The performance of the majority of high resolution algorithms designed for either spectral analysis or Direction-of-Arrival (DoA) estimation drastically degrades when the amplitude sources are highly correlated or when the number of available snapshots is very small and possibly less than the number of sources. Under such circumstances, only Maximum Likelihood (ML) or ML-based techniques can st...

We study the convex optimization approach for parameter estimation of several sinusoidal models, namely, single complex/real tone, multiple complex sinusoids, and single two-dimensional complex tone, in the presence of additive Gaussian noise. The major difficulty for optimally determining the parameters is that the corresponding maximum likelihood (ML) estimators involve finding the global min...

The parameters of the prior, the hyperparameters, play an important role in Bayesian image estimation. Of particular importance for the case of Gibbs priors is the global hyperparameter, beta, which multiplies the Hamiltonian. Here we consider maximum likelihood (ML) estimation of beta from incomplete data, i.e., problems in which the image, which is drawn from a Gibbs prior, is observed indire...

A novel type of approximation scheme to the maximum likelihood (ML) approach is presented and discussed in the context of phylogenetic tree reconstruction from aligned DNA sequences. It is based on a parameterized approximation to the conditional distribution of hidden variables (related, e.g., to the sequences of unobserved branch point ancestors) given the observed data. A modified likelihood...

A novel approximate maximum likelihood algorithm is proposed for estimating the time difference of arrival between signals received at two spatially separated sensors. Prior to cross correlation, one of the channel outputs is optimally weighted at different frequency bands with the use of an orthogonal wavelet transform. It composes an array of multirate filters and is a time-domain implementat...

In many life-testing and reliability studies, the experimenter might not always obtain complete information on failure times for all experimental units. Multiply Type-II censored sampling arises in a life-testing experiment whenever the experimenter does not observe the failure times of some units placed on a life-test. In this paper, we obtain estimators for the entropy function of a double ex...

We present a quasi-maximum likelihood approach to blind source separation (BSS) which is based on approximating the source distributions by their truncated Edgeworth expansions. The paper focuses on the 22 case, for which the problem is known to reduce to the estimation of a single rotation angle. Unlike existing maximum likelihood BSS techniques , the proposed algorithm is consistent for any s...

Blind equalization of single input multiple output (SIMO) FIR channels can be reformulated as the problem of blind source separation. It was shown in [4] that the natural gradient-based source separation method could recover source successfully for ill-conditioned channels since it has the equivariant property. However, the e ect of additive noise was not considered in [4]. In this letter we de...

The transition density of a diffusion process does not admit an explicit expression in general, which prevents the full maximum likelihood estimation (MLE) based on discretely observed sample paths. Aı̈t-Sahalia (1999, 2002) proposed asymptotic expansions to the transition densities of diffusion processes, which lead to an approximate maximum likelihood estimation (AMLE) for parameters. Built on...