Normal mixture models are widely used for statistical modeling of data, including cluster analysis. However maximum likelihood estimation (MLE) for normal mixtures using the EM algorithm may fail as the result of singularities or degeneracies. To avoid this, we propose replacing the MLE by a maximum a posteriori (MAP) estimator, also found by the EM algorithm. For choosing the number of compone...

This paper considers the maximum likelihood estimation of factor models of high dimension, where the number of variables (N) is comparable with or even greater than the number of observations (T ). An inferential theory is developed. We establish not only consistency but also the rate of convergence and the limiting distributions. Five different sets of identification conditions are considered....

| We formulate the problem of approximating a signal with a sum of chirped Gaussians, the so-called chirplets, under the framework of maximum likelihood estimation. For a signal model of one chirplet in noise, we formulate the maximum likelihood estimator (MLE) and compute the Cram er-Rao lower bound. An approximate MLE is developed, based on time-frequency methods, and is applied sequentially ...

This paper investigates the use of discriminative training techniques for large vocabulary speech recogntion with training datasets up to 265 hours. Techniques for improving lattice-based Maximum Mutual Information Estimation (MMIE) training are described and compared to Frame Discrimination (FD). An objective function which is an interpolation of MMIE and standard Maximum Likelihood Estimation...

In this study, I investigate the necessary condition for the consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters is generally inconsistent when heteroskedasticity is not considered in the estimation. I also show that the MLE of pa...

We present an improved version of random projections that takes advantage of marginal norms. Using a maximum likelihood estimator (MLE), marginconstrained random projections can improve estimation accuracy considerably. Theoretical properties of this estimator are analyzed in detail.

This paper presents a speech-model using the Linear Predictive (LP) residual signal and Maximum Likelihood Estimator (MLE). With this model an accuracy of the reverberation time estimation can be improved. During past decade, the reverberation time estimation was performed using only maximum likelihood detector, which resulted in excess time of estimation. For the purpose of estimating room aco...

General methods for obtaining maximum likelihood estimates in exponential families are demonstrated using a constrained autologistic model for estimating relatedness from DNA fingerprint data. The novel features are the use of constrained optimization and two new algorithms for maximum likelihood estimation. The first, the "phase I" algorithm determines the support of the MLE in the closure of ...

The Quantization Theorem I (QT I) implies that the likelihood function can be reconstructed from quantized sensor observations, given that appropriate dithering noise is added before quantization. We present constructive algorithms to generate such dithering noise. The application to maximum likelihood estimation (mle) is studied in particular. In short, dithering has the same role for amplitud...

3 Sparse Representation and DOA estimation 7 3.1 Sparse Representation and Compressed Sensing . . . . . . . . . . . . . . . . . . . . . . . . 7 3.1.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.1.2 Convex Relaxation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.1.3 `q Optimization . . . . . . . . . . . . . . . . . . . ....