For the study of functional organization and reorganization of the human cortex by means of electromagnetic source imaging, changes in the location and spatial extent of neural sources is of interest. We present a probabilistic approach based on the maximum likelihood (ML) theory for modeling extended sources using anatomical and physiological knowledge to reduce the ambiguity of the ill-posed ...

In this paper, we consider estimation of survivor functions from groups of observations with right-censored data when the groups are subject to a stochastic ordering constraint. Many methods and algorithms have been proposed to estimate distribution functions under such restrictions, but none have completely satisfactory properties when the observations are censored. We propose a pointwise cons...

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrik...

A wide variety of processes are thought to show ‘‘long-range persistence’’, specifically an autocorrelation function with power-law decay. A variety of methods have been proposed to quantify this power-law decay, and weather and climate systems, among others, have been claimed to show long-range persistence. In this paper we present a new approach, defining and illustrating a new maximum likeli...

The paper studies large sample asymptotic properties of the Maximum Likelihood Estimator (MLE) for the parameter of a continuous time Markov chain, observed in white noise. Using the method of weak convergence of likelihoods due to I.Ibragimov and R.Khasminskii [14], consistency, asymptotic normality and convergence of moments are established for MLE under certain strong ergodicity conditions o...

For the tree topology, previous studies show the maximum likelihood estimate (MLE) of a link/path takes a polynomial form with a degree that is one less than the number of descendants connected to the link/path. Since then, the main concern is focused on searching for methods to solve the high degree polynomial without using iterative approximation. An explicit estimator based on the Law of Lar...

Statistical inference for discrete time observations of an affine stochastic delay differential equation is considered. The main focus is on maximum pseudolikelihood estimators, which are easy to calculate in practice. Also a more general class of prediction-based estimating functions is investigated. In particular, the optimal prediction-based estimating function and the asymptotic properties ...

The dominant approach in Bernoulli-Gaussian myopic deconvolution consists in the joint maximization of a single Generalized Likelihood with respect to the input signal and the hyperparameters. The aim of this correspondence is to assess the theoretical properties of a related Generalized Marginal Likelihood criterion in a simpliied framework where the lter is reduced to identity. Then the outpu...

In this article we study the simultaneous estimation of the means in Poisson decomposable graphical models. We derive some classes of estimators which improve on the maximum likelihood estimator under the normalized squared losses. Our estimators are based on the argument in Chou[3] and shrink the maximum likelihood estimator depending on the marginal frequencies of variables forming a complete...

Suppose we have speciied a parametric model for the transition distribution of a Markov chain, but that the true transition distribution does not belong to the model. Then the maximum likelihood estimator estimates the parameter which maximizes the Kullback{Leibler information between the true transition distribution and the model. We prove that the maximum likelihood estimator is asymp-totical...