We first review the concepts fundamental to the statistical inference procedures using nonparametric regression models. The global error properties of an estimator over its parameter space are employed to define a general framework that puts various existing optimality criteria and heuristics...

Gaussian mixture models have been used extensively in model-based clustering. It is well known that the likelihood function for this model is unbounded and the global MLE does not exist. Existing methods confine the parameter estimates in the interior of the parameter space and the MLE is shown...

For hyperspectral data classification, the avoidance of singularity of covariance estimates or excessive near singularity estimation error due to limited training data is a key problem. This study is intended to solve problem via regularized covariance estimators and feature extraction...