Journal articles on the topic '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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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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Legendre, Pierre, and Olivier Gauthier. "Statistical methods for temporal and space–time analysis of community composition data." Proceedings of the Royal Society B: Biological Sciences 281, no. 1778 (March 7, 2014): 20132728. http://dx.doi.org/10.1098/rspb.2013.2728.
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This review focuses on the analysis of temporal beta diversity, which is the variation in community composition along time in a study area. Temporal beta diversity is measured by the variance of the multivariate community composition time series and that variance can be partitioned using appropriate statistical methods. Some of these methods are classical, such as simple or canonical ordination, whereas others are recent, including the methods of temporal eigenfunction analysis developed for multiscale exploration (i.e. addressing several scales of variation) of univariate or multivariate response data, reviewed, to our knowledge for the first time in this review. These methods are illustrated with ecological data from 13 years of benthic surveys in Chesapeake Bay, USA. The following methods are applied to the Chesapeake data: distance-based Moran's eigenvector maps, asymmetric eigenvector maps, scalogram, variation partitioning, multivariate correlogram, multivariate regression tree, and two-way MANOVA to study temporal and space–time variability. Local (temporal) contributions to beta diversity (LCBD indices) are computed and analysed graphically and by regression against environmental variables, and the role of species in determining the LCBD values is analysed by correlation analysis. A tutorial detailing the analyses in the R language is provided in an appendix.
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Deimel, Klaus, Alina Gerke, and Greta Molinski. "Eine empirische Analyse der Bedeutung der strategischen Erfolgsfaktoren von Hidden Champions für mittelständische Unternehmen." ZfKE – Zeitschrift für KMU und Entrepreneurship 69, no. 2 (April 1, 2021): 67–96. http://dx.doi.org/10.3790/zfke.69.2.67.
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Die Implementierung strategischer Erfolgsfaktoren rückt zunehmend in den Fokus kleiner und mittelständischer Unternehmen. Vor dem Hintergrund des überdurchschnittlichen Erfolgs sogenannter Hidden Champions (HC) stellt sich unter einer praxisorientierten Perspektive die Frage, welche Bedeutung mittelständische Unternehmen grundsätzlich den von Hermann Simon identifizierten Erfolgsprinzipien für HC für den Unternehmenserfolg zumessen. Die empirische Studie analysierte dazu die Bedeutung dieser Erfolgsfaktoren für mittelständische Unternehmen und untersuchte, ob Bedeutungsunterschiede zwischen erfolgreichen und weniger erfolgreichen Unternehmen der Stichprobe existieren. Im Rahmen einer explorativen, multivariaten Datenanalyse konnten außerdem zwei Cluster, die „Internationalen Innovatoren“ und die „Nationalen Traditionalisten“, im Datensatz identifiziert werden, die sich hinreichend in der Bedeutungszumessung der Erfolgsfaktoren voneinander unterschieden. The implementation of strategic success factors is increasingly gaining the attention of small and medium-sized enterprises. In light of the above-average success of so-called hidden champions (HC), the question arises which importance medium-sized companies in general attach to the success principles for HC identified by Hermann Simon. This empirical study analyzed the importance of these success factors for small and medium-sized enterprises and investigated whether differences in relevance exist between successful and less successful companies in the sample. An explorative, multivariate data analysis also identified two clusters in the data set, the “international innovators” and the “national traditionalists”, which differed sufficiently from each other in the measurement of the importance of these success factors.
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Ali, Nairveen, Jeroen Jansen, André van den Doel, Gerjen Herman Tinnevelt, and Thomas Bocklitz. "WE-ASCA: The Weighted-Effect ASCA for Analyzing Unbalanced Multifactorial Designs—A Raman Spectra-Based Example." Molecules 26, no. 1 (December 25, 2020): 66. http://dx.doi.org/10.3390/molecules26010066.
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Analyses of multifactorial experimental designs are used as an explorative technique describing hypothesized multifactorial effects based on their variation. The procedure of analyzing multifactorial designs is well established for univariate data, and it is known as analysis of variance (ANOVA) tests, whereas only a few methods have been developed for multivariate data. In this work, we present the weighted-effect ASCA, named WE-ASCA, as an enhanced version of ANOVA-simultaneous component analysis (ASCA) to deal with multivariate data in unbalanced multifactorial designs. The core of our work is to use general linear models (GLMs) in decomposing the response matrix into a design matrix and a parameter matrix, while the main improvement in WE-ASCA is to implement the weighted-effect (WE) coding in the design matrix. This WE-coding introduces a unique solution to solve GLMs and satisfies a constrain in which the sum of all level effects of a categorical variable equal to zero. To assess the WE-ASCA performance, two applications were demonstrated using a biomedical Raman spectral data set consisting of mice colorectal tissue. The results revealed that WE-ASCA is ideally suitable for analyzing unbalanced designs. Furthermore, if WE-ASCA is applied as a preprocessing tool, the classification performance and its reproducibility can significantly improve.
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Zhang, Yanli, Peng Liu, Yuanfeng Li, and Ai-Hua Zhang. "Exploration of metabolite signatures using high-throughput mass spectrometry coupled with multivariate data analysis." RSC Advances 7, no. 11 (2017): 6780–87. http://dx.doi.org/10.1039/c6ra27461g.
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Ding, Linfang, Liqiu Meng, Jian Yang, and Jukka M. Krisp. "Interactive visual exploration and analysis of origin-destination data." Proceedings of the ICA 1 (May 16, 2018): 1–5. http://dx.doi.org/10.5194/ica-proc-1-29-2018.
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In this paper, we propose a visual analytics approach for the exploration of spatiotemporal interaction patterns of massive origin-destination data. Firstly, we visually query the movement database for data at certain time windows. Secondly, we conduct interactive clustering to allow the users to select input variables/features (e.g., origins, destinations, distance, and duration) and to adjust clustering parameters (e.g. distance threshold). The agglomerative hierarchical clustering method is applied for the multivariate clustering of the origin-destination data. Thirdly, we design a parallel coordinates plot for visualizing the precomputed clusters and for further exploration of interesting clusters. Finally, we propose a gradient line rendering technique to show the spatial and directional distribution of origin-destination clusters on a map view. We implement the visual analytics approach in a web-based interactive environment and apply it to real-world floating car data from Shanghai. The experiment results show the origin/destination hotspots and their spatial interaction patterns. They also demonstrate the effectiveness of our proposed approach.
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Wang, Zhiliang, Yalin Sun, and Peng Li. "Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index." Discrete Dynamics in Nature and Society 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/365204.
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The main purpose of this paper is to explore the principle components of Shanghai stock exchange 50 index by means of functional principal component analysis (FPCA). Functional data analysis (FDA) deals with random variables (or process) with realizations in the smooth functional space. One of the most popular FDA techniques is functional principal component analysis, which was introduced for the statistical analysis of a set of financial time series from an explorative point of view. FPCA is the functional analogue of the well-known dimension reduction technique in the multivariate statistical analysis, searching for linear transformations of the random vector with the maximal variance. In this paper, we studied the monthly return volatility of Shanghai stock exchange 50 index (SSE50). Using FPCA to reduce dimension to a finite level, we extracted the most significant components of the data and some relevant statistical features of such related datasets. The calculated results show that regarding the samples as random functions is rational. Compared with the ordinary principle component analysis, FPCA can solve the problem of different dimensions in the samples. And FPCA is a convenient approach to extract the main variance factors.
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Sobczuk, Dorota, and Marcin Komosa. "Morphological Differentiation of Polish Arabian Horses - Multivariate Analysis." Bulletin of the Veterinary Institute in Pulawy 56, no. 4 (December 1, 2012): 623–29. http://dx.doi.org/10.2478/v10213-012-0110-5.
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Abstract The aim of the study was to show the variability of exterior type in Arabian horses bred in three leading stud farms in Poland. A total of 334 adult animals from the basic herd of stud farms in Bialka, Janow Podlaski, and Michalow were studied. Each horse underwent 26 exterior measurements. In the first stage, the method of exploration of multidimensional data - principal component analysis, was used, and subsequently, a stepwise canonical discriminant analysis. It was found that each stud farm breeds horses with a different metrical pattern. Horses bred in Bialka represent clearly different morphotype comparing to horses from the other stud farms. Individuals from Janow Podlaski are more similar to horses originating from Michalow but morphological variation also occurs between them. From among the 26 metric traits, 10 parameters have a meaningful discriminative power. These include in particular such parameters as the length of the metatarsus, pelvis, arm, and neck, and depth of the chest.
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Zhao, Zhonghai, Kai Qiao, Yiwen Liu, Jun Chen, and Chenglu Li. "Geochemical Data Mining by Integrated Multivariate Component Data Analysis: The Heilongjiang Duobaoshan Area (China) Case Study." Minerals 12, no. 8 (August 17, 2022): 1035. http://dx.doi.org/10.3390/min12081035.
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The Heilongjiang Duobaoshan area is located at the confluence of the Great Xing’an Range and the Lesser Xing’an Range, and the area has undergone a complex magmatic and tectonic evolutionary history resulting in a complex and diverse geological background for mineralization. As a result of this geological complexity and the multi-period nature of mineralization, the geochemical data of the area are usually not satisfied with a single statistical distribution form, so traditional statistical methods cannot adequately explore and identify the distribution of deep-seated information in the geochemical data. Based on the above problems, this paper adopts a multivariate component data analysis method to process 14 mass fraction data elements, namely Ag, As, Au, Bi, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Sb, W, and Zn, in the 1:50,000 soil geochemical data from the Duobaoshan area of Heilongjiang. The spatial distribution and internal structural characteristics of raw, logarithmic transformation and isometric logarithmic ratio (ILR) transformed data were compared using exploratory data analysis (EDA); robust principal component analysis (RPCA) was applied to obtain the PC1 and PC2 principal component combinations associated with mineralization, and a spectrum–area (S–A) fractal model was further used to decompose the geochemical anomalies of the PC1 and PC2 principal component combinations as composite anomalies. The results show the following: (i) The data transformed by the isometric logarithmic ratio (ILR) eliminate the influence of the original data closure effect, and the spatial scale of the data is more uniform; the data are approximately normally distributed, based on which RPCA can be applied to better explore the correlation between elements and the pattern of co-associated combinations. (ii) The S–A method was further used to decompose the composite anomalies of the PC1 and PC2 principal component combination in the study area. The anomalous and background fields of the screened-out PC1 and PC2 principal component combinations reflect anomalous information on mineralization dominated by Au mineralization. Moreover, the anomaly and background information after extraction were in good agreement with the known Au deposits (points), and many geochemical anomalies with prospecting potential were obtained in the periphery, providing a theoretical basis and exploration focus for the next step in the searching and exploring of the study area.
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Selvi, Huseyin Zahit, and Burak Caglar. "Using cluster analysis methods for multivariate mapping of traffic accidents." Open Geosciences 10, no. 1 (December 20, 2018): 772–81. http://dx.doi.org/10.1515/geo-2018-0060.
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Abstract Many factors affect the occurrence of traffic accidents. The classification and mapping of the different attributes of the resulting accident are important for the prevention of accidents. Multivariate mapping is the visual exploration of multiple attributes using a map or data reduction technique. More than one attribute can be visually explored and symbolized using numerous statistical classification systems or data reduction techniques. In this sense, clustering analysis methods can be used for multivariate mapping. This study aims to compare the multivariate maps produced by the K-means method, K-medoids method, and Agglomerative and Divisive Hierarchical Clustering (AGNES) method, which among clustering analysis methods, with real data. The results from the study will suggest which clustering methods should be preferred in terms of multivariate mapping. The results show that the K-medoids method is more appropriate in terms of clustering success. Moreover, the aim is to reveal spatial similarities in traffic accidents according to the results of traffic accidents that occur in different years. For this aim, multivariate maps created from traffic accident data of two different years in Turkey are used. The methods are compared, and the use of the maps produced with these methods for risk management and planning is discussed. Analysis of the maps reveals significant similarities for both years.
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Bernardi, Antonio. "Gruppi di reti di franchising e indicatori economici caratteristici." ECONOMIA E DIRITTO DEL TERZIARIO, no. 1 (October 2009): 7–45. http://dx.doi.org/10.3280/ed2009-001001.
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- The research presents a cluster analysis of networks of franchising with data taken from the business register of the main Italian association in the sector. The basic processed information is the count of networks, national outlets, outlets abroad, employees and the level in revenues earned in various economic sectors where the franchising is active. The previous variables, after an examination by Explorative Data Analysis, were used for the construction of some characteristic ratios relating to aspects of dimensions, efficiency and strategy of the franchisors and franchisees. The ratios, after a screening for detection of uni-multivariate outliers, became the new variables for the cluster analysis, iterated with a different methodology in order to guarantee the stability of the results. The analysis revealed four types of networks, with specific patterns of connection that, in general but a few cases, are due to a good degree of symmetry and balance between the dynamics of franchisors and franchisees.
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Soriano-Vargas, Aurea, Bernd Hamann, and Maria Cristina F de Oliveira. "TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data." Information Visualization 19, no. 1 (July 3, 2019): 3–23. http://dx.doi.org/10.1177/1473871619858937.
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We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.
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Wang, Changhe. "Exploration of Exercise Characteristics and Psychological Impact of Cold Urban Elderly Based on Multivariate Data Analysis." Wireless Communications and Mobile Computing 2022 (August 25, 2022): 1–8. http://dx.doi.org/10.1155/2022/7169789.
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Cold cities will cause a lot of inconvenience for the elderly to travel and exercise due to their low temperature, snowfall, freezing, and other climatic characteristics. On the basis of theoretical support, this paper firstly analyzes the physical, psychological, and behavioral characteristics of the elderly in cold cities and their impact on the design of small- and medium-sized sports venues from the physical needs of the elderly in cold cities. Secondly, based on architectural theory, combined with the research results of aging sociology, behavioral sociology, sports sociology, and other disciplines, combined with a large number of literature, examples, and practical research data, this paper summarizes the problems faced by middle-aged and elderly people in cold cities, constraints, advantages, and motivations for age-friendly design and problems in construction.
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Iyiola-Tunji, A. O., D. Baba, and W. Buba. "Explorative analysis of relationships among breed, growth traits, prices and sex of sheep using structural equation modeling." Nigerian Journal of Animal Production 47, no. 1 (December 19, 2020): 114–21. http://dx.doi.org/10.51791/njap.v47i1.195.
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The dynamics of factors that cumulate to determine the prices of sheep during festive periods (Eid al-Adha) are known. However, such factors that dictate prices during off-season (non-festivity periods) are not fully known. Structural equation modeling as a multivariate analytical tool is capable of bringing out the latent (hidden) factors responsible for price determination for sheep. Prices, body weight and linear body measurements of 621 sheep of both sexes consisting of Balami, Sudanese, Uda and Yankasa breeds were used to examine relationships among the variables. The sheep were obtained from Unguwa Uku, Dorayi, Kara, Bachirawa, Yankaba, Mariri and Kabara livestock markets in Municipal, Taruauni, Gmale, Nassarawa, Fagge and Dala Local Government Areas of Kano State. Body weights, neck length, back length, leg length, height at wither, loin girth and chest girth were parameters monitored. The data obtained were subjected to generalized least squares (GLS) estimator of a triangular seemingly unrelated regression (SUR) model. Maximum likelihood estimation method of structural equation modelling was used to generate parameter estimates through recursive system with correlated errors (SEM Command Language). Price of sheep is an observed variable while type and body parameters were generated as latent variables. Type as a latent variable had direct relationships with breed, sex and body weight class. Body parameter on the other hand had direct relationships with body weight and all the linear body measurements. The z-values were 2.9 for breed, 2.8 for sex and 2.5 for body weight class, others are 4.6 for birth weight, 1.9 for neck length, 4.1 for back length, 6.8 for loin, 7.4 for chest girth, 8.3 for height at wither and 6.1 for leg length. Simultaneous prediction equations for estimating prices of sheep had been generated for some selected livestock markets in Kano State.
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Yu, Fengmin, Liming Liu, Nanxiang Yu, Lianghao Ji, and Dong Qiu. "A Method of L1-Norm Principal Component Analysis for Functional Data." Symmetry 12, no. 1 (January 20, 2020): 182. http://dx.doi.org/10.3390/sym12010182.
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Recently, with the popularization of intelligent terminals, research on intelligent big data has been paid more attention. Among these data, a kind of intelligent big data with functional characteristics, which is called functional data, has attracted attention. Functional data principal component analysis (FPCA), as an unsupervised machine learning method, plays a vital role in the analysis of functional data. FPCA is the primary step for functional data exploration, and the reliability of FPCA plays an important role in subsequent analysis. However, classical L2-norm functional data principal component analysis (L2-norm FPCA) is sensitive to outliers. Inspired by the multivariate data L1-norm principal component analysis methods, we propose an L1-norm functional data principal component analysis method (L1-norm FPCA). Because the proposed method utilizes L1-norm, the L1-norm FPCs are less sensitive to the outliers than L2-norm FPCs which are the characteristic functions of symmetric covariance operator. A corresponding algorithm for solving the L1-norm maximized optimization model is extended to functional data based on the idea of the multivariate data L1-norm principal component analysis method. Numerical experiments show that L1-norm FPCA proposed in this paper has a better robustness than L2-norm FPCA, and the reconstruction ability of the L1-norm principal component analysis to the original uncontaminated functional data is as good as that of the L2-norm principal component analysis.
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Feizi, Faranak, Amir Abbas Karbalaei-Ramezanali, and Sasan Farhadi. "Application of multivariate regression on magnetic data to determine further drilling site for iron exploration." Open Geosciences 13, no. 1 (January 1, 2021): 138–47. http://dx.doi.org/10.1515/geo-2020-0165.
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Abstract In this study, a new approach of the multivariate regression model has been applied to make a precise mathematical model to determine further drilling for the detailed iron exploration in the Koohbaba area, Northwest of Iran. Furthermore, to figure out the additional drilling locations, the ore length to the total core ratio for the drilled boreholes has been used based on the geophysical exploration dataset. Hence, different regression analyses including linear, cubic, and quadratic models have been applied. In this study, the ore length to the total core ratio of the chosen drilled boreholes has been considered as a dependent variable; besides, the outputs of the magnetic data using the UP10 (10m upward-continuation), RTP (reduction to the pole), and A.S. (analytic signal) techniques have been designated as independent variables. Based on probability value (p-value), coefficients of determination (R 2 and R adj 2 {R}_{\text{adj}}^{2} ), and efficiency formula (EF), the fourth regression model has revealed the best results. The accuracy of the model has been confirmed by the defined ratio of boreholes and demonstrated by four additional drilled boreholes in the study area. Therefore, the results of the regression analysis are reasonable and can be used to determine the additional drilling for the detailed exploration.
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Liu, Zhicheng, Shamkant B. Navathe, and John T. Stasko. "Ploceus: Modeling, visualizing, and analyzing tabular data as networks." Information Visualization 13, no. 1 (June 5, 2013): 59–89. http://dx.doi.org/10.1177/1473871613488591.
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Tabular data are pervasive. Although tables often describe multivariate data without explicit definitions of a network, it may be advantageous to explore the data by modeling it as a graph or network for analysis. Even when a given table design specifies a network structure, analysts may want to look at multiple networks from different perspectives, at different levels of abstraction, and with different edge semantics. We present a system called Ploceus that offers a general approach for performing multidimensional and multilevel network–based visual analysis on multivariate tabular data. Powered by an underlying relational algebraic framework, Ploceus supports flexible construction and transformation of networks through a direct manipulation interface and integrates dynamic network manipulation with visual exploration through immediate feedback mechanisms. We report our findings on the learnability and usability of Ploceus and propose a model of user actions in visualization construction using Ploceus.
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Guo, Genmao, Qing Huang, Fangming Jin, Linyi Lin, Qingqing Wang, Qionglin Fu, Yin Liu, et al. "Exploration of the Interrelationship within Biomass Pyrolysis Liquid Composition Based on Multivariate Analysis." Molecules 27, no. 17 (September 2, 2022): 5656. http://dx.doi.org/10.3390/molecules27175656.
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The diverse utilization of pyrolysis liquid is closely related to its chemical compositions. Several factors affect PA compositions during the preparation. In this study, multivariate statistical analysis was conducted to assess PA compositions data obtained from published paper and experimental data. Results showed the chemical constituents were not significantly different in different feedstock materials. Acids and phenolics contents were 31.96% (CI: 25.30–38.62) and 26.50% (CI: 21.43–31.57), respectively, accounting for 58.46% (CI: 46.72–70.19) of the total relative contents. When pyrolysis temperatures range increased to above 350 °C, acids and ketones contents decreased by more than 5.2-fold and 1.53-fold, respectively, whereas phenolics content increased by more than 2.1-fold, and acetic acid content was the highest, reaching 34.16% (CI: 25.55–42.78). Correlation analysis demonstrated a significantly negative correlation between acids and phenolics (r2 = −0.43, p < 0.001) and significantly positive correlation between ketones and alcohols (r2 = 0.26, p < 0.05). The pyrolysis temperatures had a negative linear relationship with acids (slope = −0.07, r2 = 0.16, p < 0.001) and aldehydes (slope = −0.02, r2 = 0.09, p < 0.05) and positive linear relationship with phenolics (slope = 0.04, r2 = 0.07, p < 0.05). This study provides a theoretical reference of PA application.
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Canfora, Alfonso, Antonio Ferronetti, Gianpaolo Marte, Vittorio Di Maio, Claudio Mauriello, Pietro Maida, Vincenzo Bottino, Giovanni Aprea, and Bruno Amato. "Predictive factors of intestinal necrosis in acute mesenteric ischemia." Open Medicine 14, no. 1 (December 17, 2019): 883–89. http://dx.doi.org/10.1515/med-2019-0104.
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AbstractObjectivesAcute mesenteric ischemia (AMI) is a gastrointestinal and vascular emergency in which the detection of patients requiring intestinal resection is mandatory.MethodsRegistered data of 55 consecutive patients admitted to our center between January 2010 and December 2016 that underwent an explorative laparotomy for a suspected diagnosis of irreversible transmural intestinal necrosis (ITIN) were analyzed. Demographic, clinical, laboratory and CT findings were statistically analyzed in order to search predictive factors of ITIN and their correlation to its clinical spectre.ResultsTobacco use was the most statistically significant (p<0.01) cardiovascular disease risk factor involved in ITIN. Among lab tests, Serum lactate levels ˃ 2mmol/L resulted in a statistically significant association with ITIN (p=0.0001). Organ failure (defined as Marshall score> 2) and the three main CT findings (decreased bowel wall enhancement, bowel loop dilation and demonstrated vessel occlusion) were strongly associated with ITIN (p values: 0.001, 0.007, 0.0013, 0.0005). Only serum lactate levels>2 mmol/L resulted as statistically significant as predictive factors of ITIN in multivariate analysis using logistic regression (OR 49.66 and p-value 0.0021).ConclusionOur univariate and multivariate analysis identified multiple factors (Serum lactate levels ˃ 2mmol/L, Organ failure, CT signs) that could suggest patients that require a surgical approach for ITIN.
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Moen, Birgitte, Astrid Oust, Øyvind Langsrud, Nick Dorrell, Gemma L. Marsden, Jason Hinds, Achim Kohler, Brendan W. Wren, and Knut Rudi. "Explorative Multifactor Approach for Investigating Global Survival Mechanisms of Campylobacter jejuni under Environmental Conditions." Applied and Environmental Microbiology 71, no. 4 (April 2005): 2086–94. http://dx.doi.org/10.1128/aem.71.4.2086-2094.2005.
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ABSTRACT Explorative approaches such as DNA microarray experiments are becoming increasingly important in microbial research. Despite these major technical advancements, approaches to study multifactor experiments are still lacking. We have addressed this problem by using rotation testing and a novel multivariate analysis of variance (MANOVA) approach (50-50 MANOVA) to investigate interacting experimental factors in a complex experimental design. Furthermore, a new rotation testing based method was introduced to calculate false-discovery rates for each response. This novel analytical concept was used to investigate global survival mechanisms in the environment of the major food-borne pathogen C. jejuni. We simulated nongrowth environmental conditions by investigating combinations of the factors temperature (5 and 25°C) and oxygen tension (anaerobic, microaerobic, and aerobic). Data were generated with DNA microarrays for information about gene expression patterns and Fourier transform infrared (FT-IR) spectroscopy to study global macromolecular changes in the cell. Microarray analyses showed that most genes were either unchanged or down regulated compared to the reference (day 0) for the conditions tested and that the 25°C anaerobic condition gave the most distinct expression pattern with the fewest genes expressed. The few up-regulated genes were generally stress related and/or related to the cell envelope. We found, using FT-IR spectroscopy, that the amount of polysaccharides and oligosaccharides increased under the nongrowth survival conditions. Potential mechanisms for survival could be to down regulate most functions to save energy and to produce polysaccharides and oligosaccharides for protection against harsh environments. Basic knowledge about the survival mechanisms is of fundamental importance in preventing transmission of this bacterium through the food chain.
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Gu, Xiaomeng, Andrew Metcalfe, Nigel Cook, Chris Aldrich, and L. George. "Exploratory analysis of multivariate drill core time series measurements." ANZIAM Journal 63 (January 10, 2023): C208—C230. http://dx.doi.org/10.21914/anziamj.v63.17192.
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Demand for mineral resources is increasing, necessitating exploitation of lower grade and more heterogeneous orebodies. The high variability inherent in such orebodies leads to an increase in the cost, complexity and environmental footprint associated with mining and mineral processing. Enhanced knowledge of orebody characteristics is thus vital for mining companies to optimize profitability. We present a pilot study to investigate prediction of geometallurgical variables from drill sensor data. A comparison is made of the performance of multilayer perceptron (MLP) and multiple linear regression models (MLR) for predicting a geometallurgical variable. This comparison is based on simulated data that are physically realistic, having been derived from models fitted to the one available drill core. The comparison is made in terms of the mean and standard deviation (over repeated samples from the population) of the mean absolute error, root mean square error, and coefficient of determination. The best performing model depends on the form of the response variable and the sample size. The standard deviation of performance measures tends to be higher for the MLP, and MLR appears to offer a more consistent performance for the test cases considered. References R. M. Balabin and S. V. Smirnov. Interpolation and extrapolation problems of multivariate regression in analytical chemistry: Benchmarking the robustness on near-infrared (NIR) spectroscopy data”. Analyst 137.7 (2012), pp. 1604–1610. doi: 10.1039/c2an15972d C. M. Bishop. Pattern recognition and machine learning. Springer, 2006. url: https://link.springer.com/book/9780387310732 J. B. Boisvert, M. E. Rossi, K. Ehrig, and C. V. Deutsch. Geometallurgical modeling at Olympic dam mine, South Australia”. Math. Geosci. 45 (2013), pp. 901–925. doi: 10.1007/s11004-013-9462-5 T. Bollerslev. Generalized autoregressive conditional heteroskedasticity”. J. Economet. 31.3 (1986), pp. 307–327. doi: 10.1016/0304-4076(86)90063-1 C. Both and R. Dimitrakopoulos. Applied machine learning for geometallurgical throughput prediction—A case study using production data at the Tropicana Gold Mining Complex”. Minerals 11.11 (2021), p. 1257. doi: 10.3390/min11111257 J. Chen and G. Li. Tsallis wavelet entropy and its application in power signal analysis”. Entropy 16.6 (2014), pp. 3009–3025. doi: 10.3390/e16063009 S. Coward, J. Vann, S. Dunham, and M. Stewart. The primary-response framework for geometallurgical variables”. Seventh international mining geology conference. 2009, pp. 109–113. https://www.ausimm.com/publications/conference->url: https://www.ausimm.com/publications/conference- proceedings/seventh-international-mining-geology- conference-2009/the-primary-response-framework-for- geometallurgical-variables/ A. C. Davis and N. B. Christensen. Derivative analysis for layer selection of geophysical borehole logs”. Comput. Geosci. 60 (2013), pp. 34–40. doi: 10.1016/j.cageo.2013.06.015 C. Dritsaki. An empirical evaluation in GARCH volatility modeling: Evidence from the Stockholm stock exchange”. J. Math. Fin. 7.2 (2017), pp. 366–390. doi: 10.4236/jmf.2017.72020 R. F. Engle and T. Bollerslev. Modelling the persistence of conditional variances”. Econ. Rev. 5.1 (1986), pp. 1–50. doi: 10.1080/07474938608800095 A. S. Hadi and R. F. Ling. Some cautionary notes on the use of principal components regression”. Am. Statistician 52.4 (1998), pp. 15–19. doi: 10.2307/2685559 J. Hunt, T. Kojovic, and R. Berry. Estimating comminution indices from ore mineralogy, chemistry and drill core logging”. The Second AusIMM International Geometallurgy Conference (GeoMet) 2013. 2013, pp. 173–176. http://ecite.utas.edu.au/89773>url: http://ecite.utas.edu.au/89773 on p. C210). R. Hyndman, Y. Kang, P. Montero-Manso, T. Talagala, E. Wang, Y. Yang, M. O’Hara-Wild, S. Ben Taieb, H. Cao, D. K. Lake, N. Laptev, and J. R. Moorman. tsfeatures: Time series feature extraction. R package version 1.0.2. 2020. https://CRAN.R-project.org/package=tsfeatures>url: https://CRAN.R-project.org/package=tsfeatures on p. C222). C. L. Johnson, D. A. Browning, and N. E. Pendock. Hyperspectral imaging applications to geometallurgy: Utilizing blast hole mineralogy to predict Au-Cu recovery and throughput at the Phoenix mine, Nevada”. Econ. Geol. 114.8 (2019), pp. 1481–1494. doi: 10.5382/econgeo.4684 E. B. Martin and A. J. Morris. An overview of multivariate statistical process control in continuous and batch process performance monitoring”. Trans. Inst. Meas. Control 18.1 (1996), pp. 51–60. doi: 10.1177/014233129601800107 E. Sepulveda, P. A. Dowd, C. Xu, and E. Addo. Multivariate modelling of geometallurgical variables by projection pursuit”. Math. Geosci. 49.1 (2017), pp. 121–143. doi: 10.1007/s11004-016-9660-z S. J. Webb, G. R. J. Cooper, and L. D. Ashwal. Wavelet and statistical investigation of density and susceptibility data from the Bellevue drill core and Moordkopje borehole, Bushveld Complex, South Africa”. SEG Technical Program Expanded Abstracts 2008. Society of Exploration Geophysicists, 2008, pp. 1167–1171. doi: 10.1190/1.3059129 R. Zuo. Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China)”. J. Geochem. 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Mansouri, Edris, Faranak Feizi, Alireza Jafari Rad, and Mehran Arian. "Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran." Solid Earth 9, no. 2 (March 28, 2018): 373–84. http://dx.doi.org/10.5194/se-9-373-2018.
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Abstract. This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).
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Lucchesi, Simone, Simone Furini, Donata Medaglini, and Annalisa Ciabattini. "From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies." Vaccines 8, no. 1 (March 20, 2020): 138. http://dx.doi.org/10.3390/vaccines8010138.
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Flow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data. The potentialities of automated analyses range from the improvement of the data quality in the pre-processing steps up to the unbiased, data-driven examination of complex datasets using a variety of algorithms based on different approaches. In this review, an overview of the automated analysis pipeline is provided, spanning from the pre-processing phase to the automated population analysis. Analysis based on computational tools might overcame both the subjectivity of manual gating and the operator-biased exploration of expected populations. Examples of applications of automated tools that have successfully improved the characterization of different cell populations in vaccination studies are also presented.
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Delev, Daniel, Anna Pavlova, Alexander Grote, Azize Boström, Anke Höllig, Johannes Schramm, Rolf Fimmers, Johannes Oldenburg, and Matthias Simon. "NOTCH4 gene polymorphisms as potential risk factors for brain arteriovenous malformation development and hemorrhagic presentation." Journal of Neurosurgery 126, no. 5 (May 2017): 1552–59. http://dx.doi.org/10.3171/2016.3.jns151731.
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OBJECTIVEArteriovenous malformations (AVMs) of the brain are a frequent and important cause of intracranial hemorrhage in young adults. Little is known about the molecular-genetic pathomechanisms underlying AVM development. Genes of the NOTCH family control the normal development of vessels and proper arteriovenous specification. Transgenic mice with constitutive expression of active NOTCH4 frequently develop AVMs. Here, the authors report a genetic association study investigating possible associations between NOTCH4 gene polymorphisms and formation and clinical presentation of AVMs.METHODSAfter PCR amplification and direct DNA sequencing or restriction digests, 10 single-nucleotide polymorphisms (SNPs) of the NOTCH4 gene were used for genotyping 153 AVM patients and 192 healthy controls (i.e., blood donors). Pertinent clinical data were available for 129 patients. Uni- and multivariate single-marker and explorative haplotype analyses were performed to identify potential genetic risk factors for AVM development and for hemorrhagic or epileptic presentation.RESULTSEleven calculated haplotypes consisting of 3–4 SNPs (most of which were located in the epidermal growth factor–like domain of the NOTCH4 gene) were observed significantly more often among AVM patients than among controls. Univariate analysis indicated that rs443198_TT and rs915895_AA genotypes both were significantly associated with hemorrhage and that an rs1109771_GG genotype was associated with epilepsy. The association between rs443198_TT and AVM bleeding remained significant in the multivariate regression analysis.CONCLUSIONSThe authors' results suggest NOTCH4 SNPs as possible genetic risk factors for the development and clinical presentation of AVMs and a role of NOTCH4 in the pathogenesis of this disease.
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He, Ke, and Cheng Kang Wang. "A Preliminary Exploration on Digitization Analysis for Spatial Form of Ancient City Wall in Southeast of China Based on Multivariate Analysis." Applied Mechanics and Materials 638-640 (September 2014): 2189–95. http://dx.doi.org/10.4028/www.scientific.net/amm.638-640.2189.
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The harmonious development of ancient city walls in China and their surrounding spatial forms faces a very complicated situation. In this study, the multivariate analysis method is used to coordinate, summarize and analyze the complicated influencing factors for objective data support, providing the objective reference and theoretical pattern to the reasonable protection and sustainable development for ancient city wall and its surrounding space. As a preliminary exploration of the study, this paper aims to establish the overall study framework.
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Ghorbani, Zohreh, Fatemeh Gholizadeh, Juliana Casali, Chunyi Hao, Hannah E. Cavallin, Lisa L. Van Loon, and Neil R. Banerjee. "Application of multivariate data analysis to biogeochemical exploration at the Twin Lakes Deposit, Monument Bay Gold Project, Manitoba, Canada." Chemical Geology 593 (March 2022): 120739. http://dx.doi.org/10.1016/j.chemgeo.2022.120739.
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Kukreti, B. M., Pradeep Pandey, and R. V. Singh. "Multivariate analysis of subsurface radiometric data in Rongsohkham area, East Khasi Hills district, Meghalaya (India): Implication on uranium exploration." Applied Radiation and Isotopes 70, no. 8 (August 2012): 1644–48. http://dx.doi.org/10.1016/j.apradiso.2012.04.025.
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Zhao, Ming, Gerard Downey, and Colm P. O'Donnell. "Exploration of microwave dielectric and near infrared spectroscopy with multivariate data analysis for fat content determination in ground beef." Food Control 68 (October 2016): 260–70. http://dx.doi.org/10.1016/j.foodcont.2016.03.031.
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Almuqbil, Rashed M., Nagaraja Sreeharsha, and Anroop B. Nair. "Formulation-by-Design of Efinaconazole Spanlastic Nanovesicles for Transungual Delivery Using Statistical Risk Management and Multivariate Analytical Techniques." Pharmaceutics 14, no. 7 (July 6, 2022): 1419. http://dx.doi.org/10.3390/pharmaceutics14071419.
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As regulatory and technical landscapes for pharmaceutical formulation development are rapidly evolving, a risk-management approach using multivariate analysis is highly essential for designing a product with requisite critical quality attributes (CQA). Efinaconazole, a newly approved poorly water-soluble antifungal triazole drug has poor permeability. Spanlastics, new-generation surfactant nanovesicles, being fluidic, help improve the permeability of drugs. Therefore, we optimized efinaconazole spanlastics using the concepts of Formulation-by-Design (FbD) and explored the feasibility of transungual delivery for the management of onychomycosis. Using the Ishikawa fishbone diagram, the risk factors that may have an impact on the CQA of efinaconazole spanlastic vesicles were identified. Application of the Plackett–Burman experimental design facilitated the screening of eight different formulation and process parameters influencing particle size, transmittance, relative deformability, zeta potential, entrapment efficiency, and dissolution efficiency. With the help of Pareto charts, the three most significant factors were identified, viz., vesicle builder (Span), edge activator (Tween), and mixing time. The levels of these three critical variables were optimized by FbD to reduce the particle size and maximize the transparency, relative deformability, encapsulation efficiency, and dissolution efficiency of efinaconazole spanlastic nanovesicles. Bayesian and Lenth’s analysis and mathematical modeling of the experimental data helped to quantify the critical formulation attributes required for getting the formulation with optimum quality features. The optimized efinaconazole-loaded spanlastic vesicles had a particle size of 197 nm, transparency of 91%, relative deformability of 12.5 min, and dissolution efficiency of 81.23%. The spanlastic formulation was incorporated into a gel and explored ex vivo for transungual delivery. This explorative study provides an example of the application of principles of risk management, statistical multivariate analysis, and the FbD approach in developing efinaconazole spanlastic nanovesicles.
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Michelaki, Kostalena, Michael J. Hughes, and Ronald G. V. Hancock. "On establishing ceramic chemical groups: exploring the influence of data analysis methods and the role of the elements chosen in analysis." Open Journal of Archaeometry 1, no. 1 (June 27, 2013): 1. http://dx.doi.org/10.4081/arc.2013.e1.
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Since the 1970s, archaeologists have increasingly depended on archaeometric rather than strictly stylistic data to explore questions of ceramic provenance and technol- ogy, and, by extension, trade, exchange, social networks and even identity. It is accepted as obvious by some archaeometrists and statisti- cians that the results of the analyses of compo- sitional data may be dependent on the format of the data used, on the data exploration method employed and, in the case of multivari- ate analyses, even on the number of elements considered. However, this is rarely articulated clearly in publications, making it less obvious to archaeologists. In this short paper, we re- examine compositional data from a collection of bricks, tiles and ceramics from Hill Hall, near Epping in Essex, England, as a case study to show how the method of data exploration used and the number of elements considered in multivariate analyses of compositional data can affect the sorting of ceramic samples into chemical groups. We compare bivariate data splitting (BDS) with principal component analysis (PCA) and centered log ratio-principal component analysis (CLR-PCA) of different unstandardized data formats [original concen- tration data and logarithmically transformed (i.e. log10 data)], using different numbers of elements. We confirm that PCA, in its various forms, is quite sensitive to the numbers and types of elements used in data analysis.
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PFANNKUCH, MAXINE, and AMANDA RUBICK. "AN EXPLORATION OF STUDENTS’ STATISTICAL THINKING WITH GIVEN DATA." STATISTICS EDUCATION RESEARCH JOURNAL 1, no. 2 (December 29, 2002): 4–21. http://dx.doi.org/10.52041/serj.v1i2.562.
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This paper examines how two twelve-year-old students built up their recognition and understanding of relationships in a set of data. Using a small multivariate dataset created by Watson, Collis, Callingham and Moritz (1995), the students conducted an investigation of their choice in a pencil-and-paper environment. The students’ thinking across the three representations of cards, tables and graphs is analysed from the perspectives of transnumeration, consideration of variation, reasoning with statistical models, and integrating the statistical with the contextual, which were identified as fundamental statistical thinking elements in empirical enquiry in the framework of Wild and Pfannkuch (1999). The ways of thinking within each element across the representations are identified. In the analysis, references are also made to the types of statistical thinking present in the other ten students in the study. From the analysis we identified five issues that should be considered for determining how students construct meanings from data. They are: prior contextual and statistical knowledge; thinking at a higher level than constructed representations; actively representing and construing; the intertwinement of local and global thinking; and the changing statistical thinking dialogue across the representations.
First published December 2002 at Statistics Education Research Journal: Archives
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Luna, Augustin, Fathi Elloumi, Sudhir Varma, Yanghsin Wang, Vinodh N. Rajapakse, Mirit I. Aladjem, Jacques Robert, Chris Sander, Yves Pommier, and William C. Reinhold. "CellMiner Cross-Database (CellMinerCDB) version 1.2: Exploration of patient-derived cancer cell line pharmacogenomics." Nucleic Acids Research 49, no. D1 (November 16, 2020): D1083—D1093. http://dx.doi.org/10.1093/nar/gkaa968.
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Abstract CellMiner Cross-Database (CellMinerCDB, discover.nci.nih.gov/cellminercdb) allows integration and analysis of molecular and pharmacological data within and across cancer cell line datasets from the National Cancer Institute (NCI), Broad Institute, Sanger/MGH and MD Anderson Cancer Center (MDACC). We present CellMinerCDB 1.2 with updates to datasets from NCI-60, Broad Cancer Cell Line Encyclopedia and Sanger/MGH, and the addition of new datasets, including NCI-ALMANAC drug combination, MDACC Cell Line Project proteomic, NCI-SCLC DNA copy number and methylation data, and Broad methylation, genetic dependency and metabolomic datasets. CellMinerCDB (v1.2) includes several improvements over the previously published version: (i) new and updated datasets; (ii) support for pattern comparisons and multivariate analyses across data sources; (iii) updated annotations with drug mechanism of action information and biologically relevant multigene signatures; (iv) analysis speedups via caching; (v) a new dataset download feature; (vi) improved visualization of subsets of multiple tissue types; (vii) breakdown of univariate associations by tissue type; and (viii) enhanced help information. The curation and common annotations (e.g. tissues of origin and identifiers) provided here across pharmacogenomic datasets increase the utility of the individual datasets to address multiple researcher question types, including data reproducibility, biomarker discovery and multivariate analysis of drug activity.
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Saalbach, Axel, Jörg Ontrup, Helge Ritter, and Tim W. Nattkemper. "Image Fusion Based on Topographic Mappings Using the Hyperbolic Space." Information Visualization 4, no. 4 (October 13, 2005): 266–75. http://dx.doi.org/10.1057/palgrave.ivs.9500106.
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The analysis of multivariate image data is a field of research that is becoming increasingly important in a broad range of applications from remote sensing to medical imaging. While traditional scientific visualization techniques are often not suitable for the analysis of this kind of data, methods of image fusion have evolved as a promising approach for synergistic data integration. In this paper, a new approach for the analysis of multivariate image data by means of image fusion is presented, which employs topographic mapping techniques based on non-Euclidean geometry. The hyperbolic self-organizing map (HSOM) facilitates the exploration of high-dimensional data and provides an interface in the tradition of distortion-oriented presentation techniques. For the analysis of hidden patterns and spatial relationships, the HSOM gives rise to an intuitive and efficient framework for the dynamic visualization of multivariate image data by means of color. In an application, the hyperbolic data explorer (HyDE) is employed for the visualization of image data from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Using 12 image sequences from breast cancer research, the method is introduced by different visual representations of the data and is also quantitatively analyzed. The HSOM is compared to different standard classifiers and evaluated with respect to topology preservation.
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Zhang, Huijie, Ke Ren, Yiming Lin, Dezhan Qu, and Zhenxin Li. "AirInsight: Visual Exploration and Interpretation of Latent Patterns and Anomalies in Air Quality Data." Sustainability 11, no. 10 (May 23, 2019): 2944. http://dx.doi.org/10.3390/su11102944.
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Nowadays, huge volume of air quality data provides unprecedented opportunities for analyzing pollution. However, due to the high complexity, most traditional analytical methods focus on abstracting data, so these techniques discard the original structure and limit the understanding of the results. Visual analysis is a powerful technique for exploring unknown patterns since it retains the details of the original data and gives visual feedback to users. In this paper, we focus on air quality data and propose the AirInsight design, an interactive visual analytic system for recognizing, exploring, and summarizing regular patterns, as well as detecting, classifying, and interpreting abnormal cases. Based on the time-varying and multivariate features of air quality data, a dimension reduction method Composite Least Square Projection (CLSP) is proposed, which allows appreciating and interpreting the data patterns in the context of attributes. On the basis of the observed regular patterns, multiple abnormal cases are further detected, including the multivariate anomalies by the proposed Noise Hierarchical Clustering (NHC) method, abruptly changing timestamps by Time diversity (TD) indicator, and cities with unique patterns by the Geographical Surprise (GS) measure. Moreover, we combine TD and GS to group anomalies based on their underlying spatiotemporal correlations. AirInsight includes multiple coordinated views and rich interactive functions to provide contextual information from different aspects and facilitate a comprehensive understanding. In particular, a pair of glyphs are designed that provide a visual representation of the temporal variation in air quality conditions for a user-selected city. Experiments show that CLSP improves the accuracy of Least Square Projection (LSP) and that NHC has the ability to separate noises. Meanwhile, several case studies and task-based user evaluation demonstrate that our system is effective and practical for exploring and interpreting multivariate spatiotemporal patterns and anomalies in air quality data.
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Tatarczak, Anna, and Oleksandra Boichuk. "The multivariate techniques in evaluation of unemployment analysis of Polish regions." Oeconomia Copernicana 9, no. 3 (September 30, 2018): 361–80. http://dx.doi.org/10.24136/oc.2018.018.
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Research background: The labour market situation is considered to be the most widely discussed part of economic development. However, it should be noted that the unemployment situation of young people (aged 15–24 years) in Poland in general terms seems to be problematic. Overall, the unemployment rate among young people in Poland is significantly higher than the overall unemployment rate in the EU. Moreover, the situation varies greatly across the regions.
Purpose of the article: Using multivariate techniques as a theoretical framework, the main goal of the paper is to identify groups of Polish regions that share similar patterns regarding unemployment among young people. To reach this goal, first a set of labour market indicators were selected. Next, the authors compared the labour market situation of young people between the Polish regions in 2005 and in 2014. Finally, the conclusions regarding the conducted analysis are explored.
Methods: The initial calculation is based on the concept of the taxonomic measure developed by Hellwig. The final method used to create clusters of objects (across 16 voivodeships of Poland) is cluster analysis. A segmentation of the voivodeships is observed for the years 2005 and 2014, based on selected indicators to determine the labour market situation. The data was gathered from the databases of the Central Statistical Office of Poland and Eurostat.
Findings & Value added: Through the exploration of the advantages of multivariate methods, the nature of youth unemployment is revealed in more detail. Indeed, dendrogram analysis divided the voivodeships into five groups, which are characterized by similar features associated with the labour market. It was found that the groups which emerged in 2005 have a different composition of regions than in 2014; this difference seems to be connected with the economic crisis.
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Liberati, Claudio, Concetta Cardillo, and Antonella Di Fonzo. "Sustainability and competitiveness in farms: An evidence of Lazio region agriculture through FADN data analysis." Economia agro-alimentare, no. 3 (January 2022): 1–22. http://dx.doi.org/10.3280/ecag2021oa12767.
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The new legislative proposals related to the Common Agricultural Policy (CAP) reform 2021-2027 aim to promote a sustainable and competitive agricultural sector. The new CAP supports agriculture in making a much stronger contribution to climate, biodiversity, environment and improving farms' competitiveness in the agri-food sector, in a European context.The importance of a strong focus on results and performance in the CAP legislation requires a continuous assessment and monitoring of the effectiveness of the measures adopted in the Rural Development Programs (RDP) with respect to the specific goals set during the cap program. In order to assess the progress in improving the competitiveness and sustainability of the agri-food sector in reaching their targets and the objectives of the CAP, the need arises to investigate whether the RDP measures contribute to supporting the transition towards sustainable agriculture, to the competitiveness of the agri-food sector and to a balanced development of the rural areas. In this new legislative framework, where it becomes important to evaluate whether the CAP provides a much stronger contribution to achieving the specific objectives, our paper aims to describe agricultural sector in the Lazio region and to analyze the effects, in terms of sustainability and competitiveness, of the measures approved by RDP 2014-2020, which have almost expired. In particular, we provide a comparative analysis of the data collected by the Farm Accountancy Data Network (FADN), focusing on two different periods: one prior to the last programming and one referring to the latest available data. The collected data refer to farms, regarding their structural, economic, financial and patrimonial characteristics, as well as variables that describe attitudes and behaviour towards the environment. A multivariate analysis (clustering) is applied; it focuses on explorative factor analysis based on principal components, in order to identify homogeneous groups of farms with sustainability and competitiveness and identify similar characteristics and potential for development trajectories. The results found that farms are moving towards more sustainable and multifunctional development paths. The assessment of EU goals for social, environmental, and economic sustainability in agriculture and rural areas are a basis for discussion among public decisionmakers involved in the reforming process of the explanatory measures of the new strategic objectives of the post-2020 CAP.Our results can offer a contribution to meeting the current challenges posed by the EU to ensure a smooth transition to the future CAP program. Major challenges that raise policy debate on the considerable potential of the FADN for assessing sustainability and farm competitiveness in the EU framework which places strong emphasis on results and performance.
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Wang, Sirao, Jiajuan Liang, Min Zhou, and Huajun Ye. "Testing Multivariate Normality Based on F-Representative Points." Mathematics 10, no. 22 (November 16, 2022): 4300. http://dx.doi.org/10.3390/math10224300.
Full textAbstract:
The multivariate normal is a common assumption in many statistical models and methodologies for high-dimensional data analysis. The exploration of approaches to testing multivariate normality never stops. Due to the characteristics of the multivariate normal distribution, most approaches to testing multivariate normality show more or less advantages in their power performance. These approaches can be classified into two types: multivariate and univariate. Using the multivariate normal characteristic by the Mahalanobis distance, we propose an approach to testing multivariate normality based on representative points of the simple univariate F-distribution and the traditional chi-square statistic. This approach provides a new way of improving the traditional chi-square test for goodness-of-fit. A limited Monte Carlo study shows a considerable power improvement of the representative-point-based chi-square test over the traditional one. An illustration of testing goodness-of-fit for three well-known datasets gives consistent results with those from classical methods.
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Gergely, Bence, and András Vargha. "How to use model-based cluster analysis efficiently in person-oriented research." Journal for Person-Oriented Research 7, no. 1 (August 26, 2021): 22–35. http://dx.doi.org/10.17505/jpor.2021.23449.
Full textAbstract:
Model-based cluster analysis (MBCA) was created to automatize the often subjective model-selection procedure of traditional explorative clustering methods. It is a type of finite mixture modelling, assuming that the data come from a mixture of different subpopulations following given distributions, typically multivariate normal. In that case cluster analysis is the exploration of the underlying mixture structure. In MBCA finding the possible number of clusters and the best clustering model is a statistical model-selection problem, where the models with differing number and type of component distributions are compared. For fitting a certain model MBCA uses a likelihood based Bayesian Information Criterion (BIC) to evaluate its appropriateness and the model with the highest BIC value is accepted as the final solution. The aim of the present study is to investigate the adequacy of automatic model selection in MBCA using BIC, and suggested alternative methods, like the Integrated Completed Likelihood Criterion (ICL), or Baudry’s method. An additional aim is to refine these procedures by using so called quality coefficients (QCs), borrowed from methodological advances within the field of exploratory cluster analysis, to help in the choice of an appropriate cluster structure (CLS), and also to compare the efficiency of MBCA in identifying a theoretical CLS with those of various other clustering methods. The analyses are restricted to studying the performance of various procedures of the type described above for two classification situations, typical in person-oriented studies: (1) an example data set characterized by a perfect theoretical CLS with seven types (seven completely homogeneous clusters) was used to generate three data sets with varying degrees of measurement error added to the original values, and (2) three additional data sets based on another perfect theoretical CLS with four types. It was found that the automatic decision rarely led to an optimal solution. However, dropping solutions with irregular BIC curves, and using different QCs as an aid in choosing between different solutions generated by MBCA and by fusing close clusters, optimal solutions were achieved for the two classification situations studied. With this refined procedure the revealed cluster solutions of MBCA often proved to be at least as good as those of different hierarchical and k-center clustering methods. MBCA was definitely superior in identifying four-type CLS models. In identifying seven-type CLS models MBCA performed at a similar level as the best of other clustering methods (such as k-means) only when the reliability level of the input variables was high or moderate, otherwise it was slightly less efficient.
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van Vliet, Marijn, Marc M. Van Hulle, and Riitta Salmelin. "Exploring the Organization of Semantic Memory through Unsupervised Analysis of Event-related Potentials." Journal of Cognitive Neuroscience 30, no. 3 (March 2018): 381–92. http://dx.doi.org/10.1162/jocn_a_01211.
Full textAbstract:
Modern multivariate methods have enabled the application of unsupervised techniques to analyze neurophysiological data without strict adherence to predefined experimental conditions. We demonstrate a multivariate method that leverages priming effects on the evoked potential to perform hierarchical clustering on a set of word stimuli. The current study focuses on the semantic relationships that play a key role in the organization of our mental lexicon of words and concepts. The N400 component of the event-related potential is considered a reliable neurophysiological response that is indicative of whether accessing one concept facilitates subsequent access to another (i.e., one “primes” the other). To further our understanding of the organization of the human mental lexicon, we propose to utilize the N400 component to drive a clustering algorithm that can uncover, given a set of words, which particular subsets of words show mutual priming. Such a scheme requires a reliable measurement of the amplitude of the N400 component without averaging across many trials, which was here achieved using a recently developed multivariate analysis method based on beamforming. We validated our method by demonstrating that it can reliably detect, without any prior information about the nature of the stimuli, a well-known feature of the organization of our semantic memory: the distinction between animate and inanimate concepts. These results motivate further application of our method to data-driven exploration of disputed or unknown relationships between stimuli.
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Rebiai, Abdelkrim, Bachir Ben Seghir, Hadia Hemmami, Soumeia Zeghoud, Ilham Ben Amor, Imane Kouadri, Mohammed Messaoudi, et al. "Quality Assessment of Medicinal Plants via Chemometric Exploration of Quantitative NMR Data: A Review." Compounds 2, no. 2 (June 13, 2022): 163–81. http://dx.doi.org/10.3390/compounds2020012.
Full textAbstract:
Since ancient times, herbal medicines (HM) have played a vital role in worldwide healthcare systems. It is therefore critical that a thorough evaluation of the quality and control of its complicated chemical makeup be conducted, in order to ensure its efficacy and safety. The notion of HM chemical prints, which aim to acquire a full characterization of compound chemical matrices, has become one of the most persuasive techniques for HM quality evaluation during the last few decades. The link between NMR and chemometrics is discussed in this article. The chemometric latent variable technique has been shown to be extremely valuable in inductive studies of biological systems as well as in solving industrial challenges. The results of unsupervised data exploration utilizing main component analysis as well as the multivariate curve resolution, were various. On the other hand, many contemporary NMR applications in metabolomics and quality control are based on supervised regression or classification analyses.
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Chang, Cheng-Fen, Jiun-Yi Wang, Tien-Ho Kuo, Ying-Lien Lin, and Shang-Yu Yang. "Stages of Change in Dairy Intake among Older Adults: Application of the Transtheoretical Model." International Journal of Environmental Research and Public Health 19, no. 3 (January 20, 2022): 1146. http://dx.doi.org/10.3390/ijerph19031146.
Full textAbstract:
Adequate dairy product intake can reduce the risk of chronic disease, mortality, low quality of life, and healthcare expenditure. However, the insufficient consumption of dairy products is a serious issue in Eastern societies. To the authors’ knowledge, few studies have explored dairy intake among Taiwanese older adults, especially using the transtheoretical model. The study aims were to address the following unknowns: (i) the distribution of dairy product intake behavior on stages of change (SOC); (ii) differences in variables (intake knowledge (IK), intake cons (IC), intake pros (IP), and intake self-efficacy (ISE)) among SOCs; (iii) discriminative abilities of variables on SOCs; and (iv) predictive ability of variables (IK, IC, IP, and ISE) for dairy product intake behavior on SOC for older adults. An explorative cross-sectional study was conducted to collect data from northern Taiwan using a questionnaire. A total of 342 older adults were recruited. Data were analyzed using multivariate analysis of variance, discriminant analysis, and multiple linear regression. There was a significant difference between the variables and SOCs. There was a better discriminant among the five SOCs. Dairy product intake behaviors were significantly associated with knowledge and self-efficacy in the pre-action stage, and with cons, pros, and self-efficacy in the post-action stage. In conclusion, appropriate nutritional empowerment could benefit older adults by improving dairy intake among the different SOCs.