| 1. |
- Eriksson, Lennart, et al.
(författare)
-
Multi- and Megavariate Data Analysis : Part II: Advanced Applications and Method Extensions
- 2006
-
Bok (refereegranskat)abstract
- This second volume has two parts, the first with specialized applications of multi- and mega-variate analysis, namely:QSAR (quantitative structure-activity relationships) describes how series of molecular structures can be translated to quantitative data and how these data then are used to model and predict biological activity measurements made on the corresponding molecules. Chapters on how the QSAR concept applies in peptide QSAR, lead finding and optimization, combinatorial chemistry, and chem-and bio-informatics, are included.The multi- and megavariate analysis of “omics” data, has a special chapter, i.e., data from metabonomics, proteomics, genomics and other areas.Then follow six chapters on extensions of the basic projection methods (PCA and PLS):Orthogonal PLS (OPLS) showing how a PLS model can be “rotated” so that all y-related information appears in the first component, which facilitates the model interpretation.Hierarchical modeling, both PC and PLS, allowing variables of different types to be handled in separate blocks, which greatly simplifies the handling of datasets with very many variables.Non-linear PLS describes various approaches to the modeling of non-linear relationships between predictors X and responses Y.The Image Analysis chapter shows how multivariate analysis applies to the analysis of series of digital images.Data Mining and Integration has a discussion of how to get useful information out of large and complicated data sets, and how to manage and organize data in complex investigations.The second volume ends with a chapter on preference and sensory data, followed by an appendix summarizing the multivariate approach, statistical notes, and references.
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
- Fridén, Håkan, et al.
(författare)
-
Multivariate process monitoring and forcasting by projection methods
- 1994
-
Ingår i: Proceedings of the Third IEEE Conference on Control Applications Glasgow, 24-26 aug 1994. - Piscataway : IEEE Control Systems Society. ; , s. 1345-1346
-
Konferensbidrag (refereegranskat)abstract
- Immense amounts of data are collected into today's modern process monitoring systems. There are, however, few methods that have the capability to grasp the essentials in these, usually heavily correlated, data. The multivariate statistical techniques, principal components (PC) modelling and modelling by projection to latent structures (PLS) are two methods that have a great potential for process monitoring and forecasting in these situations
|
|
| 5. |
- Wikström, Conny, et al.
(författare)
-
Multivariate process and quality monitoring applied to an electrolysis process. : Part II - Multivariate time-series analysis of lagged latent variables
- 1998
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439. ; 42:1-2, s. 233-240
-
Tidskriftsartikel (refereegranskat)abstract
- Multivariate time series analysis is applied to understand and model the dynamics of an electrolytic process manufacturing copper. Here, eight metal impurities were measured, twice daily, over a period of one year, to characterize the quality of the copper. In the data analysis, these eight variables were summarized by means of principal component analysis PCA.. Two principal component PC.scores were sufficient to well summarize the eight measured variables R2s0.67.. Subse-quently, the dynamics of these PC-scores latent variables.were investigated using multivariate time series analysis, i.e., par-tial least squares PLS.modelling of the lagged latent variables. Stochastic models of the auto-regressive moving average ARMA.family were appropriate for both PC-scores. Hence, the dynamics of both scores make the exponentially weighted moving average EWMA.control chart suitable for process monitoring.
|
|
| 6. |
- Wikström, Conny, et al.
(författare)
-
Multivariate process and quality monitoring applied to an electrolysis process. : Part I - Process supervision with multivariate control charts
- 1998
-
Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439. ; 42, s. 221-231
-
Tidskriftsartikel (refereegranskat)abstract
- Multivariate statistical process control MSPC.is applied to an electrolysis process. The process produces extremely pure copper, and to monitor its quality the levels of eight metal impurities were recorded twice a day. These quality data are analysed adopting an 1. ‘intuitive’ univariate approach, and 2. with multivariate techniques. It is demonstrated that the univariate analysis gives confusing results with regards to outlier detection, while the multivariate approach identifies two types of outliers. Moreover, it is shown how the results from the multivariate principal component analysis PCA.method can be displayed graphically in multivariate control charts. Multivariate Shewhart, cumulative sum CUSUM.and exponentially weighted moving average EWMA.control charts are used and compared. Also, an informationally powerful control chart, the simultaneous scores monitoring and residual tracking SMART.chart, is introduced and used.
|
|
| 7. |
- Wikström, Sverre, 1978-, et al.
(författare)
-
Tactile stimulus and neurohormonal response : A pilot study
- 2003
-
Ingår i: International Journal of Neuroscience. - : Informa UK Limited. - 0020-7454 .- 1563-5279 .- 1543-5245. ; 113:6, s. 787-793
-
Tidskriftsartikel (refereegranskat)abstract
- The effects of tactile stimuli on plasma oxytocin and neuropeptide Y (NPY) were investigated in 21 volunteers exposed to massage. Blood samples for basal values were drawn immediately before and immediately after finishing the massage. A third sample was drawn after 60 min of restricted rest. On focusing on the difference between oxytocin concentrations before and immediately after massage, we found a sex difference. An opposite sex difference was found for NPY. The results imply that there might be sex-related difference in neurohormonal response to tactile stimuli such as in massage, and the results contradict those of previously reported animal experiments.
|
|
