Some control charts based on machine learning approaches have been developed recently in the stat... more Some control charts based on machine learning approaches have been developed recently in the statistical process control (SPC) literature. These charts are usually designed for monitoring processes with independent observations at different observation times. In practice, however, serial data correlation almost always exists in the observed data of a temporal process. It has been well demonstrated in the SPC literature that control charts designed for monitoring independent data would not be reliable to use in applications with serially correlated data. In this chapter, we suggest using certain existing machine learning control charts together with a recursive data de-correlation procedure. It is shown that the performance of these charts can be substantially improved for monitoring serially correlated processes after data de-correlation. Xiulin Xie Department of Biostatistics, University of Florida, 2004 Mowry Road, Gainesville, FL 32610. e-mail: xiulin.xie@ufl.edu Peihua Qiu Depar...
Statistical process control (SPC) charts are widely used in manufacturing industries for quality ... more Statistical process control (SPC) charts are widely used in manufacturing industries for quality control and management. They are used in more and more other applications, such as internet traffic monitoring, disease surveillance, and environmental protection. Traditional SPC charts designed for monitoring production lines in manufacturing industries are based on the assumptions that observed data are independent and identically distributed with a parametric in-control distribution. These assumptions, however, are rarely valid in practice. Therefore, recent SPC research focuses mainly on development of new control charts that are appropriate to use without these assumptions. In this article, we briefly introduce some recent studies on nonparametric SPC, control charts for monitoring dynamic processes, and spatio-temporal process monitoring. Control charts developed in these directions have found broad applications in practice.
In many clinical studies, evaluating the association between longitudinal and survival outcomes i... more In many clinical studies, evaluating the association between longitudinal and survival outcomes is of primary concern. For analyzing data from such studies, joint modeling of longitudinal and survival data becomes an appealing approach. In some applications, there are multiple longitudinal outcomes whose longitudinal pattern is difficult to describe by a parametric form. For such applications, existing research on joint modeling is limited. In this article, we develop a novel joint modeling method to fill the gap. In the new method, a local polynomial mixed-effects model is used for describing the nonparametric longitudinal pattern of the multiple longitudinal outcomes. Two model estimation procedures, that is, the local EM algorithm and the local penalized quasi-likelihood estimation, are explored. Practical guidelines for choosing tuning parameters and for variable selection are provided. The new method is justified by some theoretical arguments and numerical studies.
Three-dimensional (3D) images have become increasingly popular in practice. They are commonly use... more Three-dimensional (3D) images have become increasingly popular in practice. They are commonly used in medical imaging applications. In such applications, it is often critical to compare two 3D images, or monitor a sequence of 3D images. To make the image comparison or image monitoring valid, the related 3D images should be geometrically aligned first, which is called image registration (IR). However, IR for 3D images would take much computing time, especially when a flexible method is considered, which does not impose any parametric form on the underlying geometric transformation. Here, the authors explore a fast-computing environment for 3D IR based on the distributed parallel computing. The selected 3D IR method is based on the Taylor's expansion and 3D local kernel smoothing. It is flexible, but involves much computation. The authors demonstrate that this fast-computing environment can effectively handle the computing problem while keeping the good properties of the 3D IR method. The method discussed here is therefore useful for applications involving big data.
JOURNAL OF CHEMOMETRICS J. Chemometrics 2005; 19: 2331 Published online in Wiley InterScience (w... more JOURNAL OF CHEMOMETRICS J. Chemometrics 2005; 19: 2331 Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cem.903 ... U. Thissen 1 ,H.Swierenga 2 ,APdeWeijer 2 , R. Wehrens 1 ,WJMelssen 1 andL.MCBuydens 1 *
To monitor the Earth’s surface, the satellite of the NASA Landsat program provides us image seque... more To monitor the Earth’s surface, the satellite of the NASA Landsat program provides us image sequences of any region on the Earth constantly over time. These image sequences give us a unique resource to study the Earth’s surface, changes of the Earth resource over time, and their implications in agriculture, geology, forestry, and more. Besides natural sciences, image sequences are also commonly used in functional magnetic resonance imaging (fMRI) of medical studies for understanding the functioning of brains and other organs. In practice, observed images almost always contain noise and other contaminations. For a reliable subsequent image analysis, it is important to remove such contaminations in advance. This paper focuses on image sequence denoising, which has not been well-discussed in the literature yet. To this end, an edge-preserving image denoising procedure is suggested. The suggested method is based on a jump-preserving local smoothing procedure, in which the bandwidths are...
Some control charts based on machine learning approaches have been developed recently in the stat... more Some control charts based on machine learning approaches have been developed recently in the statistical process control (SPC) literature. These charts are usually designed for monitoring processes with independent observations at different observation times. In practice, however, serial data correlation almost always exists in the observed data of a temporal process. It has been well demonstrated in the SPC literature that control charts designed for monitoring independent data would not be reliable to use in applications with serially correlated data. In this chapter, we suggest using certain existing machine learning control charts together with a recursive data de-correlation procedure. It is shown that the performance of these charts can be substantially improved for monitoring serially correlated processes after data de-correlation. Xiulin Xie Department of Biostatistics, University of Florida, 2004 Mowry Road, Gainesville, FL 32610. e-mail: xiulin.xie@ufl.edu Peihua Qiu Depar...
Statistical process control (SPC) charts are widely used in manufacturing industries for quality ... more Statistical process control (SPC) charts are widely used in manufacturing industries for quality control and management. They are used in more and more other applications, such as internet traffic monitoring, disease surveillance, and environmental protection. Traditional SPC charts designed for monitoring production lines in manufacturing industries are based on the assumptions that observed data are independent and identically distributed with a parametric in-control distribution. These assumptions, however, are rarely valid in practice. Therefore, recent SPC research focuses mainly on development of new control charts that are appropriate to use without these assumptions. In this article, we briefly introduce some recent studies on nonparametric SPC, control charts for monitoring dynamic processes, and spatio-temporal process monitoring. Control charts developed in these directions have found broad applications in practice.
In many clinical studies, evaluating the association between longitudinal and survival outcomes i... more In many clinical studies, evaluating the association between longitudinal and survival outcomes is of primary concern. For analyzing data from such studies, joint modeling of longitudinal and survival data becomes an appealing approach. In some applications, there are multiple longitudinal outcomes whose longitudinal pattern is difficult to describe by a parametric form. For such applications, existing research on joint modeling is limited. In this article, we develop a novel joint modeling method to fill the gap. In the new method, a local polynomial mixed-effects model is used for describing the nonparametric longitudinal pattern of the multiple longitudinal outcomes. Two model estimation procedures, that is, the local EM algorithm and the local penalized quasi-likelihood estimation, are explored. Practical guidelines for choosing tuning parameters and for variable selection are provided. The new method is justified by some theoretical arguments and numerical studies.
Three-dimensional (3D) images have become increasingly popular in practice. They are commonly use... more Three-dimensional (3D) images have become increasingly popular in practice. They are commonly used in medical imaging applications. In such applications, it is often critical to compare two 3D images, or monitor a sequence of 3D images. To make the image comparison or image monitoring valid, the related 3D images should be geometrically aligned first, which is called image registration (IR). However, IR for 3D images would take much computing time, especially when a flexible method is considered, which does not impose any parametric form on the underlying geometric transformation. Here, the authors explore a fast-computing environment for 3D IR based on the distributed parallel computing. The selected 3D IR method is based on the Taylor's expansion and 3D local kernel smoothing. It is flexible, but involves much computation. The authors demonstrate that this fast-computing environment can effectively handle the computing problem while keeping the good properties of the 3D IR method. The method discussed here is therefore useful for applications involving big data.
JOURNAL OF CHEMOMETRICS J. Chemometrics 2005; 19: 2331 Published online in Wiley InterScience (w... more JOURNAL OF CHEMOMETRICS J. Chemometrics 2005; 19: 2331 Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cem.903 ... U. Thissen 1 ,H.Swierenga 2 ,APdeWeijer 2 , R. Wehrens 1 ,WJMelssen 1 andL.MCBuydens 1 *
To monitor the Earth’s surface, the satellite of the NASA Landsat program provides us image seque... more To monitor the Earth’s surface, the satellite of the NASA Landsat program provides us image sequences of any region on the Earth constantly over time. These image sequences give us a unique resource to study the Earth’s surface, changes of the Earth resource over time, and their implications in agriculture, geology, forestry, and more. Besides natural sciences, image sequences are also commonly used in functional magnetic resonance imaging (fMRI) of medical studies for understanding the functioning of brains and other organs. In practice, observed images almost always contain noise and other contaminations. For a reliable subsequent image analysis, it is important to remove such contaminations in advance. This paper focuses on image sequence denoising, which has not been well-discussed in the literature yet. To this end, an edge-preserving image denoising procedure is suggested. The suggested method is based on a jump-preserving local smoothing procedure, in which the bandwidths are...
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