Готові списки джерел за темами / Explorative multivariate data analysis

Добірка наукової літератури з теми "Explorative multivariate data analysis"

Автор: Grafiati
Опубліковано: 23 вересня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Explorative multivariate data analysis"

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Demšar, Janez, Gregor Leban, and Blaž Zupan. "FreeViz—An intelligent multivariate visualization approach to explorative analysis of biomedical data." Journal of Biomedical Informatics 40, no. 6 (December 2007): 661–71. http://dx.doi.org/10.1016/j.jbi.2007.03.010.

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Javadnejad, Farid, Javad EskandariShahraki, Sanaz Khoubani, Elham Kalantari, and Firouz Alinia. "Multivariate Analysis of Stream Sediment Geochemical Data for Gold Exploration in Delijan, Iran." International Journal of Research and Engineering 5, no. 2 (March 2018): 325–34. http://dx.doi.org/10.21276/ijre.2018.5.3.2.

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Doleisch, Helmut, and Helwig Hauser. "Interactive Visual Exploration and Analysis of Multivariate Simulation Data." Computing in Science & Engineering 14, no. 2 (March 2012): 70–77. http://dx.doi.org/10.1109/mcse.2012.27.

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Rudi, Knut, Tove Maugesten, Sigrun E. Hannevik, and Hilde Nissen. "Explorative Multivariate Analyses of 16S rRNA Gene Data from Microbial Communities in Modified-Atmosphere-Packed Salmon and Coalfish." Applied and Environmental Microbiology 70, no. 8 (August 2004): 5010–18. http://dx.doi.org/10.1128/aem.70.8.5010-5018.2004.

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ABSTRACT Modified-atmosphere packaging (MAP) of foods in combination with low-temperature storage extends product shelf life by limiting microbial growth. We investigated the microbial biodiversity of MAP salmon and coalfish by using an explorative approach and analyzing both the total amounts of bacteria and the microbial group composition (both aerobic and anaerobic bacteria). Real-time PCR analyses revealed a surprisingly large difference in the microbial loads for the different fish samples. The microbial composition was determined by examining partial 16S rRNA gene sequences from 180 bacterial isolates, as well as by performing terminal restriction fragment length polymorphism analysis and cloning 92 sequences from PCR products of DNA directly retrieved from the fish matrix. Twenty different bacterial groups were identified. Partial least-squares (PLS) regression was used to relate the major groups of bacteria identified to the fish matrix and storage time. A strong association of coalfish with Photobacterium phosphoreum was observed. Brochothrix spp. and Carnobacterium spp., on the other hand, were associated with salmon. These bacteria dominated the fish matrixes after a storage period. Twelve Carnobacterium isolates were identified as either Carnobacterium piscicola (five isolates) or Carnobacterium divergens (seven isolates), while the eight Brochothrix isolates were identified as Brochothrix thermosphacta by full-length 16S rRNA gene sequencing. Principal-component analyses and PLS analysis of the growth characteristics (with 49 different substrates) showed that C. piscicola had distinct substrate requirements, while the requirements of B. thermosphacta and C. piscicola were quite divergent. In conclusion, our explorative multivariate approach gave a picture of the total microbial biodiversity in MAP fish that was more comprehensive than the picture that could be obtained previously. Such information is crucial in controlled food production when, for example, the hazard analysis of critical control points principle is used.
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Rehder, S., and A. Muller. "MAX, a program system for multivariate data analysis of geochemical exploration data." Journal of Geochemical Exploration 29, no. 1-3 (January 1987): 429. http://dx.doi.org/10.1016/0375-6742(87)90117-8.

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Liu, Xiaotong, and Han-Wei Shen. "Association Analysis for Visual Exploration of Multivariate Scientific Data Sets." IEEE Transactions on Visualization and Computer Graphics 22, no. 1 (January 31, 2016): 955–64. http://dx.doi.org/10.1109/tvcg.2015.2467431.

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7

Carbonara, Pierluigi, Walter Zupa, Aikaterini Anastasopoulou, Andrea Bellodi, Isabella Bitetto, Charis Charilaou, Archontia Chatzispyrou, et al. "Explorative analysis on red mullet (Mullus barbatus) ageing data variability in the Mediterranean." Scientia Marina 83, S1 (January 9, 2020): 271. http://dx.doi.org/10.3989/scimar.04999.19a.

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The uncertainty in age estimation by otolith reading may be at the root of the large variability in red mullet (Mullus barbatus) growth models in the Mediterranean. In the MEDITS survey, red mullet age data are produced following the same sampling protocol and otolith reading methodology. However, ageing is assigned using different interpretation schemes, including variations in theoretical birthdate and number of false rings considered, in addition to differences in the experience level of readers. The present work analysed the influence of these variations and the geographical location of sampling on red mullet ageing using a multivariate approach (principal component analysis). Reader experience was the most important parameter correlated with the variability. The number of rings considered false showed a significant effect on the variability in the first age groups but had less influence on the older ones. The effect of the theoretical birthdate was low in all age groups. Geographical location had a significant influence, with longitude showing greater effects than latitude. In light of these results, workshops, exchanges and the adoption of a common ageing protocol based on age validation studies are considered fundamental tools for improving precision in red mullet ageing.
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Gili-Kovács, Judit, Robert Hoepner, Anke Salmen, Maud Bagnoud, Ralf Gold, Andrew Chan, and Myriam Briner. "An algorithm using clinical data to predict the optimal individual glucocorticoid dosage to treat multiple sclerosis relapses." Therapeutic Advances in Neurological Disorders 14 (January 2021): 175628642110200. http://dx.doi.org/10.1177/17562864211020074.

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Background: Glucocorticoid (GC) pulse therapy is used for multiple sclerosis (MS) relapse treatment; however, GC resistance is a common problem. Considering that GC dosing is individual with several response-influencing factors, establishing a predictive model, which supports clinicians to estimate the maximum GC dose above which no additional therapeutic value can be expected presents a huge clinical need. Method: We established two, independent retrospective cohorts of MS patients. The first was an explorative cohort for model generation, while the second was established for its validation. Using the explorative cohort, a multivariate regression analysis with the GC dose used as the dependent variable and serum vitamin D (25D) concentration, sex, age, EDSS, contrast enhancement on cranial magnetic resonance imaging (MRI), immune therapy, and the involvement of the optic nerve as independent variables was established. Results: In the explorative cohort, 113 MS patients were included. 25-hydroxyvitamin D (25D) serum concentration and the presence of optic neuritis were independent predictors of the GC dose needed to treat MS relapses [(25D): −25.95 (95% confidence interval (CI)): −47.40 to −4.49; p = 0.018; optic neuritis: 2040.51 (95% CI: 584.64–3496.36), p = 0.006]. Validation of the multivariate linear regression model was performed within a second cohort. Here, the predicted GC dose did not differ significantly from the dose administered in clinical routine (mean difference: −843.54; 95% CI: −2078.08–391.00; n = 30, p = 0.173). Conclusion: Our model could predict the GC dose given in clinical, routine MS relapse care, above which clinicians estimate no further benefit. Further studies should validate and improve our algorithm to help the implementation of predictive models in GC dosing.
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Bjørsvik, Hans-René. "Reaction Monitoring in Explorative Organic Synthesis Using Fiber-Optical NIR Spectroscopy and Principal Component Analysis." Applied Spectroscopy 50, no. 12 (December 1996): 1541–44. http://dx.doi.org/10.1366/0003702963904485.

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A method of combining spectroscopy and multivariate data analysis for obtaining quantitative information on how a reaction proceeds is presented. The method is an approach for the explorative synthetic organic laboratory rather than the analytical chemistry laboratory. The method implements near-infrared spectroscopy with an optical fiber transreflectance probe as instrumentation. The data analysis consists of decomposition of the spectral data, which are recorded during the course of a reaction by using principal component analysis to obtain latent variables, scores, and loading. From the scores and the corresponding reaction time, it is possible to obtain a reaction profile. This reaction profile can easily be recalculated to obtain the concentration profile over time. This calculation is based on only two quantitative measurements, which can be (1) measurement from the work-up of the reaction or (2) chromatographic analysis from two withdrawn samples during the reaction. The method is applied to the synthesis of 3-amino-propan-1,2-diol.
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Mehmedinović, Senad. "FUNDAMENTALS OF APPLICATION FACTOR ANALYSIS IN EDUCATION AND REHABILITATION." Journal Human Research in Rehabilitation 7, no. 1 (April 2017): 61–65. http://dx.doi.org/10.21554/hrr.041708.

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Factor analysis is one of multivariate data processing methods, which studies the causal relationships of phenomena, that is, the cause of integration. In the introductory part of the paper, the basic definitions and interpretations regarding the factor analysis and the terms of multivariate methods, and some examples have been given in defining the manifest and latent, as explorative and confirmative examples. The justification for the application of factor analysis is elaborated in the main part of the paper with reference to the various authors who have dealt with this issue. Also, the paper presents the procedures of factor analysis, and presents tables and graphs showing the results necessary for interpretation. Given that for special education and rehabilitation a biopsychosocial approach is fundamental, factor analysis can be a powerful tool when studying interconnections of different phenomena. Its proper application by educatorsrehabilitators, who act to this problem, may help in understanding the causes of connections of phenomena, and as such it helps in the development of a treatment for the prevention, education and rehabilitation of persons with disabilities.
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Більше джерел

Дисертації з теми "Explorative multivariate data analysis"

1

Bergfors, Linus. "Explorative Multivariate Data Analysis of the Klinthagen Limestone Quarry Data." Thesis, Uppsala University, Department of Information Technology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-122575.

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The today quarry planning at Klinthagen is rough, which provides an opportunity to introduce new exciting methods to improve the quarry gain and efficiency. Nordkalk AB, active at Klinthagen, wishes to start a new quarry at a nearby location. To exploit future quarries in an efficient manner and ensure production quality, multivariate statistics may help gather important information.

In this thesis the possibilities of the multivariate statistical approaches of Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression were evaluated on the Klinthagen bore data. PCA data were spatially interpolated by Kriging, which also was evaluated and compared to IDW interpolation.

Principal component analysis supplied an overview of the variables relations, but also visualised the problems involved when linking geophysical data to geochemical data and the inaccuracy introduced by lacking data quality.

The PLS regression further emphasised the geochemical-geophysical problems, but also showed good precision when applied to strictly geochemical data.

Spatial interpolation by Kriging did not result in significantly better approximations than the less complex control interpolation by IDW.

In order to improve the information content of the data when modelled by PCA, a more discrete sampling method would be advisable. The data quality may cause trouble, though with sample technique of today it was considered to be of less consequence.

Faced with a single geophysical component to be predicted from chemical variables further geophysical data need to complement existing data to achieve satisfying PLS models.

The stratified rock composure caused trouble when spatially interpolated. Further investigations should be performed to develop more suitable interpolation techniques.

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2

Yang, Di. "Analysis guided visual exploration of multivariate data." Worcester, Mass. : Worcester Polytechnic Institute, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-050407-005925/.

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Engel, Daniel [Verfasser], Hans [Akademischer Betreuer] Hagen, and Bernd [Akademischer Betreuer] Hamann. "Explorative and Model-based Visual Analysis of Multivariate Data / Daniel Engel. Betreuer: Hans Hagen ; Bernd Hamann." Kaiserslautern : Technische Universität Kaiserslautern, 2014. http://d-nb.info/1054636176/34.

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Doshi, Punit Rameshchandra. "Adaptive prefetching for visual data exploration." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0131103-203307.

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Анотація:
Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: Adaptive prefetching; Large-scale multivariate data visualization; Semantic caching; Hierarchical data exploration; Exploratory data analysis. Includes bibliographical references (p.66-70).
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5

Lu, Kewei. "Distribution-based Exploration and Visualization of Large-scale Vector and Multivariate Fields." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1483545901567695.

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Vargas, Aurea Rossy Soriano. "Visual exploration to support the identification of relevant attributes in time-varying multivariate data." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-23102018-115029/.

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Ionospheric scintillation is a rapid variation in the amplitude and/or phase of radio signals traveling through the ionosphere. This spatial and time-varying phenomenon is of interest because its occurrence may affect the reception quality of satellite signals. Specialized receivers at strategic regions can track multiple variables related to the phenomenon, generating a database of historical observations on the regional behavior of ionospheric scintillation. The analysis of such data is very challenging, since it consists of time-varying measurements of many variables which are heterogeneous in nature and with possibly many missing values, recorded over extensive time periods. There is a need to introduce alternative intuitive strategies that contribute to experts acquiring further knowledge from the ionospheric scintillation data. Such challenges motivated a study on the applicability of visualization techniques to support tasks of identification of relevant attributes in the study of the behavior of phenomena described by multiple time-varying variables, of which the ionospheric scintillation is a good example. In particular, this thesis introduces a visual analytics framework, named TV-MV Analytics, that supports exploratory tasks on time-varying multivariate data and was developed following the requirements of experts on ionospheric scintillation from the Faculty of Science and Technology of UNESP at Presidente Prudente, Brazil. TV-MV Analytics provides an interactive visual exploration loop to analysts inspecting the behavior of multiple variables at different temporal scales, through temporal representations associated with clustering and multidimensional projection techniques. Analysts can also assess how different feature sub-spaces contribute to characterizing a certain behavior, where they may direct the analysis process and include their domain knowledge in the exploratory analysis. We also illustrate the application of TV-MV Analytics on multivariate time-varying data sets from three alternative application domains. Experimental results indicate the proposed solutions show good potential on assisting time-varying multivariate data mining tasks, since it reduces the effort required from experts to gain deeper insight into the historical behavior of the variables describing a phenomenon or domain.
A cintilação ionosférica é uma variação rápida na amplitude e/ou na fase dos sinais de rádio que viajam através da ionosfera. Este fenômeno espacial e variante no tempo é de grande interesse, pois pode afetar a qualidade de recepção dos sinais de satélite. Receptores especializados em regiões estratégicas podem rastrear múltiplas variáveis relacionadas ao fenômeno, gerando um banco de dados de observações históricas sobre o comportamento regional da cintilação. O estudo do comportamento da cintilação é desafiador, uma vez que requer a análise extensiva de dados multivariados e variantes no tempo, coletados por longos períodos. Medições são registradas continuamente, e são de natureza heterogênea, compreendendo múltiplas variáveis de diferentes categorias e possivelmente com muitos valores faltantes. Portanto, existe a necessidade de introduzir estratégias alternativas, eficientes e intuitivas, que contribuam para a adquisição de conhecimento, a partir dos dados, por especialistas que estudam a cintilação ionosférica. Tais desafios motivaram o estudo da aplicabilidade de técnicas de visualização para apoiar tarefas de identificação de atributos relevantes no estudo do comportamento de fenômenos ou domínios que envolvem múltiplas variáveis, como a cintilação. Em particular, esta tese introduz um arcabouço visual, o qual foi denominado TV-MV Analytics, que apoia tarefas de análise exploratória sobre dados multivariados e variáveis no tempo, inspirado em requisitos de especialistas no estudo da cintilação, vinculados à Faculdade de Ciências e Tecnologia da UNESP de Presidente Prudente, Brasil. O TV-MV Analytics fornece aos analistas um ciclo de interativo de exploração que apoia a inspeção do comportamento temporal de múltiplas variáveis, em diferentes escalas temporais, por meio de representações visuais temporais associadas a técnicas de agrupamento e de projeção multidimensional. Também permite avaliar como diferentes sub-espaços de atributos caracterizam um determinado comportamento, podendo direcionar o processo de análise e inserir seu conhecimento do domínio no processo de análise exploratória. As funcionalidades do TV-MV Analytics também são ilustradas em dados variantes no tempo oriundos de outros três domínios de aplicação. Os resultados experimentais indicaram que as soluções propostas têm bom potencial em tarefas de mineração de dados multivariados e variantes no tempo, uma vez que reduz o esforço e contribui para os especialistas obterem informações detalhadas sobre o comportamento histórico das variáveis que descrevem um determinado fenômeno ou domínio.
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Rammelkamp, Kristin. "Investigation of LIBS and Raman data analysis methods in the context of in-situ planetary exploration." Doctoral thesis, Humboldt-Universität zu Berlin, 2019. http://dx.doi.org/10.18452/20703.

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Die in dieser Arbeit vorgestellten Studien untersuchen verschiedene Ansätze für die Analyse von spektroskopischen Daten für die Erforschung anderer Himmelskörper. Der Fokus lag hierbei auf der laserinduzierten Plasmaspektroskopie (LIBS, engl. laser-induced breakdown spectroscopy), aber auch Daten der Raman-Spektroskopie wurden analysiert. Das erste extraterrestrisch eingesetzte LIBS Instrument, ChemCam, auf dem Mars Science Laboratory (MSL) der NASA untersucht die Marsoberfläche seit 2012 und weitere Missionen mit LIBS und Raman Instrumenten zum Mars sind geplant. Neben analytischen Ansätzen wurden statistische Methoden, die als multivariate Datenanalysen (MVA) bekannt sind, verwendet und evaluiert. In dieser Arbeit werden insgesamt vier Studien vorgestellt. In der ersten Studie wurde die Normalisierung von LIBS Daten mit Plasmaparametern, also der Plasmatemperatur und der Elektronendichte, untersucht. In der zweiten Studie wurden LIBS Messungen unter Vakuumbedingungen im Hinblick auf den Ionisierungsgrad des Plasmas untersucht. In der dritten Studie wurden MVA Methoden wie die Hauptkomponentenanalyse (PCA) und die partielle Regression kleinster Quadrate (PLS-R) zur Identifizierung und Quantifizierung von Halogenen mittels molekularer Emissionen angewandt. Die Ergebnisse sind vielversprechend, da es möglich war z.B. Chlor in einem ausgewählten Konzentrationsbereich zu quantifizieren. In der letzten Studie wurden LIBS-Daten mit komplementären Raman-Daten von Mars relevanten Salzen in einem low-level Datenfusionsansatz kombiniert. Es wurden MVA Methoden angewandt und auch Konzepte der high-level Datenfusion implementiert. Mit der low-level LIBS und Raman Datenfusion konnten im Vergleich zu den einzelnen Techniken mehr Salze richtig identifiziert werden. Der Gewinn durch die low-level Datenfusion ist jedoch vergleichsweise gering und für konkrete Missionen müssen individuelle und angepasste Strategien für die gemeinsame Analyse von LIBS und Raman-Daten gefunden werden.
The studies presented in this thesis investigate different data analysis approaches for mainly laser-induced breakdown spectroscopy (LIBS) and also Raman data in the context of planetary in-situ exploration. Most studies were motivated by Mars exploration due to the first extraterrestrially employed LIBS instrument ChemCam on NASA's Mars Science Laboratory (MSL) and further planned LIBS and Raman instruments on upcoming missions to Mars. Next to analytical approaches, statistical methods known as multivariate data analysis (MVA) were applied and evaluated. In this thesis, four studies are presented in which LIBS and Raman data analysis strategies are evaluated. In the first study, LIBS data normalization with plasma parameters, namely the plasma temperature and the electron density, was studied. In the second study, LIBS measurements in vacuum conditions were investigated with a focus on the degree of ionization of the LIBS plasma. In the third study, the capability of MVA methods such as principal component analysis (PCA) and partial least squares regression (PLS-R) for the identification and quantification of halogens by means of molecular emissions was tested. The outcomes are promising, as it was possible to distinguish apatites and to quantify chlorine in a particular concentration range. In the fourth and last study, LIBS data was combined with complementary Raman data in a low-level data fusion approach using MVA methods. Also, concepts of high-level data fusion were implemented. Low-level LIBS and Raman data fusion can improve identification capabilities in comparison to the single datasets. However, the improvement is comparatively small regarding the higher amount of information in the low-level fused data and dedicated strategies for the joint analysis of LIBS and Raman data have to be found for particular scientific objectives.
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8

Ablin, Pierre. "Exploration of multivariate EEG /MEG signals using non-stationary models." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT051.

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L'Analyse en Composantes Indépendantes (ACI) modèle un ensemble de signaux comme une combinaison linéaire de sources indépendantes. Cette méthode joue un rôle clé dans le traitement des signaux de magnétoencéphalographie (MEG) et électroencéphalographie (EEG). L'ACI de tels signaux permet d'isoler des sources de cerveau intéressantes, de les localiser, et de les séparer d'artefacts. L'ACI fait partie de la boite à outils de nombreux neuroscientifiques, et est utilisée dans de nombreux articles de recherche en neurosciences. Cependant, les algorithmes d'ACI les plus utilisés ont été développés dans les années 90. Ils sont souvent lents lorsqu'ils sont appliqués sur des données réelles, et sont limités au modèle d'ACI classique.L'objectif de cette thèse est de développer des algorithmes d'ACI utiles en pratique aux neuroscientifiques. Nous suivons deux axes. Le premier est celui de la vitesse : nous considérons le problème d'optimisation résolu par deux des algorithmes les plus utilisés par les praticiens: Infomax et FastICA. Nous développons une nouvelle technique se basant sur un préconditionnement par des approximations de la Hessienne de l'algorithm L-BFGS. L'algorithme qui en résulte, Picard, est conçu pour être appliqué sur données réelles, où l'hypothèse d’indépendance n'est jamais entièrement vraie. Sur des données de M/EEG, il converge plus vite que les implémentations `historiques'.Les méthodes incrémentales, qui traitent quelques échantillons à la fois au lieu du jeu de données complet, constituent une autre possibilité d’accélération de l'ACI. Ces méthodes connaissent une popularité grandissante grâce à leur faculté à bien passer à l'échelle sur de grands jeux de données. Nous proposons un algorithme incrémental pour l'ACI, qui possède une importante propriété de descente garantie. En conséquence, cet algorithme est simple d'utilisation, et n'a pas de paramètre critique et difficile à régler comme un taux d'apprentissage.En suivant un second axe, nous proposons de prendre en compte du bruit dans le modèle d'ACI. Le modèle resultant est notoirement difficile et long à estimer sous l'hypothèse standard de non-Gaussianité de l'ACI. Nous nous reposons donc sur une hypothèse de diversité spectrale, qui mène à un algorithme facile d'utilisation et utilisable en pratique, SMICA. La modélisation du bruit permet de nouvelles possibilités inenvisageables avec un modèle d'ACI classique, comme une estimation fine des source et l'utilisation de l'ACI comme une technique de réduction de dimension statistiquement bien posée. De nombreuses expériences sur données M/EEG démontrent l'utilité de cette nouvelle approche.Tous les algorithmes développés dans cette thèse sont disponibles en accès libre sur internet. L’algorithme Picard est inclus dans les librairies de traitement de données M/EEG les plus populaires en Python (MNE) et en Matlab (EEGlab)
Independent Component Analysis (ICA) models a set of signals as linear combinations of independent sources. This analysis method plays a key role in electroencephalography (EEG) and magnetoencephalography (MEG) signal processing. Applied on such signals, it allows to isolate interesting brain sources, locate them, and separate them from artifacts. ICA belongs to the toolbox of many neuroscientists, and is a part of the processing pipeline of many research articles. Yet, the most widely used algorithms date back to the 90's. They are often quite slow, and stick to the standard ICA model, without more advanced features.The goal of this thesis is to develop practical ICA algorithms to help neuroscientists. We follow two axes. The first one is that of speed. We consider the optimization problems solved by two of the most widely used ICA algorithms by practitioners: Infomax and FastICA. We develop a novel technique based on preconditioning the L-BFGS algorithm with Hessian approximation. The resulting algorithm, Picard, is tailored for real data applications, where the independence assumption is never entirely true. On M/EEG data, it converges faster than the `historical' implementations.Another possibility to accelerate ICA is to use incremental methods, which process a few samples at a time instead of the whole dataset. Such methods have gained huge interest in the last years due to their ability to scale well to very large datasets. We propose an incremental algorithm for ICA, with important descent guarantees. As a consequence, the proposed algorithm is simple to use and does not have a critical and hard to tune parameter like a learning rate.In a second axis, we propose to incorporate noise in the ICA model. Such a model is notoriously hard to fit under the standard non-Gaussian hypothesis of ICA, and would render estimation extremely long. Instead, we rely on a spectral diversity assumption, which leads to a practical algorithm, SMICA. The noise model opens the door to new possibilities, like finer estimation of the sources, and use of ICA as a statistically sound dimension reduction technique. Thorough experiments on M/EEG datasets demonstrate the usefulness of this approach.All algorithms developed in this thesis are open-sourced and available online. The Picard algorithm is included in the largest M/EEG processing Python library, MNE and Matlab library, EEGlab
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Oliveira, Irene. "Correlated data in multivariate analysis." Thesis, University of Aberdeen, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401414.

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After presenting (PCA) Principal Component Analysis and its relationship with time series data sets, we describe most of the existing techniques in this field. Various techniques, e.g. Singular Spectrum Analysis, Hilbert EOF, Extended EOF or Multichannel Singular Spectrum Analysis (MSSA), Principal Oscillation Pattern Analysis (POP Analysis), can be used for such data. The way we use the matrix of data or the covariance or correlation matrix, makes each method different from the others. SSA may be considered as a PCA performed on a lagged versions of a single time series where we may decompose the original time series into some main components. Following SSA we have its multivariate version (MSSA) where we try to augment the initial matrix of data to get information on lagged versions of each variable (time series) and so past (or future) behaviour can be used to reanalyse the information between variables. In POP Analysis a linear system involving the vector field is analysed, xt+1=Axt+nt, in order to “know” xt at time t+1 given the information from time t. The matrix A is estimated by using not only the covariance matrix but also the matrix of covariances between the systems at the current time and at lag 1. In Hilbert EOF we try to get some (future) information from the internal correlation in each variable by using the Hilbert transform of each series in a augmented complex matrix with the data themselves in the real part and the Hilbert time series in the imaginary part Xt + XtH. In addition to all these ideas from the statistics and other literature we develop a new methodology as a modification of HEOF and POP Analysis, namely Hilbert Oscillation Patterns (HOP) Analysis or the related idea of Hilbert Canonical Correlation Analysis (HCCA), by using a system, xHt = Axt + nt. Theory and assumptions are presented and HOPS results will be related with the results extracted from a Canonical Correlation Analysis between the time series data matrix and its Hilbert transform. Some examples will be given to show the differences and similarities of the results of the HCCA technique with those from PCA, MSSA, HEOF and POPs. We also present PCA for time series as observations where a technique of linear algebra (PCA) becomes a problem in function analysis leading to Functional PCA (FPCA).  We also adapt PCA to allow for this and discuss the theoretical and practical behaviour of using PCA on the even part (EPCA) and odd part (OPCA) of the data, and its application in functional data. Comparisons will be made between PCA and this modification, for the reconstruction of data sets for which considerations of symmetry are especially relevant.
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Prelorendjos, Alexios. "Multivariate analysis of metabonomic data." Thesis, University of Strathclyde, 2014. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=24286.

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Metabonomics is one of the main technologies used in biomedical sciences to improve understanding of how various biological processes of living organisms work. It is considered a more advanced technology than e.g. genomics and proteomics, as it can provide important evidence of molecular biomarkers for the diagnosis of diseases and the evaluation of beneficial adverse drug effects, by studying the metabolic profiles of living organisms. This is achievable by studying samples of various types such as tissues and biofluids. The findings of a metabonomics study for a specific disease, disorder or drug effect, could be applied to other diseases, disorders or drugs, making metabonomics an important tool for biomedical research. This thesis aims to review and study various multivariate statistical techniques which can be used in the exploratory analysis of metabonomics data. To motivate this research, a metabonomics data set containing the metabolic profiles of a group of patients with epilepsy was used. More specifically, the metabolic fingerprints (proton NMR spectra) of 125 patients with epilepsy, of blood serum type, have been obtained from the Western Infirmary, Glasgow, for the purposes of this project. These data were originally collected as baseline data in a study to investigate if the treatment with Anti-Epileptic Drugs (AEDs), of patients with pharmacoresistant epilepsy affects the seizure levels of the patients. The response to the drug treatment in terms of the reduction in seizure levels of these patients enabled two main categories of response to be identified, i.e. responders and the non-responders to AEDs. We explore the use of statistical methods used in metabonomics to analyse these data. Novel aspects of the thesis are the use of Self Organising Maps (SOM) and of Fuzzy Clustering Methods to pattern recognition in metabonomics data. Part I of the thesis defines metabonomics and the other main "omics" technologies, and gives a detailed description of the metabonomics data to be analysed, as well as a description of the two main analytical chemical techniques, Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR), that can be used to generate metabonomics data. Pre-processing and pre-treatment methods that are commonly used in NMR-generated metabonomics data to enhance the quality and accuracy of the data, are also discussed. In Part II, several unsupervised statistical techniques are reviewed and applied to the epilepsy data to investigate the capability of these techniques to discriminate the patients according to their type of response. The techniques reviewed include Principal Components Analysis (PCA), Multi-dimensional scaling (both Classical scaling and Sammon's non-linear mapping) and Clustering techniques. The latter include Hierarchical clustering (with emphasis on Agglomerative Nesting algorithms), Partitioning methods (Fuzzy and Hard clustering algorithms) and Competitive Learning algorithms (Self Organizing maps). The advantages and disadvantages of the different methods are examined, for this kind of data. Results of the exploratory multivariate analyses showed that no natural clusters of patients existed with regards to th eir response to AEDs, therefore none of these techniques was capable of discriminating these patients according to their clinical characteristics. To examine the capability of an unsupervised technique such as PCA, to identify groups in such data as the data based on metabolic fingerprints of patients with epilepsy, a simulation algorithm was developed to run a series of experiments, covered in Part III of the thesis. The aim of the simulation study is to investigate the extent of the difference in the clusters of the data, and under what conditions this difference is detectable by unsupervised techniques. Furthermore, the study examines whether the existence or lack of variation in the mean-shifted variables affects the discriminating ability of the unsupervised techniques (in this case PCA) or not. In each simulation experiment, a reference and a test data set were generated based on the original epilepsy data, and the discriminating capability of PCA was assessed. A test set was generated by mean-shifting a pre-selected number of variables in a reference set. Three methods of selecting the variables to meanshift (maximum and minimum standard deviations and maximum means), five subsets of variables of sizes 1, 3, 20, 120 and 244 (total number of variables in the data sets) and three sample sizes (100, 500 and 1000) were used. Average values in 100 runs of an experiment for two statistics, i.e. the misclassification rate and the average separation (Webb, 2002) were recorded. Results showed that the number of mean-shifted variables (in general) and the methods used to select the variables (in some cases) are important factors for the discriminating ability of PCA, whereas the sample size of the two data sets does not play any role in the experiments (although experiments in large sample sizes showed greater stability in the results for the two statistics in 100 runs of any experiment). The results have implications for the use of PCA with metabonomics data generally.
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Книги з теми "Explorative multivariate data analysis"

1

Podani, János. Introduction to the exploration of multivariate biological data. Leiden: Backhuys Publishers, 2000.

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Cooley, William W. Multivariate data analysis. Malabar, Fla: R.E. Krieger Pub. Co., 1985.

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Murtagh, Fionn. Multivariate data analysis. Dordrecht: D. Reidel, 1987.

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Murtagh, Fionn, and André Heck. Multivariate Data Analysis. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3789-5.

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F, Hair Joseph, ed. Multivariate data analysis. Upper Saddle River, N.J: Prentice Hall, 1998.

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F, Hair Joseph, ed. Multivariate data analysis. 6th ed. Upper Saddle River, N.J: Pearson Prentice Hall, 2005.

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F, Hair Joseph, ed. Multivariate data analysis. 5th ed. Englewood Cliffs, N.J: Prentice Hall, 1998.

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Everitt, Brian. Applied multivariate data analysis. 2nd ed. London: Arnold, 2001.

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9

1949-, Dunn G., ed. Applied multivariate data analysis. New York: Oxford University Press, 1992.

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Jobson, J. D. Applied multivariate data analysis. 4th ed. New York: Springer, 1999.

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Частини книг з теми "Explorative multivariate data analysis"

1

Leder, O., and H. Kurz. "Description and Classification of Respiratory Patterns with Multivariate Explorative Statistics." In Studies in Classification, Data Analysis, and Knowledge Organization, 285–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-46757-8_29.

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Everitt, Brian S., and Graham Dunn. "Multivariate Data and Multivariate Statistics." In Applied Multivariate Data Analysis, 1–8. West Sussex, United Kingdom: John Wiley & Sons, Ltd,., 2013. http://dx.doi.org/10.1002/9781118887486.ch1.

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3

Bürgel, Oliver. "Multivariate Data Analysis." In The Internationalisation of British Start-up Companies in High-Technology Industries, 141–85. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57671-3_6.

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Haslwanter, Thomas. "Multivariate Data Analysis." In An Introduction to Statistics with Python, 221–25. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28316-6_12.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Introduction to Empirical Data Analysis." In Multivariate Analysis, 1–54. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32589-3_1.

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Everitt, Brian Sidney. "Multivariate Data and Multivariate Analysis." In Springer Texts in Statistics, 1–15. London: Springer London, 2005. http://dx.doi.org/10.1007/1-84628-124-5_1.

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Everitt, Brian, and Torsten Hothorn. "Multivariate Data and Multivariate Analysis." In An Introduction to Applied Multivariate Analysis with R, 1–24. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9650-3_1.

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Vehkalahti, Kimmo, and Brian S. Everitt. "Multivariate Data and Multivariate Analysis." In Multivariate Analysis for the Behavioral Sciences, 225–37. Second edition. | Boca Raton, Florida : CRC Press [2019] | Earlier edition published as: Multivariable modeling and multivariate analysis for the behavioral sciences / [by] Brian S. Everitt.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351202275-12.

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Dugard, pat, John Todman, and Harry Staines. "Longitudinal data." In Approaching Multivariate Analysis, 359–76. 2nd ed. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003343097-15.

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Murtagh, Fionn, and André Heck. "Cluster Analysis." In Multivariate Data Analysis, 55–109. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3789-5_3.

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Тези доповідей конференцій з теми "Explorative multivariate data analysis"

1

Di Yang, Elke A. Rundensteiner, and Matthew O. Ward. "Analysis Guided Visual Exploration of Multivariate Data." In 2007 IEEE Symposium on Visual Analytics Science and Technology. IEEE, 2007. http://dx.doi.org/10.1109/vast.2007.4389000.

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Rubel, Oliver, Peter Messmer, Hans Hagen, Bernd Hamann, E. Wes Bethel, Prabhat, Kesheng Wu, et al. "High performance multivariate visual data exploration for extremely large data." In 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2008. http://dx.doi.org/10.1109/sc.2008.5214436.

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Wang Xiaohuan, Yuan Guodong, Wang Huan, and Hu Wei. "Visual exploration for time series data using multivariate analysis method." In 2013 8th International Conference on Computer Science & Education (ICCSE). IEEE, 2013. http://dx.doi.org/10.1109/iccse.2013.6554098.

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Kewei Lu and Han-Wei Shen. "Multivariate volumetric data analysis and visualization through bottom-up subspace exploration." In 2017 IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2017. http://dx.doi.org/10.1109/pacificvis.2017.8031588.

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Bezkhodarnov, Vladimir V., Tatiana I. Chichinina, Mikhail O. Korovin, and Valeriy V. Trushkin. "Prediction of Reservoir Properties from Seismic Data by Multivariate Geostatistics Analysis." In SPE Russian Petroleum Technology Conference. SPE, 2021. http://dx.doi.org/10.2118/206595-ms.

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Abstract A new technique has been developed and is being improved, which allows, on the basis of probabilistic and statistical analysis of seismic data, to predict and evaluate the most important parameters of rock properties (including the reservoir properties such as porosity and permeability), that is, oil saturation, effective thicknesses of reservoirs, their sand content, clay content of seals, and others; it is designed to predict the reservoir properties with sufficient accuracy and detail, for subsequent consideration of these estimates when evaluating hydrocarbon reserves and justifying projects for the deposits development. Quantitative reservoir-property prediction is carried out in the following stages: –Optimization of the graph ("scenario") of seismic data processing to solve not only the traditional structural problem of seismic exploration, but also the parametric one that is, the quantitative estimation of rock properties.–Computation of seismic attributes, including exclusive ones, not provided for in existing interpretation software packages.–Estimation of reservoir properties from well logs as the base data.–Multivariate correlation and regression analysis (MCRA) includes the following two stages: Establishing correlations of seismic attributes with estimates of rock properties obtained from well logs.Construction of multidimensional (multiple) regression equations with an assessment of the "information value" of seismic attributes and the reliability of the resulting predictive equations. (By the "informative value" we mean the informativeness quality of the attribute.)–Computation and construction of the forecast map variants, their analysis and producing the resultant map (as the most optimal map version) for each predicted parameter.–Obtaining the resultant forecast maps with their zoning according to the degree of the forecast reliability. The MCRA technique is tested by production and prospecting trusts during exploration and reserves’ estimation of several dozen fields in Western Siberia: Kulginskoye, Shirotnoye, Yuzhno-Tambaevskoye, etc. (Tomsk Geophysical Trust, 1997-2002); Dvurechenskoe, Zapadno-Moiseevskoe, Talovoe, Krapivinskoe, Ontonigayskoe, etc. (TomskNIPIneft, 2002–2013).
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Kawamura, Takuma, Tomoyuki Noda, and Yasuhiro Idomura. "In-Situ Visual Exploration of Multivariate Volume Data Based on Particle Based Volume Rendering." In 2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV). IEEE, 2016. http://dx.doi.org/10.1109/isav.2016.009.

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7

Kraft, Volker. "Storytelling from social data: dynamic data exploration using JMP." In Promoting Understanding of Statistics about Society. International Association for Statistical Education, 2016. http://dx.doi.org/10.52041/srap.16604.

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Interacting with graphical displays and multivariate analysis tools helps to understand what the data are telling us. The interactivity of JMP for all types of data, including social, geographic and time-series data, helps with efficient visualization and modeling, supports decision-making from data, and facilitates the communication of findings and results. In this hands-on workshop we used recent social data to illustrate the power of dynamic tools for data exploration, visualization and analysis, and explored the role of interactivity in statistical education. Participants were asked to download and install the 30-day trial version of JMP (www.jmp.com/trial for Windows or Mac) before the workshop.
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"Using Visual Analytics to Enhance Data Exploration and Knowledge Discovery in Financial Systemic Risk Analysis: The Multivariate Density Estimator." In iConference 2014 Proceedings: Breaking Down Walls. Culture - Context - Computing. iSchools, 2014. http://dx.doi.org/10.9776/14307.

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9

Ridgway, Jim, James Nicholson, and Sean McCusker. "The semantic web demands ‘new’ statistics." In Technology in Statistics Education: Virtualities and Realities. International Association for Statistical Education, 2012. http://dx.doi.org/10.52041/srap.12112.

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The concept of statistical literacy needs to be refreshed, regularly. Major changes in the ways that data can be accessed from government and non-government agencies (the ‘semantic web’) allow everyone to access huge databases, to create new variables, and to explore new relationships. New ways of visualising data provide further challenges and opportunities. The Open Data movement, and the rise of data driven journalism are increasing public access to large scale data via the media. Here, we map out the potential and pitfalls of the semantic web, and discuss the rebalancing of statistics curricula that is required. The most obvious challenge is the need to introduce students to the exploration and analysis of large scale multivariate data sets. We present examples of our visualizations of complex multivariate data, and describe some examples of use in classrooms. General issues of pedagogy and curriculum innovation are discussed.
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Yap, Von Bing. "Simulation-based exploration of surveys with non-response." In New Skills in the Changing World of Statistics Education. International Association for Statistical Education, 2020. http://dx.doi.org/10.52041/srap.20405.

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A simple model of random selection and systematic non-response from a linear bivariate population is proposed to illustrate the bias in estimating regression parameters based only on the responses. The problem is explored graphically and numerically via a simulation study implemented in the statistical computing environment R. It highlights the propensity to bias in regression analysis of observational multivariate data. The model can be substantially modified, to make it possible to imagine any dataset as coming from a randomised survey with possible non-response.
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Звіти організацій з теми "Explorative multivariate data analysis"

1

Alam, M. Kathleen. Multivariate Analysis of Seismic Field Data. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/8993.

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Chen, Maximillian Gene, Kristin Marie Divis, James D. Morrow, and Laura A. McNamara. Visualizing Clustering and Uncertainty Analysis with Multivariate Longitudinal Data. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1472228.

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DeJong, Stephanie, Rosalie Multari, Kelsey Wilson, and Paiboon Tangyunyong. Evaluation of COTS Electronics by Power Spectrum Analysis and Multivariate Data Analysis. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1890397.

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Wong, George Y. Statistical Analysis of Multivariate Interval Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada409921.

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5

Grunsky, E. Spatial factor analysis: a technique to assess the spatial relationships of multivariate data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128074.

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Wong, George Y. Statistical Analysis of Multivariate Interval-Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada418647.

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Wong, George. Statistical Analysis of Multivariate Interval-Censored Data in Breast Cancer Follow-Up Studies. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada390768.

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Mayer, B. P., D. A. Mew, A. DeHope, P. E. Spackman, and A. M. Williams. Identification of Chemical Attribution Signatures of Fentanyl Syntheses Using Multivariate Statistical Analysis of Orthogonal Analytical Data. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1366919.

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Mayer, B. P., C. A. Valdez, A. J. DeHope, P. E. Spackman, R. D. Sanner, H. P. Martinez, and A. M. Williams. Multivariate Statistical Analysis of Orthogonal Mass Spectral Data for the Identification of Chemical Attribution Signatures of 3-Methylfentanyl. Office of Scientific and Technical Information (OSTI), November 2016. http://dx.doi.org/10.2172/1335778.

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Hassena, Amal ben, Hanen Sellami, Abdelkader Bougarech, Morsi Gdoura, Caroline Amiel, and Radhouane Gdoura. Differentiation of the Salmonella enterica Serovars Enteritidis and Kentucky Using Transmittance and Reflectance FTIR Spectroscopies and Multivariate Data Analysis. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, April 2021. http://dx.doi.org/10.7546/crabs.2021.04.14.

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