Maximum Likelihood vs. Minimum Distance

Maximum likelihood vs. minimum distance: searching for hills in the plain.

of deleterious spontaneous mutation from simulated tances have a random component. To deal with this commutation accumulation data, Garcı́a-Dorado and Galponent objectively, after a minimum was identified for lego (2003) found that ML estimates of the deleterious the distance, we used regression to test the significance mutation rate (U) had higher mean square errors (MSE) of the increase in dis...

Maximum Likelihood and Minimum Classi cation

Hidden Markov models (HMMs) for automatic speech recognition rely on high dimensional feature vectors to summarize the short-time properties of speech. Correlations between features can arise when the speech signal is non-stationary or corrupted by noise. We investigate how to model these correlations using factor analysis, a statistical method for dimensionality reduction. Factor analysis uses...

Maximum-Likelihood Estimate for Distance Matrix

The structure of relationship among subjects is an interesting topic in many social studies, especially phylogeny. The between-subject relationships are commonly measured by similarity or dissimilarity metrics and represented using a distance matrix. Based on this distance matrix, a tree can be constructed to characterize the overall structure of inferred relationships among all subjects. Curre...

Likelihood-based Vs Distance-based Evidential Classifiers

Nonparametric Maximum Likelihood Density Estimation and Simulation-Based Minimum Distance Estimators

Indirect inference estimators (i.e., simulation-based minimum distance estimators) in a parametric model that are based on auxiliary nonparametric maximum likelihood density estimators are shown to be asymptotically normal. If the parametric model is correctly specified, it is furthermore shown that the asymptotic variance-covariance matrix equals the inverse of the Fisher-information matrix. T...