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Wavelet-variance-based estimation for composite stochastic processes

Publications associées (56)

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An algorithm for automatic inertial sensor calibration

Jan Skaloud, Stéphane Guerrier

We present an algorithm for determining the nature of stochastic processes together with its parameters based on the analysis of time series of inertial errors. The algorithm is suitable mainly (but not only) for situations when several stochastic processe ...

2013

MMSE Denoising of Sparse Lévy Processes via Message Passing

Michaël Unser, Azin Amini, Ulugbek Kamilov

Many recent algorithms for sparse signal recovery can be interpreted as maximum-a-posteriori (MAP) estimators relying on some specific priors. From this Bayesian perspective, state-of-the-art methods based on discrete-gradient regularizers, such as total- ...

IEEE2012

Mmse Denoising Of Sparse Levy Processes Via Message Passing

Michaël Unser, Arash Amini, Ulugbek Kamilov

Ieee2012

Advantages of Variance Stabilization

Stephan Morgenthaler, Robert Staudte

Variance stabilization is a simple device for normalizing a statistic. Even though its large sample properties are similar to those of studentizing, many simulation studies of confidence interval procedures show that variance stabilization works better for ...

Wiley-Blackwell2012

Robust parameter estimation for the Ornstein-Uhlenbeck process

Sonja Rieder

In this paper, we derive elementary M- and optimally robust asymptotic linear (AL)-estimates for the parameters of an Ornstein-Uhlenbeck process. Simulation and estimation of the process are already well-studied, see Iacus (Simulation and inference for sto ...

Springer-Verlag2012

Robust Parameter Estimation for the Ornstein-Uhlenbeck Process

Sonja Rieder

In this thesis, we treat robust estimation for the parameters of the Ornstein–Uhlenbeck process, which are the mean, the variance, and the friction. We start by considering classical maximum likelihood estimation. For the simulation study, where we also in ...

EPFL2012

Bivariate Extreme Statistics, Ii

We review the current state of statistical modeling of asymptotically independent data. Our discussion includes necessary and sufficient conditions for asymptotic independence, results on the asymptotic independence of statistics of interest, estimation an ...

2012

A Sampling Theory Approach for Continuous ARMA Identification

Michaël Unser, Hagay Kirshner, Simona Maggio

The problem of estimating continuous-domain autoregressive moving-average processes from sampled data is considered. The proposed approach incorporates the sampling process into the problem formulation while introducing exponential models for both the cont ...

IEEE2011

Maximum-Likelihood Identification of Sampled Gaussian Processes

Michaël Unser, Hagay Kirshner, Simona Maggio

This work considers sampled data of continuous-domain Gaussian processes. We derive a maximum-likelihood estimator for identifying autoregressive moving average parameters while incorporating the sampling process into the problem formulation. The proposed ...

SampTA2011

Stratégies de compromis pour l'estimation des paramètres de régression et pour la classification floue

Nicolas Fournier

Model specification is an integral part of any statistical inference problem. Several model selection techniques have been developed in order to determine which model is the best one among a list of possible candidates. Another way to deal with this questi ...

EPFL2011