cases where the error distribution is infinitely supported and its characteristic function does not ever vanish. Even in these settings, optimal convergence rates are achieved by kernel estimators only when the...

Abstract. We consider the regression model with errors-in-variables where we observe n i.i.d. copies of (Y, Z) satisfying Y = f(X) + ξ, Z = X + σε, involving independent and unobserved random variablesX, ξ, ε. The density g ofX is unknown, whereas the density of σε is completely known. Using the observations (Yi, Zi), i = 1, · · · , n, we propose an estimator of the regression function f , buil...

We have developed a new nonlinear filter that is superior to particle filters in five ways: (1) it exploits smoothness; (2) it uses an exact solution of the Fokker-Planck equation in continuous time; (3) it uses a convolution to compute the effect of process noise at discrete times; (4) it uses the adjoint method to compute the optimal density of points in state space to represent the smooth co...

We propose a novel tracking algorithm based on the WangLandau Monte Carlo sampling method which efficiently deals with the abrupt motions. Abrupt motions could cause conventional tracking methods to fail since they violate the motion smoothness constraint. To address this problem, we introduce the Wang-Landau algorithm that has been recently proposed in statistical physics, and integrate this a...

This paper develops approximation techniques for directional densities by nite dimensional exponential densities. The methodology is to use expansions with respect to classical spherical harmonics followed by estimating the unknown parameters by maximum likelihood. The advantage of this approach is that one estimates directional probability densities by probability densities while at the same t...

This article is focused on the impact of different binder mixtures on the performances of paper surface and printability. The coating properties were studied with the chosen silica pigment, and the binder consisted of vinyl acetate copolymer and polyvinyl alcohol with different ratios, followed by the measurement of smoothness, whiteness, and surface strength. The inkjet image quality was asses...

Kernel methods for deconvolution have attractive features, and prevail in the literature. However, they have disadvantages, which include the fact that they are usually suitable only for cases where the error distribution is infinitely supported and its characteristic function does not ever vanish. Even in these settings, optimal convergence rates are achieved by kernel estimators only when the...

This paper examines asymptotic equivalence in the sense of Le Cam between density estimation experiments and the accompanying Poisson experiments. The significance of asymptotic equivalence is that all asymptotically optimal statistical procedures can be carried over from one experiment to the other. The equivalence given here is established under a weak assumption on the parameter space F . In...

We propose a classical (nonBayesian) Laplace estimator alternative for a large class of 3 p n–consistent estimators, including isotonic density and regression estimators, inverse density and regression estimators, the maximum score and mode regression estimators, and interval censoring and monotone hazard rate estimators. The proposed alternative provides a unified method of smoothing that appl...