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Готові списки джерел за темами / Correlation analysis

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Добірка наукової літератури з теми "Correlation analysis"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 11 березня 2023

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Статті в журналах з теми "Correlation analysis"

1

Bolshakova, Lyudmila Valentinovna. "Correlation and Regression Analysis of Economic Problems." Revista Gestão Inovação e Tecnologias 11, no. 3 (June 30, 2021): 2077–88. http://dx.doi.org/10.47059/revistageintec.v11i3.2074.

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2

NAOKO, Fujii. "LECTURE: Correlation Analysis." Journal of exercise physiology 6, no. 3 (1991): 127–32. http://dx.doi.org/10.1589/rika1986.6.127.

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3

Strack, Rita. "Comprehensive correlation analysis." Nature Methods 16, no. 1 (December 20, 2018): 25. http://dx.doi.org/10.1038/s41592-018-0279-5.

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4

Mazuruse, Peter. "Canonical correlation analysis." Journal of Financial Economic Policy 6, no. 2 (May 6, 2014): 179–96. http://dx.doi.org/10.1108/jfep-09-2013-0047.

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Purpose – The purpose of this paper was to construct a canonical correlation analysis (CCA) model for the Zimbabwe stock exchange (ZSE). This paper analyses the impact of macroeconomic variables on stock returns for the Zimbabwe Stock Exchange using the canonical correlation analysis (CCA). Design/methodology/approach – Data for the independent (macroeconomic) variables and dependent variables (stock returns) were extracted from secondary sources for the period from January 1990 to December 2008. For each variable, 132 sets of data were collected. Eight top trading companies at the ZSE were selected, and their monthly stock returns were calculated using monthly stock prices. The independent variables include: consumer price index, money supply, treasury bills, exchange rate, unemployment, mining and industrial index. The CCA was used to construct the CCA model for the ZSE. Findings – Maximization of stock returns at the ZSE is mostly influenced by the changes in consumer price index, money supply, exchange rate and treasury bills. The four macroeconomic variables greatly affect the movement of stock prices which, in turn, affect stock returns. The stock returns for Hwange, Barclays, Falcon, Ariston, Border, Caps and Bindura were significant in forming the CCA model. Research limitations/implications – During the research period, some companies delisted due to economic hardships, and this reduced the sample size for stock returns for respective companies. Practical implications – The results from this research can be used by policymakers, stock market regulators and the government to make informed decisions when crafting economic policies for the country. The CCA model enables the stakeholders to identify the macroeconomic variables that play a pivotal role in maximizing the strength of the relationship with stock returns. Social implications – Macroeconomic variables, such as consumer price index, inflation, etc., directly affect the livelihoods of the general populace. They also impact on the performance of companies. The society can monitor economic trends and make the right decisions based on the current trends of economic performance. Originality/value – This research opens a new dimension to the study of macroeconomic variables and stock returns. Most studies carried out so far in Zimbabwe zeroed in on multiple regression as the central methodology. No study has been done using the CCA as the main methodology.

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5

Stephenson, Peter. "Analysis and Correlation." Computer Fraud & Security 2002, no. 12 (December 2002): 16–18. http://dx.doi.org/10.1016/s1361-3723(02)01214-9.

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6

Chen, Xiaohong, Songcan Chen, and Hui Xue. "Large correlation analysis." Applied Mathematics and Computation 217, no. 22 (July 2011): 9041–52. http://dx.doi.org/10.1016/j.amc.2011.03.117.

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7

Yoshioka, Tomohiko, Yasuyuki Morita, Mitsugu Todo, Yasuyuki Matsusita, and Kazuo Arakawa. "P-19 Deformation Analysis of Periodontal Tissue using Digital Image Correlation Analysis." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2007.3 (2007): S107. http://dx.doi.org/10.1299/jsmeapbio.2007.3.s107.

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8

Faltermeier, Rupert, Martin A. Proescholdt, Sylvia Bele, and Alexander Brawanski. "Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology." Computational and Mathematical Methods in Medicine 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/652030.

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Анотація:

Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses.

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9

Holmes, R. B. "On Random Correlation Matrices." SIAM Journal on Matrix Analysis and Applications 12, no. 2 (April 1991): 239–72. http://dx.doi.org/10.1137/0612019.

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Lipovetsky, Stan. "Canonical Concordance Correlation Analysis." Mathematics 11, no. 1 (December 26, 2022): 99. http://dx.doi.org/10.3390/math11010099.

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A multivariate technique named Canonical Concordance Correlation Analysis (CCCA) is introduced. In contrast to the classical Canonical Correlation Analysis (CCA) which is based on maximization of the Pearson’s correlation coefficient between the linear combinations of two sets of variables, the CCCA maximizes the Lin’s concordance correlation coefficient which accounts not just for the maximum correlation but also for the closeness of the aggregates’ mean values and the closeness of their variances. While the CCA employs the centered data with excluded means of the variables, the CCCA can be understood as a more comprehensive characteristic of similarity, or agreement between two data sets measured simultaneously by the distance of their mean values and the distance of their variances, together with the maximum possible correlation between the aggregates of the variables in the sets. The CCCA is expressed as a generalized eigenproblem which reduces to the regular CCA if the means of the aggregates are equal, but for the different means it yields a different from CCA solution. The properties and applications of this type of multivariate analysis are described. The CCCA approach can be useful for solving various applied statistical problems when closeness of the aggregated means and variances, together with the maximum canonical correlations are needed for a general agreement between two data sets.

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Дисертації з теми "Correlation analysis"

1

Bhatta, Sanjeev. "Conditional Correlation Analysis." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1495963166600514.

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2

Orvalho, André. "Botnet Detection by Correlation Analysis." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105096.

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When a bot master uses a control and commander (C&C) mechanism to assemble a large number of bots, infecting them by using well known vulnerabilities, it forms a botnet. Botnets can vary in C&C architecture (Centralized C&C or P2P are the most common), communication protocols used (IRC, HTTP or others like P2P) and observable botnet activities. They are nowadays one of the largest threats on cyber security and it is very important to specify the different characteristics of botnets in order to detect them, the same way a hunter needs to know its prey before preparing methods to catch it. There are 2 important places to look for botnet activity: The network and the infected host. This project intends to present a study that correlates the behavior on the network with the behavior on the host in order to help detection, studies like [SLWL07] (based on network behavior) and [SM07] (based on host behavior) are two good start points to help on the research. The choice of the architecture was done by looking at the botnet characteristics especially the capacity of changing and evolving which makes methods for detection by misuse obsolete. The system is designed to first look at 4 features of system calls on the host side: First which system call it is, second the name of the application using the system call, third the time between this system call and the last system call and for last the sequence of the past three system calls. A technique of unsupervised learning (the K-means algorithm) will be used to calculate the values for the threshold using an unclassified training set. when on the real world the collection is used to calculate the values to compare with the threshold. If it passes the threshold than the necessary information is passed to the network evaluation block. On the network side and before receiving any data from the host side, it will calculate the threshold for the flows given on the training set. When using the data from the host to narrow down the number of flows to look at, it very if their values pass the threshold. The feature used to calculate the threshold is the time between flows. If the network finds flows that pass the threshold for the network evaluation block than it will emit reports and alarms to the user. The small experiences done show some promising signs for use on the real world even though a lot more further testing is needed especially on the network bit. The prototype shows some limitations that can be overcome by further testing and using other techniques to evolve the prototype.

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3

Rossi, Claudio Alexander. "Empirical Analysis of Implied Equity Correlation." St. Gallen, 2009. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/01653419003/$FILE/01653419003.pdf.

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4

Lai, Pei Ling. "Neural implementations of canonical correlation analysis." Thesis, University of the West of Scotland, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311771.

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5

Droop, Alastair Philip. "Correlation Analysis of Multivariate Biological Data." Thesis, University of York, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507622.

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6

Samarov, Daniel V. Marron James Stephen. "The analysis and advanced extensions of canonical correlation analysis." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2009. http://dc.lib.unc.edu/u?/etd,2205.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2009.
Title from electronic title page (viewed Jun. 26, 2009). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.

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7

Nicovich, Philip R. "Widefield fluorescence correlation spectroscopy." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33849.

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Fluorescence correlation spectroscopy has become a standard technique for modern biophysics and single molecule spectroscopy research. Here is presented a novel widefield extension of the established single-point technique. Flow in microfluidic devices was used as a model system for microscopic motion and through widefield fluorescence correlation spectroscopy flow profiles were mapped in three dimensions. The technique presented is shown to be more tolerant to low signal strength, allowing image data with signal-to-noise values as low as 1.4 to produce accurate flow maps as well as utilizing dye-labeled single antibodies as flow tracers. With proper instrumentation flows along the axial direction can also be measured. Widefield fluorescence correlation spectroscopy has also been utilized to produce super-resolution confocal microscopic images relying on the single-molecule microsecond blinking dynamics of fluorescent silver clusters. A method for fluorescence modulation signal extraction as well as synthesis of several novel noble metal fluorophores is also presented.

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8

Gou, Zhenkun. "Canonical correlation analysis and artificial neural networks." Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269409.

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9

Lykou, Anastasia. "Sparse canonical correlation analysis using the Lasso." Thesis, Lancaster University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.533099.

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10

Moore, Julie Carolyn. "Comparisons of correlation methods in risk analysis." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06102009-063246/.

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Книги з теми "Correlation analysis"

1

Vogt, W., and R. Johnson. Correlation and Regression Analysis. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2012. http://dx.doi.org/10.4135/9781446286104.

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2

Green, Vivian. Correlation analysis, Tambon Wawi. Chiang Mai: Thai-German Highland Development Programme, 1991.

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3

Nevada. Division of Water Planning., ed. Churchill County correlation analysis: Correlation analysis of Churchill County's primary economic indicators. Carson City, Nev: Division of Water Planning, 1992.

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4

Correlation analysis of chemical data. New York: Plenum Press, 1988.

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5

1944-, Kaplan Lawrence A., Pesce Amadeo J, and Kazmierczak Steven C, eds. Clinical chemistry: Theory, analysis, correlation. 4th ed. St. Louis, Mo: Mosby, 2003.

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6

Pawlowsky-Glahn, Vera. Compositional data analysis: Theory and applications. Hoboken, N.J: Wiley, 2011.

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7

Benesty, Jacob, and Israel Cohen. Canonical Correlation Analysis in Speech Enhancement. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67020-1.

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8

A, Turovsky A., and Zaikov Gennadiĭ Efremovich, eds. Correlation analysis in chemistry of solutions. Utrecht: VSP, 2004.

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9

1944-, Kaplan Lawrence A., and Pesce Amadeo J, eds. Clinical chemistry: Theory, analysis, and correlation. 2nd ed. St. Louis: Mosby, 1989.

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10

1944-, Kaplan Lawrence A., and Pesce Amadeo J, eds. Clinical chemistry: Theory, analysis, and correlation. 3rd ed. St. Louis: Mosby, 1996.

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Частини книг з теми "Correlation analysis"

1

L. Jockers, Matthew, and Rosamond Thalken. "Correlation." In Text Analysis with R, 69–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39643-5_6.

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2

Bolin, Jocelyn H. "Correlation." In Regression Analysis in R, 7–26. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9780429295843-2.

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3

Ma, Y. Z. "Correlation Analysis." In Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling, 77–102. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17860-4_4.

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4

Filzmoser, Peter, Karel Hron, and Matthias Templ. "Correlation Analysis." In Springer Series in Statistics, 149–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96422-5_8.

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5

Mat Roni, Saiyidi, Margaret Kristin Merga, and Julia Elizabeth Morris. "Analysis: Correlation." In Conducting Quantitative Research in Education, 111–32. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9132-3_7.

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6

Sahu, Pradip Kumar. "Correlation Analysis." In Applied Statistics for Agriculture, Veterinary, Fishery, Dairy and Allied Fields, 195–221. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2831-8_7.

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7

Da Costa Lewis, Nigel. "Correlation Analysis." In Energy Risk Modeling, 125–50. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230523784_7.

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Kido, Ken’iti. "Correlation." In Digital Fourier Analysis: Advanced Techniques, 23–52. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1127-1_2.

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9

Gerbing, David W. "Correlation." In R Data Analysis without Programming, 193–216. 2nd ed. New York: Routledge, 2023. http://dx.doi.org/10.4324/9781003278412-10.

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Olive, David J. "Canonical Correlation Analysis." In Robust Multivariate Analysis, 219–31. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68253-2_7.

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Тези доповідей конференцій з теми "Correlation analysis"

1

Sakhnovskiy, Mykhaylo, Yuriy Ushenko, L. Kushnerik, I. V. Soltys, N. Pavlyukovich, and O. Pavlyukovich. "Wavelet analysis of birefringence images of myocardium tissue." In Correlation Optics 2017, edited by Oleg V. Angelsky. SPIE, 2018. http://dx.doi.org/10.1117/12.2305333.

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2

Tscharauter, Walther W. "Mobility measurements by Phase Analysis." In Photon Correlation and Scattering. Washington, D.C.: OSA, 2000. http://dx.doi.org/10.1364/pcs.2000.ma3.

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3

Ushenko, O. G., A. V. Dubolazov, V. O. Balanets'ka, A. V. Karachevtsev, and M. Sydor. "Wavelet analysis for polarization inhomogeneous laser images of blood plasma." In Correlation Optics 2011, edited by Oleg V. Angelsky. SPIE, 2011. http://dx.doi.org/10.1117/12.920169.

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4

Penttinen, Niko, Stanislav Hason, Ludek Joska, Ladislav Cvrcek, and Raimo Silvennoinen. "On the analysis of optical signals from Ti35Nb6Ta and Ti6Al4V surfaces." In Correlation Optics 2011, edited by Oleg V. Angelsky. SPIE, 2011. http://dx.doi.org/10.1117/12.916062.

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Smart, Anthony E., Robert V. Edwards, and William V. Meyer. "Quantitative simulation of errors in correlation analysis." In Photon Correlation and Scattering. Washington, D.C.: OSA, 2000. http://dx.doi.org/10.1364/pcs.2000.wc1.

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Bogatyryova, G. V., and D. Y. Kondratenko. "Analysis of influence of plane-parallel plate on resolving power of Fourier spectrometer." In Correlation Optics 2011, edited by Oleg V. Angelsky. SPIE, 2011. http://dx.doi.org/10.1117/12.920575.

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Strinadko, Marina M., and Katerina B. Timochko. "Correlation method of electrocardiogram analysis." In Fifth International Conference on Correlation Optics, edited by Oleg V. Angelsky. SPIE, 2002. http://dx.doi.org/10.1117/12.455228.

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Gudyma, Iurii V., and Artur I. Maksymov. "Theoretical analysis of photoinduced first order phase transition in spin-crossover complexes under noise action." In Correlation Optics 2011, edited by Oleg V. Angelsky. SPIE, 2011. http://dx.doi.org/10.1117/12.917711.

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McNeil-Watson, F. K. "Improved Analysis of Photon Correlation Data." In Photon Correlation Techniques and Applications. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/pcta.1988.dsopp205.

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Анотація:

The emphasis in recent years on improvements in the polydisperse analysis problem - that is the recovery of accurate particle size distributions from PCS data, have concentrated on two areas. Firstly the measurement of the correlation points at a logarithmically spaced set of points, that effectively match the collection of data to the distribution of information in exponential functions. Secondly the use of fitting methods that apply constraints either implicitly or explicitly to the range of possible solutions that are evaluated in the fitting process.

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10

Ungurian, V. P., O. I. Ivashchuk, and V. O. Ushenko. "Statistical analysis of polarizing maps of blood plasma laser images for the diagnostics of malignant formations." In Correlation Optics 2011, edited by Oleg V. Angelsky. SPIE, 2011. http://dx.doi.org/10.1117/12.920592.

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Звіти організацій з теми "Correlation analysis"

1

Todros, Koby, and Alfred O. Hero. On Measure Transformed Canonical Correlation Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada578246.

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Herman, Matthew Joseph. Two-Dimensional Correlation Method for Polymer Analysis. Office of Scientific and Technical Information (OSTI), June 2015. http://dx.doi.org/10.2172/1172208.

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Steed, Chad A., J. Edward SwanII, Patrick J. Fitzpatrick, and T. J. Jankun-Kelly. A Visual Analytics Approach for Correlation, Classification, and Regression Analysis. Office of Scientific and Technical Information (OSTI), February 2012. http://dx.doi.org/10.2172/1035521.

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Ma, Kwan-Liu. Interactive Correlation Analysis and Visualization of Climate Data. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1325752.

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Meisel, L. V., and M. A. Johnson. Numerical Box-Counting and Correlation Integral Multifractal Analysis. Fort Belvoir, VA: Defense Technical Information Center, May 1994. http://dx.doi.org/10.21236/ada282902.

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Fujikoshi, Y., P. R. Krishnaiah, and J. Schmidhammer. Effect of Additional Variables in Principal Component Analysis, Discriminant Analysis and Canonical Correlation Analysis. Fort Belvoir, VA: Defense Technical Information Center, August 1985. http://dx.doi.org/10.21236/ada162069.

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Burr, T. L., L. E. Wangen, and M. F. Mullen. Authentication of reprocessing plant safeguards data through correlation analysis. Office of Scientific and Technical Information (OSTI), April 1995. http://dx.doi.org/10.2172/45607.

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Pulsipher, B. Statistical analysis of shard and canister glass correlation test. Office of Scientific and Technical Information (OSTI), December 1990. http://dx.doi.org/10.2172/257360.

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Soloviev, Vladimir N., Symon P. Yevtushenko, and Viktor V. Batareyev. Comparative analysis of the cryptocurrency and the stock markets using the Random Matrix Theory. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3681.

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Анотація:

This article demonstrates the comparative possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency and developed stock market. Then, combining the empirical cross-correlation matrix with the Random Matrix Theory, we mainly examine the statistical properties of cross-correlation coefficients, the evolution of the distribution of eigenvalues and corresponding eigenvectors in both markets using the daily returns of price time series. The result has indicated that the largest eigenvalue reflects a collective effect of the whole market, and is very sensitive to the crash phenomena. It has been shown that introduced the largest eigenvalue of the matrix of correlations can act like indicators-predictors of falls in both markets.

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Pakko, Michael R. A Spectral Analysis of the Cross-Country Consumption Correlation Puzzle. Federal Reserve Bank of St. Louis, 2003. http://dx.doi.org/10.20955/wp.2003.023.

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