RNA viruses offer a very exciting arena in which to study evolution in 'real time' owing to both their high replication rate-many generations per day are possible-and their high mutation rate, leading to a large phenotypic variety. They can be regarded as a swarm of genetically related mutants around a dominant or master genetic sequence. This system is called a 'viral quasi-species'. Thus, a c...

The autologistic model and related auto-models, commonly applied as autocovariate regression, offer distinct advantages for analysing spatially autocorrelated species distribution or abundance data, allowing simple and direct modelling of the dependence of nearby observations. However, Carl and Kühn (Ecological Modelling, 2007, 207, 159) and Dormann (Ecological Modelling, 2007, 207, 234) questi...

Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distanc...

INTEGRATION OF DATA MJNING ALGORITHMS AND CONTROL' CBARI'S FOR MULTIVARIATE AND AUTOCORRELATED PROCESSES WEERAWAT JITPITAKLERT, Ph.D. ,The University of Texas at Arlington, 2009 Supervising Professor: Seoung Bum Kim The objective of tllli3 dissertation is to integrate state-of-the-art data mining 3lgoritbms with statistical process control (SPC) tools to a.chieve efficient 'monitoring in multiv...

This paper proposes a new spatial lag regression model which addresses global autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the correlation parameter as measurement impact neighbouring observations on conditional expectation It allows for flexible likelihood-based inference based different distributional assumptions us...

In this correspondence, we will point out a problem with testing adaptive classifiers on autocorrelated data. In such a case random change alarms may boost the accuracy figures. Hence, we cannot be sure if the adaptation is working well.

Many problems do not have one or more variables that determine quality characteristics. In these situations, as a solution method, profile is descibed by linking independent to the response variable. One of common assumptions in most monitoring schemes assumption residuals. Contravention this can lead misleading results control chart. On other hand, when data are contaminated, classical methods...