The Experts below are selected from a list of 43809 Experts worldwide ranked by ideXlab platform
Tobias Ryden - One of the best experts on this subject based on the ideXlab platform.
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asymptotic properties of the maximum Likelihood Estimator in autoregressive models with markov regime
Annals of Statistics, 2004Co-Authors: Randal Douc, Eric Moulines, Tobias RydenAbstract:An autoregressive process with Markov regime is an autoregressive process for which the regression function at each time point is given by a nonobservable Markov chain. In this paper we consider the asymptotic properties of the maximum Likelihood Estimator in a possibly nonstationary process of this kind for which the hidden state space is compact but not necessarily finite. Consistency and asymptotic normality are shown to follow from uniform exponential forgetting of the initial distribution for the hidden Markov chain conditional on the observations.
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asymptotic normality of the maximum Likelihood Estimator for general hidden markov models
Annals of Statistics, 1998Co-Authors: Peter J Bickel, Yaacov Ritov, Tobias RydenAbstract:Hidden Markov models (HMMs) have during the last decade become a widespread tool for modeling sequences of dependent random variables. Inference for such models is usually based on the maximum-Likelihood Estimator (MLE), and consistency of the MLE for general HMMs was recently proved by Leroux. In this paper we show that under mild conditions the MLE is also asymptotically normal and prove that the observed information matrix is a consistent Estimator of the Fisher information.
Hatem Hmam - One of the best experts on this subject based on the ideXlab platform.
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Profile Likelihood Estimator for Passive Scan-Based Emitter Localization
2007 IEEE International Conference on Acoustics Speech and Signal Processing - ICASSP '07, 2007Co-Authors: Kutluyll Dogancay, Hatem HmamAbstract:This paper is concerned with the geolocation of a scanning emitter from time of intercept measurements of the rotating emitter beam. The problem of estimating the emitter location is cast into a profile Likelihood estimation framework by treating the unknown scan rate of the emitter as a nuisance parameter. A grid search technique is developed for initializing iterative profile Likelihood estimation algorithms. The grid spacing is determined from an estimate of the Lipschitz constant of the profile Likelihood cost function. Maxima of directional derivatives of the cost function are fitted to a Weibull distribution to estimate the Lipschitz constant. The performance of the profile Likelihood Estimator is illustrated with simulation examples.
Kutluyll Dogancay - One of the best experts on this subject based on the ideXlab platform.
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Profile Likelihood Estimator for Passive Scan-Based Emitter Localization
2007 IEEE International Conference on Acoustics Speech and Signal Processing - ICASSP '07, 2007Co-Authors: Kutluyll Dogancay, Hatem HmamAbstract:This paper is concerned with the geolocation of a scanning emitter from time of intercept measurements of the rotating emitter beam. The problem of estimating the emitter location is cast into a profile Likelihood estimation framework by treating the unknown scan rate of the emitter as a nuisance parameter. A grid search technique is developed for initializing iterative profile Likelihood estimation algorithms. The grid spacing is determined from an estimate of the Lipschitz constant of the profile Likelihood cost function. Maxima of directional derivatives of the cost function are fitted to a Weibull distribution to estimate the Lipschitz constant. The performance of the profile Likelihood Estimator is illustrated with simulation examples.
C.g. Jauffret - One of the best experts on this subject based on the ideXlab platform.
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Maximum Likelihood Estimator for magneto-acoustic localisation
1997 IEEE International Conference on Acoustics Speech and Signal Processing, 1997Co-Authors: G. Dassot, R. Blanpain, C.g. JauffretAbstract:This paper is devoted to the localization of magneto-acoustic sources moving in a straight line at a constant speed. Our technique is based on the association of narrow band acoustic signals and magnetostatic measurements. First of all, we describe features that make possible the association of magnetic and acoustic data, secondly, we show that the positioning accuracy is much improved by this association. We focus on solving the problem with as few sensors as possible. A geometric discussion of identifiability is proposed, as well as a batch maximum Likelihood Estimator whose covariance matrix asymptotically achieves Cramer Rao lower bounds (CRLB).
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Linear maximum Likelihood Estimator
[Proceedings] ICASSP 91: 1991 International Conference on Acoustics Speech and Signal Processing, 1991Co-Authors: C.j. Musso, C.g. JauffretAbstract:A general linear and quasi-efficient Estimator is presented which is an optimal (for a given criterion) approximation of the maximum Likelihood Estimator (MLE with nonlinear measurement equation) when the measurements are corrupted by a Gaussian noise. This approach consists of choosing a particular state vector which characterizes the signal. The model is defined by special values of the signal at sample times which are the roots of an orthogonal Lagrange polynomial. It is rigorously established that the linear Estimator is quasi-unbiased and has a covariance matrix which is close to the Cramer-Rao lower bound. A practical algorithm is derived, and it is shown to be very easy to implement. This method is successfully applied to the problem of target motion analysis (TMA).
M Vallisneri - One of the best experts on this subject based on the ideXlab platform.
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beyond the fisher matrix formalism exact sampling distributions of the maximum Likelihood Estimator in gravitational wave parameter estimation
Physical Review Letters, 2011Co-Authors: M VallisneriAbstract:: Gravitational-wave astronomers often wish to characterize the expected parameter-estimation accuracy of future observations. The Fisher matrix provides a lower bound on the spread of the maximum-Likelihood Estimator across noise realizations, as well as the leading-order width of the posterior probability, but it is limited to high signal strengths often not realized in practice. By contrast, Monte Carlo Bayesian inference provides the full posterior for any signal strength, but it is too expensive to repeat for a representative set of noises. Here I describe an efficient semianalytical technique to map the exact sampling distribution of the maximum-Likelihood Estimator across noise realizations, for any signal strength. This technique can be applied to any estimation problem for signals in additive Gaussian noise.
