Relevant bibliographies by topics / Multivariate analysis

Academic literature on the topic 'Multivariate analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Multivariate analysis"

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Fischer, Hagen S. "Multivariate analysis of phenological data." Phytocoenologia 30, no. 3-4 (November 24, 2000): 477–89. http://dx.doi.org/10.1127/phyto/30/2000/477.

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Editorial Submission, Haworth. "Multivariate Analysis." Journal of Housing For the Elderly 4, no. 2 (June 16, 1988): 121–68. http://dx.doi.org/10.1300/j081v04n02_05.

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Cooper, R. I., and Maurice Kendall. "Multivariate Analysis." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 2 (1988): 373. http://dx.doi.org/10.2307/2982780.

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AHLGREN, A. "Multivariate Analysis." Science 234, no. 4776 (October 31, 1986): 530–31. http://dx.doi.org/10.1126/science.234.4776.530.

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Wallbäcks, Lars. "Multivariate data analysis of multivariate populations." Chemometrics and Intelligent Laboratory Systems 86, no. 1 (March 2007): 10–16. http://dx.doi.org/10.1016/j.chemolab.2006.08.002.

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Varis, Olli, Hannu Sirviö, and Juhani Kettunen. "Multivariate analysis of lake phytoplankton and environmental factors." Archiv für Hydrobiologie 117, no. 2 (December 20, 1989): 163–75. http://dx.doi.org/10.1127/archiv-hydrobiol/117/1989/163.

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Chakrapani, Chuck. "Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences." Journal of the Royal Statistical Society: Series A (Statistics in Society) 175, no. 3 (June 20, 2012): 825–26. http://dx.doi.org/10.1111/j.1467-985x.2012.01045_8.x.

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Green, R. H. "Understanding Multivariate Analysis." Ecology 67, no. 2 (April 1986): 585. http://dx.doi.org/10.2307/1938605.

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Ziegel, Eric, and P. R. Krishnaiah. "Multivariate Analysis VI." Technometrics 28, no. 4 (November 1986): 404. http://dx.doi.org/10.2307/1268998.

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Goodall, Colin, and Albert Gifi. "Nonlinear Multivariate Analysis." Technometrics 34, no. 3 (August 1992): 357. http://dx.doi.org/10.2307/1270046.

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Dissertations / Theses on the topic "Multivariate analysis"

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Wolting, Duane. "MULTIVARIATE SYSTEMS ANALYSIS." International Foundation for Telemetering, 1985. http://hdl.handle.net/10150/615760.

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International Telemetering Conference Proceedings / October 28-31, 1985 / Riviera Hotel, Las Vegas, Nevada
In many engineering applications, a systems analysis is performed to study the effects of random error propagation throughout a system. Often these errors are not independent, and have joint behavior characterized by arbitrary covariance structure. The multivariate nature of such problems is compounded in complex systems, where overall system performance is described by a q-dimensional random vector. To address this problem, a computer program was developed which generates Taylor series approximations for multivariate system performance in the presence of random component variablilty. A summary of an application of this approach is given in which an analysis was performed to assess simultaneous design margins and to ensure optimal component selection.
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Nisa, Khoirin. "On multivariate dispersion analysis." Thesis, Besançon, 2016. http://www.theses.fr/2016BESA2025.

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Cette thèse examine la dispersion multivariée des modelés normales stables Tweedie. Trois estimateurs de fonction variance généralisée sont discutés. Ensuite dans le cadre de la famille exponentielle naturelle deux caractérisations du modèle normal-Poisson, qui est un cas particulier de modèles normales stables Tweedie avec composante discrète, sont indiquées : d'abord par fonction variance et ensuite par fonction variance généralisée. Le dernier fournit la solution à un problème particulier d'équation de Monge-Ampère. Enfin, pour illustrer l'application de la variance généralisée des modèles Tweedie stables normales, des exemples à partir des données réelles sont fournis
This thesis examines the multivariate dispersion of normal stable Tweedie (NST) models. Three generalize variance estimators of some NST models are discussed. Then within the framework of natural exponential family, two characterizations of normal Poisson model, which is a special case of NST models with discrete component, are shown : first by variance function and then by generalized variance function. The latter provides a solution to a particular Monge-Ampere equation problem. Finally, to illustrate the application of generalized variance of normal stable Tweedie models, examples from real data are provided
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Ahrabian, Alireza. "Multivariate time-frequency analysis." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/28958.

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Recent advances in time-frequency theory have led to the development of high resolution time-frequency algorithms, such as the empirical mode decomposition (EMD) and the synchrosqueezing transform (SST). These algorithms provide enhanced localization in representing time varying oscillatory components over conventional linear and quadratic time-frequency algorithms. However, with the emergence of low cost multichannel sensor technology, multivariate extensions of time-frequency algorithms are needed in order to exploit the inter-channel dependencies that may arise for multivariate data. Applications of this framework range from filtering to the analysis of oscillatory components. To this end, this thesis first seeks to introduce a multivariate extension of the synchrosqueezing transform, so as to identify a set of oscillations common to the multivariate data. Furthermore, a new framework for multivariate time-frequency representations is developed using the proposed multivariate extension of the SST. The performance of the proposed algorithms are demonstrated on a wide variety of both simulated and real world data sets, such as in phase synchrony spectrograms and multivariate signal denoising. Finally, multivariate extensions of the EMD have been developed that capture the inter-channel dependencies in multivariate data. This is achieved by processing such data directly in higher dimensional spaces where they reside, and by accounting for the power imbalance across multivariate data channels that are recorded from real world sensors, thereby preserving the multivariate structure of the data. These optimized performance of such data driven algorithms when processing multivariate data with power imbalances and inter-channel correlations, and is demonstrated on the real world examples of Doppler radar processing.
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Ahmed, Mosabber Uddin. "Multivariate multiscale complexity analysis." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/10204.

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Established dynamical complexity analysis measures operate at a single scale and thus fail to quantify inherent long-range correlations in real world data, a key feature of complex systems. They are designed for scalar time series, however, multivariate observations are common in modern real world scenarios and their simultaneous analysis is a prerequisite for the understanding of the underlying signal generating model. To that end, this thesis first introduces a notion of multivariate sample entropy and thus extends the current univariate complexity analysis to the multivariate case. The proposed multivariate multiscale entropy (MMSE) algorithm is shown to be capable of addressing the dynamical complexity of such data directly in the domain where they reside, and at multiple temporal scales, thus making full use of all the available information, both within and across the multiple data channels. Next, the intrinsic multivariate scales of the input data are generated adaptively via the multivariate empirical mode decomposition (MEMD) algorithm. This allows for both generating comparable scales from multiple data channels, and for temporal scales of same length as the length of input signal, thus, removing the critical limitation on input data length in current complexity analysis methods. The resulting MEMD-enhanced MMSE method is also shown to be suitable for non-stationary multivariate data analysis owing to the data-driven nature of MEMD algorithm, as non-stationarity is the biggest obstacle for meaningful complexity analysis. This thesis presents a quantum step forward in this area, by introducing robust and physically meaningful complexity estimates of real-world systems, which are typically multivariate, finite in duration, and of noisy and heterogeneous natures. This also allows us to gain better understanding of the complexity of the underlying multivariate model and more degrees of freedom and rigor in the analysis. Simulations on both synthetic and real world multivariate data sets support the analysis.
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Alashwali, Fatimah Salem. "Robustness and multivariate analysis." Thesis, University of Leeds, 2013. http://etheses.whiterose.ac.uk/5299/.

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Invariant coordinate selection (ICS) is a method for �nding structures in multivariate data using the eigenvalue-eigenvector decomposition of two different scatter matrices. The performance of the ICS depends on the structure of the data and the choice of the scatter matrices. The main goal of this thesis is to understand how ICS works in some situations, and does not in other. In particular, we look at ICS under three different structures: two-group mixtures, long-tailed distributions, and parallel line structure. Under two-group mixtures, we explore ICS based on the fourth-order moment matrix, ^K , and the covariance matrix S. We find the explicit form of ^K , and the ICS criterion under this model. We also explore the projection pursuit (PP) method, a variant of ICS, based on the univariate kurtosis. A comparison is made between PP, based on kurtosis, and ICS, based on ^K and S, through a simulation study. The results show that PP is more accurate than ICS. The asymptotic distributions of the ICS and PP estimates of the groups separation direction are derived. We explore ICS and PP based on two robust measures of spread, under twogroup mixtures. The use of common location measures, and pairwise differencing of the data in robust ICS and PP are investigated using simulations. The simulation results suggest that using a common location measure can be sometimes useful. The second structure considered in this thesis, the long-tailed distribution, is modelled by two dimensional errors-in-variables model, where the signal can have a non-normal distribution. ICS based on ^K and S is explored. We gain insight into how ICS �nds the signal direction in the errors in variables problem. We also compare the accuracy of the ICS estimate of the signal direction and Geary's fourth-order cumulant-based estimates through simulations. The results suggest that some of the cumulant-based estimates are more accurate than ICS, but ICS has the advantage of affine equivariance. The third structure considered is the parallel lines structure. We explore ICS based on the W-estimate based on the pairwise di�erencing of the data, ^ V , and S. We give a detailed analysis of the e�ect of the separation between points, overall and conditional on the horizontal separation, on the power of ICS based on ^ V and S.
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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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Lans, Ivo A. van der. "Nonlinear multivariate analysis for multiattribute preference data." [Leiden] : DSWO Press, Leiden University, 1992. http://catalog.hathitrust.org/api/volumes/oclc/28733326.html.

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Chongcharoen, Samruam. "One-sided multivariate tests /." free to MU campus, to others for purchase, 1998. http://wwwlib.umi.com/cr/mo/fullcit?p9924874.

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Salter, Amy Beatrix. "Multivariate dependencies in survival analysis." Title page, contents and introduction only, 1999. http://web4.library.adelaide.edu.au/theses/09PH/09phs177.pdf.

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Bibliography: leaves 177-181. This thesis investigates determinants of factors associated with retention of injecting drug users on the South Australian methadone program over the decade 1981 to mid 1991. Truncated multivariate survival models are proposed for the analysis of data from the program, and the theory of graphical chain models applied to the data. A detailed analysis is presented which gives further insight into the nature of the relationships that exist amongst these data. This provides an application of graphical chain models to survival data.
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Tavares, Nuno Filipe Ramalho da Cunha. "Multivariate analysis applied to clinical analysis data." Master's thesis, Faculdade de Ciências e Tecnologia, 2014. http://hdl.handle.net/10362/12288.

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Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão Industrial
Folate, vitamin B12, iron and hemoglobin are essential for metabolic functions in the body. The deficiency of these can be the cause of several known pathologies and, untreated, can be responsible for severe morbidity and even death. The objective of this study is to characterize a population, residing in the metropolitan area of Lisbon and Setubal, concerning serum levels of folate, vitamin B12, iron and hemoglobin, as well as finding evidence of correlations between these parameters and illnesses, mainly cardiovascular, gastrointestinal, neurological and anemia. Clinical analysis data was collected and submitted to multivariate analysis. First the data was screened with Spearman correlation and Kruskal-Wallis analysis of variance to study correlations and variability between groups. To characterize the population, we used cluster analysis with Ward’s linkage method. Finally a sensitivity analysis was performed to strengthen the results. A positive correlation between iron with, ferritin and transferrin, and with hemoglobin was observed with the Spearman correlation. Kruskal-Wallis analysis of variance test showed significant differences between these biomarkers in persons aged 0 to 29, 30 to 59 and over 60 years old. Cluster analysis proved to be a useful tool when characterizing a population based on its biomarkers, showing evidence of low folate levels for the population in general, and hemoglobin levels below the reference values. Iron and vitamin B12 were within the reference range for most of the population. Low levels of the parameters were registered mainly in patients with cardiovascular, gastrointestinal, and neurological diseases and anemia.
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Books on the topic "Multivariate analysis"

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Hair, Joseph F. Multivariate data analysis with readings. 2nd ed. New York: Macmillan, 1987.

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S, Fidell Linda, ed. Using multivariate statistics. 3rd ed. New York, NY: HarperCollins College Publishers, 1996.

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S, Fidell Linda, ed. Using multivariate statistics. 5th ed. Boston: Pearson/Allyn & Bacon, 2007.

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Tabachnick, Barbara G. Using multivariate statistics. 2nd ed. New York: HarperCollins, 1989.

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Tabachnick, Barbara G. Using multivariate statistics. 4th ed. Boston, Mass: Allyn and Bacon, 2001.

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Johnson, Richard Arnold. Applied multivariate statistical analysis. 2nd ed. Englewood Cliffs, N.J: Prentice-Hall, 1988.

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

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1949-, Dunn G., and Everitt Brian, eds. Applied multivariate data analysis. London: E. Arnold, 1991.

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1949-, Dunn G., ed. Applied multivariate data analysis. 2nd ed. London: Arnold, 2001.

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H, Woźniakowski, ed. Tractability of multivariate problems. Zürich: European Mathematical Society, 2008.

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Book chapters on the topic "Multivariate analysis"

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Regression Analysis." In Multivariate Analysis, 55–146. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32589-3_2.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Conjoint Analysis." In Multivariate Analysis, 531–98. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32589-3_9.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Contingency Analysis." In Multivariate Analysis, 355–80. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32589-3_6.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Discriminant Analysis." In Multivariate Analysis, 205–65. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32589-3_4.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Factor Analysis." In Multivariate Analysis, 381–450. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32589-3_7.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Cluster Analysis." In Multivariate Analysis, 451–530. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32589-3_8.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Regression Analysis." In Multivariate Analysis, 55–146. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40411-6_2.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Conjoint Analysis." In Multivariate Analysis, 533–99. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40411-6_9.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Factor Analysis." In Multivariate Analysis, 381–452. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40411-6_7.

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Backhaus, Klaus, Bernd Erichson, Sonja Gensler, Rolf Weiber, and Thomas Weiber. "Cluster Analysis." In Multivariate Analysis, 453–532. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40411-6_8.

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Conference papers on the topic "Multivariate analysis"

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Filus, Jerzy K., and Lidia Z. Filus. "Multivariate “pseudodistributions” pattern - applications." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756154.

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Filus, Jerzy K., and Lidia Z. Filus. "Multivariate "pseudodistributions" as natural extensions of the multivariate normal density pattern - theory." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756153.

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Kouvaras, George, and George Kokolakis. "Random Multivariate Multimodal Distributions." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0009.

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Miyagawa, Naoki, Hiroshi Teramoto, Chun-Biu Li, Tamiki Komatsuzaki, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Spatial Heterogeneity of Multivariate Dependence." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637776.

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Hoffmann, Jan, Klaus Aehlig, and Martin Hofmann. "Multivariate amortized resource analysis." In the 38th annual ACM SIGPLAN-SIGACT symposium. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1926385.1926427.

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Nam Anh, Dao. "Multivariate Filter for Saliency." In 2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR). IEEE, 2018. http://dx.doi.org/10.1109/mapr.2018.8337522.

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Carnicer, J. M., M. Gasca, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Some Recent Advances in Multivariate Polynomial Interpolation." In Numerical Analysis and Applied Mathematics. AIP, 2007. http://dx.doi.org/10.1063/1.2790271.

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Naveed, Khuram, Sidra Mukhtar, and Naveed Ur Rehman. "Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis." In 2021 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2021. http://dx.doi.org/10.1109/ssp49050.2021.9513823.

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Guo, Hanqi, He Xiao, Min Lu, and Xiaoru Yuan. "Scalable multivariate volume visualization and analysis." In 2011 IEEE Symposium on Large Data Analysis and Visualization (LDAV). IEEE, 2011. http://dx.doi.org/10.1109/ldav.2011.6092328.

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Dirie, Abdi-Hakin, Abubakar Abid, and James Zou. "Contrastive Multivariate Singular Spectrum Analysis." In 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2019. http://dx.doi.org/10.1109/allerton.2019.8919886.

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Reports on the topic "Multivariate analysis"

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Madych, W. R. Multivariate Multiscale Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada229502.

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Rao, C. R. Applications of Multivariate Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada265250.

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Dzhangarov, A. I. Multivariate analysis of variance analysis software. Engineering Herald of Don, 2019. http://dx.doi.org/10.18411/0236-8898-1123.

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Dzhangarov, A. I. Multivariate analysis of variance analysis software. Engineering Herald of Don, 2019. http://dx.doi.org/10.18411/0236-8898-1123-2020.

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Rao, C. R. Multivariate Analysis and Its Applications. Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada205585.

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Krishnaiah, P. R., and C. R. Rao. Multivariate Analysis and Its Application. Fort Belvoir, VA: Defense Technical Information Center, September 1987. http://dx.doi.org/10.21236/ada189983.

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Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada202273.

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Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada161375.

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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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Anderson, I. M., and J. Bentley. Multivariate statistical analysis of spectrum lines and images. Office of Scientific and Technical Information (OSTI), April 1997. http://dx.doi.org/10.2172/505348.

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