[PDF] Top 20 Maximum Likelihood Estimation and Coarse Data
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Maximum Likelihood Estimation and Coarse Data
... adapt maximum likelihood estimation (MLE) to this case [ 5 , 6 , 11 , 13 , 15 , 17 , 20 ...the maximum likelihood procedure results in a consistent estimator of the parameter under some ... Voir le document complet
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Maximum Likelihood Estimation and Coarse Data
... adapt maximum likelihood estimation (MLE) to this case [5,6,11, 13, 15,17, ...the maximum likelihood procedure results in a consistent estimator of the parameter under some regularity ... Voir le document complet
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A Maximum Likelihood Approach to Inference Under Coarse Data Based on Minimax Regret
... express and solve maximum likelihood problems with incomplete ...the likelihood func-tion ...maximal likelihood induced by precise datasets compat-ible with the incomplete observations, ... Voir le document complet
9
Maximum Likelihood with Coarse Data based on Robust Optimisation
... probability estimation in the context of coarse ...the maximum likelihood ...one, and that the uncertainty that pervades its observation is epistemic, rather than representing ...the ... Voir le document complet
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Contributions to probabilistic non-negative matrix factorization - Maximum marginal likelihood estimation and Markovian temporal models
... as maximum marginal likelihood estimation ...the maximum marginal likelihood ...W and θ H in our setting) by maximizing the marginal likelihood ...algorithm), and ... Voir le document complet
164
Bounds for maximum likelihood regular and non-regular DoA estimation in K-distributed noise
... priori, maximum likelihood estimators and Cramér-Rao bounds are changing in a straightforward way with whitening ...priori and information about this matrix is substi- tuted by a number of ... Voir le document complet
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A Maximum Likelihood Approach to Inference Under Coarse Data Based on Minimax Regret
... express and solve maximum likelihood problems with incomplete ...the likelihood func-tion ...maximal likelihood induced by precise datasets compat-ible with the incomplete observations, ... Voir le document complet
8
Maximum likelihood covariance matrix estimation from two possibly mismatched data sets
... two data sets, one whose covariance matrix R 1 is the sought one and another set of samples whose covariance matrix R 2 slightly differs from the sought one, due ...two data sets with different ... Voir le document complet
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Maximum Likelihood with Coarse Data based on Robust Optimisation
... for maximum likelihood estimation under coarse data due to Couso and Dubois ( 2016a ), and situate our robust op- timization strategy in this ...minimal likelihood ... Voir le document complet
13
On the Cramer Rao bound and maximum likelihood in passive time delay estimation for complex signals
... delay estimation for wide sense stationary complex circular or noncircular Gaussian ...plex data, closed-form expressions of the Cramer Rao bound (CRB) are given for the time delay alone in presence of nui- ... Voir le document complet
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Maximum likelihood estimation of long-term HIV dynamic models and antiviral response
... Approximation Estimation Maximisation (SAEM) algorithm, a stochastic version developed by of the Expectation-Maximization algorithm introduced by ...observed data in the posterior distribution at each ... Voir le document complet
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Maximum-Likelihood Parameter Estimation of the Product Model for Multilook Polarimetric SAR Data
... estimator and the frac- tional moment-based estimator obtained from each available polarization before averaging the ...estimator and a log-determinant moment-based estimator for the ENL estimation ... Voir le document complet
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Cross Validation and Maximum Likelihood estimation of hyper-parameters of Gaussian processes with model misspecification
... the estimation boils down to estimating the corresponding parameters, that are called ...microergodic and non-microergodic ...consistent estimation of microergodic hyper-parameters, par- ticularly ... Voir le document complet
31
Fast simulated annealing in $\R^d$ and an application to maximum likelihood estimation
... ing to τ = 1/10 in Corollary 3.2, and let number of particles at step n be N n = n ∨ 20, a function which is affine for n ≥ 20 (Theorem 4.1). The algorithm was run for 5,000 iterations in each of 150 independent ... Voir le document complet
29
Second order pseudo-maximum likelihood estimation and conditional variance misspecification
... quadratic exponential if every element of the family has a p.d.f. which may be written as l(Y, m, Σ) = exp (A(m, Σ) + B(Y ) + C(m, Σ) Y + Y D(m, Σ)Y ) where A(m, Σ) and B(Y ) are scalar, C(m, Σ) is a G × 1 ... Voir le document complet
13
Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
... The paper falls into the following parts. In Section 2 we characterize the equivalence of Gaussian measures, and describe which covariance parameters are microergodic. In Section 3 we establish the strong ... Voir le document complet
31
Minimum divergence estimators, Maximum Likelihood and the generalized bootstrap
... Section 3 states that given a divergence pseudo distance φ in CR the Mini- mum Divergence Estimator (MDE) is obtained as a proxy of the minimizer of the large deviation limit for some bootstrap version of the empirical ... Voir le document complet
23
Approximate maximum likelihood direction of arrival estimation for two closely spaced sources
... MLE and AMLE have a similar behavior and depart from the CRB about 2-3 dB after ESPRIT, and the latter achieves a 2-3 dB gain compared to ...sources, and performs better than ESPRIT and ... Voir le document complet
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Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
... Our results can be extended in different directions. The most natural exten- sion, is to consider a general number k of Gaussian processes Z 1 , ..., Z k , which covariance structure is of the form Cov(Zi(s 1 ), Zj (s 2 ... Voir le document complet
31
Weighted maximum likelihood autoregressive and moving average spectrum modeling
... (MA), and ARMA models in the spectral ...a maximum likelihood approach, where spectral weights are introduced in order to selectively enhance the accuracy on a predefined set of frequencies, while ... Voir le document complet
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