New Zernike velocity moments have been developed to describe an object, not only by its shape , but also by its motion throughout an image sequence. These are an extended form of the orthogonal Zernike moment set and include velocity information introduced via centralised moments. Initial analysis shows that they perform well when applied to analysing gait sequences resulting in a good recognit...

In a first part is recalled a simulation method for a strictly stationary non-Gaussian process given its one-dimensional marginal distribution (or its -first statistical moments) and its autocorrelation function. This method was already widely treated in the articles [14] and [13]. The objective of the present paper is twofold : firstly simplify this method when by Mehler formula it is possible...

The Hierarchical Dirichlet Process (HDP) is a versatile, albeit computationally expensive tool for statistical modeling of mixture models. In this paper, we introduce a spectral algorithm. We show that it is both computationally and statistically efficient. In particular, we derive the lower-order moments of the HDP and give reconstruction guarantees. Moreover, we show that hierarchical spectra...

This article investigates the super-resolution phenomenon using the celebrated statistical estimator LASSO in the complex valued measure framework. More precisely, we study the recovery of a discrete measure (spike train) from few noisy observations (Fourier samples, moments, Stieltjes transformation...). In particular, we provide an explicit quantitative localization of the spikes. Moreover, o...

This work develops a perturbation approach to the estimation and propagation of states in nonlinear dynamical systems with uncertainties in both the process model and output measurements. The state estimation error and statistical moments are propagated in time through a regular perturbation method in an artificial ordering parameter λ, while the state updating occurs through a linear update eq...

BACKGROUND Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physici...

Method of moment estimators exhibit appealing statistical properties, such as asymptotic unbiasedness, for nonconvex problems. However, they typically require a large number of samples and are extremely sensitive to model misspecification. In this paper, we apply the framework of M-estimation to develop both a generalized method of moments procedure and a principled method for regularization. O...

This paper proposes the use of statistical criteria for early stopping Support Vector Machines, both for regression and classification problems. The method basically stops the minimization of the primal functional when moments of the error signal (up to forth order) become stationary, rather than according to a tolerance threshold of primal convergence itself. This simple strategy induces lower...

To quantify heavy-tailed distributions observed in financial time series, we propose a general method to characterize symmetric non-Gaussian distributions. In our approach, an observed time series is assumed to be described by the multiplication of Gaussian and amplitude random variables, where the amplitude variable describes fluctuations of the standard deviation. Based on this framework, it ...

This paper shows how to estimate models by the generalized method of moments and the generalized empirical likelihood using the R package gmm. A brief discussion is offered on the theoretical aspects of both methods and the functionality of the package is presented through several examples in economics and finance. It is a modified version of Chaussé (2010) published in the Journal of Statistic...