tion Classifica ject Sub s Mathematic 2000 62M10, 62G05. Abstract This paper presents an alternative approach to wavelet smoothing procedure for a time series model of signal added to autocorrelated stationary errors. The aim is to estimate the signal globally with near minimum risk. The usual approach to this problem is to threshold the wavelet coefficients with different thresholds in each le...

The combination of uncertain demand and lead times for installing capacity creates the risk of shortage during the lead time, which may have serious consequences for a service provider with dependent customers. This paper analyzes a model of capacity expansion with autocorrelated random demand and a fixed lead time for adding capacity. To provide a specified level of service, a discrete time ex...

The accurate estimation of covariance matrices is essential for many signal processing and machine learning algorithms. In high dimensional settings the sample covariance is known to perform poorly, hence regularization strategies such as analytic shrinkage of Ledoit/Wolf are applied. In the standard setting, i.i.d. data is assumed, however, in practice, time series typically exhibit strong aut...

The objective of this research is to select the appropriate control charts for detecting a shift in the autocorrelated observations. The autocorrelated processes were characterized using AR (1) and IMA (1, 1) for stationary and non-stationary processes respectively. A process model was simulated to achieve the response, the average run length (ARL). The empirical analysis was conducted to quant...

Accepted 4 April 2008 Copyright © EEF ISSN 1399-1183 Species distributional models based on lattice data often display spatial autocorrelation. Spatial autocorrelation means that observations from nearby locations are often more similar than would be expected on a random basis (Legendre and Legendre 1998). Spatial autocorrelation can arise in both species distributions and environmental variabl...

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...