Reliable and Computationally Efficient Maximum-Likelihood Estimation of “Proper” Binormal ROC Curves

Computationally efficient maximum-likelihood estimation of structured covariance matrices

A computationally e cient method for structured covariance matrix estimation is presented. The proposed method provides an Asymptotic (for large samples) Maximum Likelihood estimate of a structured covariance matrix and is referred to as AML. A closed-form formula for estimating Hermitian Toeplitz covariance matrices is derived which makes AML computationally much simpler than most existing Her...

A Semi-parametric Approach to Estimation of ROC Curves for Multivariate Binormal Mixtures

A Receiver Operating Characteristic (ROC) curve reflects the performance of a system which decides between two competing actions in a test of statistical hypothesis. This paper addresses the inference on ROC curves for the following problem: how can one statistically validate the performance of a system with a claimed ROC curve, ROC0 say? Our proposed solution consists of two main components: F...

Epsilon-skew-binormal receiver operating characteristic (ROC) curves

In this note we extend the well-known binormal model via implementation of the epsilon-skew-normal (ESN) distribution developed by Mudholkar and Hutson (2000). We derive the equation for the receiver operating characteristic (ROC) curve assuming epsilon-skew-binormal (ESBN) model and examine the behavior of the maximum likelihood estimates for estimating the ESBN parameters. We then summarize t...

Computationally Efficient Gaussian Maximum Likelihood Methods for Vector ARFIMA Models

In this paper, we discuss two distinct multivariate time series models that extend the univariate ARFIMA model. We describe algorithms for computing the covariances of each model, for computing the quadratic form and approximating the determinant for maximum likelihood estimation, and for simulating from each model. We compare the speed and accuracy of each algorithm to existing methods and mea...

Maximum Likelihood Estimation of Multiple Pronunciations for Proper Nouns