Estimation of finite mixtures using the empirical characteristic function

A problem which occurs in analyzing LANDSAT scenes is the problem of separating the components of a finite mixture of several distinct probability distributions. A review of the literature indicates this is a problem which occurs in many disciplines, such as engineering, biology, physiology and economics. Many approaches to this problem have appeared in the literature; however, most are very restrictive in their assumptions or have met with only a limited degree of success when applied to realistic situations. A proceudre is investigated with combines the k-L procedure of (Feurverger and McDunnough, 1981) with the MAICE procedure of (Akaike, 1974). The feasibility of this approach is being investigated numerically via the development of a computer software package enabling a simulation study and comparison with other procedures

UNSUPERVISED CLASSIFICATION AND CHOICE OF CLASSES: BAYESIAN APPROACH

We have given a solution to the problem of unsupervised classifica,tioll of multidinlensional data. Our approach is based on Bayesian estimation which regards the number of classes, the data partition and the parameter vectors that describe the density of classes as unknowns. We compute their MAP estimates simultaneously by maximizing their joint posterior -probability density given the data. The concept of partition as a variable to be estimated hasn\u27t been considered. This formulation also solves the problem of validating clusters obtained from various methods. Our method can also incorporate any additional information &about a class while assigning its prohability density. It can also ut,ilize any available training samples that arise from different classes. We provide a. descent algorithm that starts with an arbitrary partition of the data, and iteratively computes the MAP estimates. We also focus on robust regression which is a special case of unsupervised classification with two classes; inliers and outliers. The problem of intensity image segmentation is posed as an unsupervised classification problem and solved using the Bayesian formulation a multiscale set up. The proposed method is also applied to data sets that occur in statistical literature and target tracking. The results ohbtained demonstrate the power of Bayesian approach for unsupervised classification

Multiclus: A new method for simultaneously performing multidimensional scaling and cluster analysis

This paper develops a maximum likelihood based method for simultaneously performing multidimensional scaling and cluster analysis on two-way dominance or profile data. This MULTICLUS procedure utilizes mixtures of multivariate conditional normal distributions to estimate a joint space of stimulus coordinates and K vectors, one for each cluster or group, in a T -dimensional space. The conditional mixture, maximum likelihood method is introduced together with an E-M algorithm for parameter estimation. A Monte Carlo analysis is presented to investigate the performance of the algorithm as a number of data, parameter, and error factors are experimentally manipulated. Finally, a consumer psychology application is discussed involving consumer expertise/experience with microcomputers.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45747/1/11336_2005_Article_BF02294590.pd

A maximum likelihood method for latent class regression involving a censored dependent variable

The standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45751/1/11336_2005_Article_BF02294647.pd

Potential Alzheimer\u27s Disease Plasma Biomarkers

In this series of studies, we examined the potential of a variety of blood-based plasma biomarkers for the identification of Alzheimer\u27s disease (AD) progression and cognitive decline. With the end goal of studying these biomarkers via mixture modeling, we began with a literature review of the methodology. An examination of the biomarkers with demographics and other health factors found evidence of minimal risk of confounding along the causal pathway from biomarkers to cognitive performance. Further study examined the usefulness of linear combinations of biomarkers, achieved via partial least squares (PLS) analysis, as predictors of various cognitive assessment scores and clinical cognitive diagnosis. The identified biomarker linear combinations were not effective at predicting cognitive outcomes. The final study of our biomarkers utilized mixture modeling through the extension of group-based trajectory modeling (GBTM). We modeled five biomarkers, covering a range of functions within the body, to identify distinct trajectories over time. Final models showed statistically significant differences in baseline risk factors and cognitive assessments between developmental trajectories of the biomarker outcomes. This course of study has added valuable information to the field of plasma biomarker research in relation to Alzheimer’s disease and cognitive decline

Ein Beitrag zur Identifikation von Bewegungszuständen mittels Inertialsensorik für die Stützung von Navigationsfunktionen im Öffentlichen Personenverkehr

Die zuverlässige Ortung von Fahrgästen und Fahrzeugen bildet die Grundlage für Anwendungen im Öffentlichen Personenverkehr (ÖPV) im Rahmen intelligenter Verkehrssysteme. Unter den gegebenen Systembedingungen stoßen funkbasierte Ortungssysteme auf Grund von Abschattungen oder Mehrwegeausbreitungen an ihre Grenzen. Zusätzliche Ortungsinformationen liefern Beschleunigungssensoren. Diese Arbeit entwirft Methoden zur Nutzung dieser Sensorinformationen zur Stützung von Navigationsfunktionen im ÖPV. Ein wesentlicher Gegenstand dieser Arbeit ist der vertiefte Vergleich von theoretisch vorhandenen und praktisch messbaren Fahrzeugsignalen mit den durch die nutzende Person beeinflussten Signalen einer mobilen Sensorplattform (zum Beispiel Smartphone, entsprechend ausgestattetes Nutzermedium). Darauf aufbauend wird ein neues Verfahren zur Schätzung von Bewegungsmodus (d.h. Verkehrsmittelwahl) und -zustand (detaillierte Bewegungsform, z. B. Kurvenfahrt) entwickelt. Dazu wird ein geschichtetes Bewegungszustandsmodell entworfen, welches die verschiedenen in einer ÖPV-Umgebung zu betrachtenden Bewegungszustände und -modi definiert und miteinander verbindet. Dieses Modell ist Grundlage für den in der Arbeit entwickelten und genutzten Algorithmus zur Bewegungszustandserkennung. Anhand von Beispielmessungen von Fahrzeug- (Dresdner Messstraßenbahn, Bus der DVB AG) und Personenbewegungen wird dieses Vorgehen auf seine Anwendbarkeit hin untersucht. Die erstellten Klassifikatoren werden mit dem in dieser Arbeit entwickelten Ansatz wahrscheinlichkeitsbasierter Güteschranken bewertet. Als Teil der Laborumgebung der Professur „Informationstechnik für Verkehrssysteme“ an der TU Dresden zur originalgetreuen Wiederholung von verkehrstelematischen Messfahrten für Sensor- und Softwaretests wird die Reproduktion von Beschleunigungssignalen umgesetzt und diskutiert. Konkrete Beispiele zur Stützung von Navigationsfunktionen im ÖPV auf Basis von Beschleunigungssignalen werden ebenfalls dargestellt

The 1983 NASA/ASEE Summer Faculty Fellowship Research Program research reports

The 1983 NASA/ASEE Summary Faculty Fellowship Research Program was conducted by Texas A&M University and the Lyndon B. Johnson Space Center (JSC). The 10-week program was operated under the auspices of the American Society for Engineering Education (ASEE). The basic objectives of the programs, which began in 1965 at JSC and in 1964 nationally, are (1) to further the professional knowledge of qualified engineering and science faculty members, (2) to stimulate an exchange of ideas between participants and NASA, (3) to enrich and refresh the research and teaching activities of participants' institutions, and (4) to contribute to the research objectives of the NASA Centers. The faculty fellows spent 10 weeks at JSC engaged in a research project commensurate with their interests and background. They worked in collaboration with a NASA/JSC colleague. This document is a compilation of final reports on their research during the summer of 1983