This paper presents a statistical approach to aggregating speed and phase (directional) information for vascular segmentation in phase contrast magnetic resonance angiograms (PC-MRA), and proposes a Maxwell-Gaussian finite mixture distribution to model the background noise distribution. In this paper, we extend our previous work [6] to the segmentation of phase-difference PC-MRA speed images. W...

Recent space plasma observations have provided us with three-dimensional velocity distributions having multiple peaks. We propose a method for analyzing such velocity distributions via a multivariate Maxwellian mixture model where each component of the model represents each of the multiple peaks. The parameters of the model are determined through the Expectation-Maximization (EM) algorithm. For...

In reproductive health studies, epidemiologists are often interested in examining the effects of covariates on menstrual cycle length which is a convenient, noninvasive measure of women's ovarian and reproductive function. Previous literature (Harlow and Zeger, 1991) suggests that the distribution of cycle length is a mixture of a major symmetric distribution and a component featuring a long ri...

In this paper, we consider the problem of learning a two dimensional spatial model of a gas distribution with a mobile robot. Building maps that can be used to accurately predict the gas concentration at query locations is a challenging task due to the chaotic nature of gas dispersal. We present an approach that formulates this task as a regression problem. To deal with the specific properties ...

We consider nonparametric estimation of disease onset distribution functions in multiple populations using censored data with unknown population identifiers. The problem is motivated from studies aiming at estimating the disease risk distribution in deleterious mutation carriers for the purpose of genetic counseling and design of therapeutic intervention trials to modify disease onset progressi...

Robust stochastic modeling of speech is an important issue for the performance of practical applications. The Gaussian mixture model, GMM, is widely used in speaker ID, but its performance would get limited in the presence of unseen noise and distortions. Such noisy data, referred to as ”outliers” for the original distribution, can be better represented by the use of heavy-tail distributions, s...

Disease mapping, a method for displaying the geographical distribution of disease occurrence, has received attention for more than 2 decades. Because traditional approaches to disease mapping have some deficiencies and disadvantages in presenting the geographical distribution of disease, the mixture model--as an alternative approach--overcomes some of these deficiencies and provides a clearer p...

Circular data are commonly encountered in the earth sciences and statistical descriptions and inferences about such data are necessary in structural geology. In this paper we compare two statistical distributions appropriate for complex circular data sets: the mixture of von Mises and the projected normal distribution. We show how the number of components in a mixture of von Mises distribution ...

This paper introduces a mixture distribution approach to modeling the probability density function for lead time demand (LTD) in problems where a continuous review inventory system is implemented. The method differs from the typical “moment-matching” approach by focusing on building up an accurate, closed-form approximation to the LTD distribution from its components by using mixtures of polyno...

This article deals with mixture model based color image segmentation in the LCH color space. In this space, one of the components (representing hue in particular) is circular in nature. Hence LCH image pixels are samples on a cylinder. A statistical model for such data needs to employ circular-linear joint distributions. Here such a model is designed using the "Independent von-Mises Gaussi...