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Zeitschriftenartikel zum Thema „Correlation analysis“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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1

Hamdamov, Ahad Hamroyevich, Alimardon Toxir o'g'li To'rayev, and O'g'iloy Norpo'lat qizi Shamsiyeva. "Statistical Analysis of the Relationship Between Speed and Stopping Distance in Transportation Movements." Multidisciplinary Journal of Science and Technology 5, no. 2 (2025): 818–25. https://doi.org/10.5281/zenodo.14942327.

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This article explores the statistical relationship between speed and stopping distance in transportation movements. The study aims to analyze how variations in vehicle speed influence the distance required to bring a vehicle to a complete stop. Using real-world data, various statistical methods, such as regression analysis, were applied to determine the strength and nature of the correlation. The findings suggest that the stopping distance increases significantly with speed, highlighting the importance of safe driving practices and road design in minimizing accidents. The results also provide

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

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3

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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4

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

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5

Mazuruse, Peter. "Canonical correlation analysis." Journal of Financial Economic Policy 6, no. 2 (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 se

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

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7

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

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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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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

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BEREZKA, Kateryna, Oksana BASHUTSKA, Nataliya NAVOLSKA, and Vasil MELNYCHENKO. "ANALYSIS OF STUDENT PERFORMANCE BASED ON CANONICAL CORRELATION ANALYSIS." Computer systems and information technologies, no. 1 (March 27, 2025): 79–87. https://doi.org/10.31891/csit-2025-1-10.

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The article examines the application of Canonical Correlation Analysis (CCA) to investigate the relationships between student performance outcomes across different groups of disciplines. The disciplines were categorized into the following groups: mathematics, programming and algorithms, systems design, networks and distributed systems, applied software and technologies, and economic and managerial disciplines. The study aims to identify dependencies between these discipline groups that influence overall academic performance. The analysis revealed that discrete mathematics plays a key role in s

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

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11

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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12

Lipovetsky, Stan. "Canonical Concordance Correlation Analysis." Mathematics 11, no. 1 (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 u

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Costa, Natália, César Silva, and Paulo Ferreira. "Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies." International Journal of Financial Studies 7, no. 3 (2019): 51. http://dx.doi.org/10.3390/ijfs7030051.

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In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some e

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Krzyśko, Mirosław, and Łukasz Waszak. "Canonical correlation analysis for functional data." Biometrical Letters 50, no. 2 (2013): 95–105. http://dx.doi.org/10.2478/bile-2013-0020.

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Summary Classical canonical correlation analysis seeks the associations between two data sets, i.e. it searches for linear combinations of the original variables having maximal correlation. Our task is to maximize this correlation, and is equivalent to solving a generalized eigenvalue problem. The maximal correlation coefficient (being a solution of this problem) is the first canonical correlation coefficient. In this paper we propose a new method of constructing canonical correlations and canonical variables for a pair of stochastic processes represented by a finite number of orthonormal basi

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Teplova (Kosolapova), N., K. Itoh, S.-I. Itoh, et al. "On turbulence-correlation analysis based on correlation reflectometry." Physica Scripta 87, no. 4 (2013): 045502. http://dx.doi.org/10.1088/0031-8949/87/04/045502.

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Prevost, A. Toby, Dan Mason, Simon Griffin, Ann-Louise Kinmonth, Stephen Sutton, and David Spiegelhalter. "Allowing for correlations between correlations in random-effects meta-analysis of correlation matrices." Psychological Methods 12, no. 4 (2007): 434–50. http://dx.doi.org/10.1037/1082-989x.12.4.434.

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17

Joe, Harry. "Generating random correlation matrices based on partial correlations." Journal of Multivariate Analysis 97, no. 10 (2006): 2177–89. http://dx.doi.org/10.1016/j.jmva.2005.05.010.

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18

Aizawa, Naoyuki, and Emiko Nakasone. "Statistical Method. Correlation Analysis." Journal of exercise physiology 4, no. 4 (1989): 223–29. http://dx.doi.org/10.1589/rika1986.4.223.

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19

Lipovetsky, Stan. "Orthonormal Canonical Correlation Analysis." Open Statistics 2, no. 1 (2021): 24–36. http://dx.doi.org/10.1515/stat-2020-0104.

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Abstract Complex managerial problems are usually described by datasets with multiple variables, and in lack of a theoretical model, the data structures can be found by special multivariate statistical techniques. For two datasets, the canonical correlation analysis and its robust version are known as good working research tools. This paper presents their further development via the orthonormal approximation of data matrices which corresponds to using singular value decomposition in the canonical correlations. The features of the new method are described and applications considered. This type o

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Zhao, Hongmin, Dongting Sun, and Zhigang Luo. "Incremental Canonical Correlation Analysis." Applied Sciences 10, no. 21 (2020): 7827. http://dx.doi.org/10.3390/app10217827.

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Canonical correlation analysis (CCA) is a kind of a simple yet effective multiview feature learning technique. In general, it learns separate subspaces for two views by maximizing their correlations. However, there still exist two restrictions to limit its applicability for large-scale datasets, such as videos: (1) sufficiently large memory requirements and (2) high-computation complexity for matrix inverse. To address these issues, we propose an incremental canonical correlation analysis (ICCA), which maintains in an adaptive manner a constant memory storage for both the mean and covariance m

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Novak, A. I., and Y. O. Lyashchuk. "Biological risk correlation analysis." Proceedings of the Voronezh State University of Engineering Technologies 81, no. 4 (2020): 40–45. http://dx.doi.org/10.20914/2310-1202-2019-4-40-45.

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NAWA, Kotaro. "Patents of correlation analysis." Journal of Information Processing and Management 51, no. 10 (2009): 785–86. http://dx.doi.org/10.1241/johokanri.51.785.

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23

Guo, Yiwen, Xiaoqing Ding, Changsong Liu, and Jing-Hao Xue. "Sufficient Canonical Correlation Analysis." IEEE Transactions on Image Processing 25, no. 6 (2016): 2610–19. http://dx.doi.org/10.1109/tip.2016.2551374.

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Kotiah, Thoddi C. T. "Correlation analysis in calculus." International Journal of Mathematical Education in Science and Technology 23, no. 5 (1992): 671–82. http://dx.doi.org/10.1080/0020739920230505.

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Cocozzelli, Carmelo. "Understanding Canonical Correlation Analysis." Journal of Social Service Research 13, no. 4 (1990): 19–42. http://dx.doi.org/10.1300/j079v13n04_02.

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26

Larner, Ken, and Valmore Celis. "Selective-correlation velocity analysis." GEOPHYSICS 72, no. 2 (2007): U11—U19. http://dx.doi.org/10.1190/1.2435702.

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Increased resolution in computed velocity spectra aids in distinguishing between neighboring primary events from reflectors with conflicting dip and in identifying primaries in the presence of multiples. The transformation from the offset and reflection-time domain to the stacking-velocity and zero-offset-time domain can be achieved using any of several coherence measures based on crosscorrelations among traces in a common-midpoint (CMP) gather or a common-image gather (CIG). Use of just selected subsets of crosscorrelations rather than all possible ones in a gather can improve both the reliab

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Kraftmakher, Yaakov. "Correlation analysis with ScienceWorkshop." American Journal of Physics 70, no. 7 (2002): 694–97. http://dx.doi.org/10.1119/1.1475330.

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28

Harrington, Peter de B., Aaron Urbas, and Peter J. Tandler. "Two-dimensional correlation analysis." Chemometrics and Intelligent Laboratory Systems 50, no. 2 (2000): 149–74. http://dx.doi.org/10.1016/s0169-7439(99)00062-3.

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29

Shi, Runhua, and Steven A. Conrad. "Correlation and regression analysis." Annals of Allergy, Asthma & Immunology 103, no. 4 (2009): S35—S41. http://dx.doi.org/10.1016/s1081-1206(10)60820-4.

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30

Zhang, Enhao, Xiaohong Chen, and Liping Wang. "Consistent Discriminant Correlation Analysis." Neural Processing Letters 52, no. 1 (2020): 891–904. http://dx.doi.org/10.1007/s11063-020-10285-w.

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31

Sakar, C. Okan, Olcay Kursun, and Fikret Gurgen. "Ensemble canonical correlation analysis." Applied Intelligence 40, no. 2 (2013): 291–304. http://dx.doi.org/10.1007/s10489-013-0464-2.

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32

Hardoon, David R., and John Shawe-Taylor. "Sparse canonical correlation analysis." Machine Learning 83, no. 3 (2010): 331–53. http://dx.doi.org/10.1007/s10994-010-5222-7.

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33

Yang, Qingxi, Qiaokun Kang, Qingyang Huang, Zenghui Cui, Yu Bai, and Huanan Wei. "Linear correlation analysis of ammunition storage environment based on Pearson correlation analysis." Journal of Physics: Conference Series 1948, no. 1 (2021): 012064. http://dx.doi.org/10.1088/1742-6596/1948/1/012064.

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Grone, Robert, and Stephen Pierce. "Permanental Inequalities for Correlation Matrices." SIAM Journal on Matrix Analysis and Applications 9, no. 2 (1988): 194–201. http://dx.doi.org/10.1137/0609016.

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35

Abha, Ruchika, and M. L. Meena. "Correlation and Path Analysis in Cowpea (Vigna unguiculata (L.) Walp.)." Journal of Experimental Agriculture International 46, no. 7 (2024): 1071–85. http://dx.doi.org/10.9734/jeai/2024/v46i72660.

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This study, conducted at Horticultural Research Farm of Babasaheb Bhimrao Ambedkar University, Lucknow, India, over 2020-21 and 2021-22, aimed to examine coefficient of correlation and direct and indirect effects of yield contributing traits on economic yield among 30 diverse cowpea genotypes. The research employed a Randomized Block Design (R.B.D.) with three replications, assessing twenty-six quantitative traits. Genotypic correlations exceeded phenotypic correlations, underscoring genetic influence over environmental factors. Key findings included plant height's positive correlation with br

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Dashora, Sanchita, Medhavi Sharma, and Rajrani Sharma. "Analysis of Hysterectomies and Clinicopathological Correlation: A Prospective Study." Indian Journal of Obstetrics and Gynecology 6, no. 1 (2018): 7–14. http://dx.doi.org/10.21088/ijog.2321.1636.6118.1.

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Tawfiq, Sherwan I., Dana A. Abdulkhaleq, and Shara J. Hama. "Correlation and Path Analysis in Barley under Rainfall Conditions." Journal of Zankoy Sulaimani - Part A 18, no. 3 (2016): 99–106. http://dx.doi.org/10.17656/jzs.10538.

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ter Braak, Cajo J. F. "Interpreting canonical correlation analysis through biplots of structure correlations and weights." Psychometrika 55, no. 3 (1990): 519–31. http://dx.doi.org/10.1007/bf02294765.

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Qiu, Lin, and Vernon M. Chinchilli. "Probabilistic canonical correlation analysis for sparse count data." Journal of Statistical Research 56, no. 1 (2023): 75–100. http://dx.doi.org/10.3329/jsr.v56i1.63947.

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Canonical correlation analysis (CCA) is a classical and important multivariate technique for exploring the relationship between two sets of continuous variables. CCA has applications in many ﬁelds, such as genomics and neuroimaging. It can extract meaningful features as well as use these features for subsequent analysis. Although some sparse CCA methods have been developed to deal with high-dimensional problems, they are designed speciﬁcally for continuous data and do not consider the integer-valued data from next-generation sequencing platforms that exhibit very low counts for some important

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Seok, Jong-Won, Tae-Hwan Kim, and Keun-Sung Bae. "Underwater Target Analysis Using Canonical Correlation Analysis." Journal of the Korean Institute of Information and Communication Engineering 16, no. 9 (2012): 1878–83. http://dx.doi.org/10.6109/jkiice.2012.16.9.1878.

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Kai-guang, ZHANG, MENG Hong-ling, BA Ming-ting, and SUN Yan-min. "Correlation Analysis of Main Pollutant Concentration-A Case Study of Zhengzhou." Journal of Progressive Research in Mathematics 15, no. 2 (2019): 2632–40. https://doi.org/10.5281/zenodo.3974070.

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Air pollution is one of the main problems to be solved in the sustainable development of China's economy, its main pollution components include PM2.5, PM10, SO2, NO2, CO and O3, the pollution component governance is an effective means of atmospheric environmental management. Based on the monitor data of six main pollutant concentrations in Zhengzhou from 2015 to 2018, this paper analyzes the correlation characteristics between their concentrations by using correlation analysis, the multiple correlation characteristics of the one pollutant concentration with the other five pollutant concent

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Oussalah, Mourad, Zahir Messaoudi, and Abdelaziz Ouldali. "Track-To-Track Measurement Fusion Architectures and Correlation Analysis." JUCS - Journal of Universal Computer Science 16, no. (1) (2010): 37–61. https://doi.org/10.3217/jucs-016-01-0037.

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The purpose of this paper is to address some theoretical issues related to the track-to-track fusion problem when the measurements tracking the same target are inherently correlated by the common process noise of the underlying target. This problem has been intensively investigated using standard Kalman filter with some appealing theoretical results, however such results are no longer valid in case of suboptimality due to either the presence of strong nonlinearity or to the discrete uncertainty pervading the origin of the measurement. This paper reviews several architectures of parallelized bl

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Chhetry, Devendra, Jan De Leeuw, and Allan R. Sampson. "Monotone Correlation and Monotone Disjunct Pieces." SIAM Journal on Matrix Analysis and Applications 11, no. 3 (1990): 361–68. http://dx.doi.org/10.1137/0611024.

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Li, Chi-Kwong, and Bit-Shun Tam. "A Note on Extreme Correlation Matrices." SIAM Journal on Matrix Analysis and Applications 15, no. 3 (1994): 903–8. http://dx.doi.org/10.1137/s0895479892240683.

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MORARIU, VASILE V., LUIZA BUIMAGA-IARINCA, CĂLIN VAMOŞ, and ŞTEFAN M. ŞOLTUZ. "DETRENDED FLUCTUATION ANALYSIS OF AUTOREGRESSIVE PROCESSES." Fluctuation and Noise Letters 07, no. 03 (2007): L249—L255. http://dx.doi.org/10.1142/s0219477507003908.

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Autoregressive processes (AR) have typical short-range memory. Detrended Fluctuation Analysis (DFA) was basically designed to reveal long-range correlations in non stationary processes. However DFA can also be regarded as a suitable method to investigate both long-range and short-range correlations in non stationary and stationary systems. Applying DFA to AR processes can help understanding the non-uniform correlation structure of such processes. We systematically investigated a first order autoregressive model AR(1) by DFA and established the relationship between the interaction constant of A

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von Frese, Ralph R. B., Michael B. Jones, Jeong Woo Kim, and Jeong‐Hee Kim. "Analysis of anomaly correlations." GEOPHYSICS 62, no. 1 (1997): 342–51. http://dx.doi.org/10.1190/1.1444136.

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Recognizing correlations between data sets is the basis for rationalizing geophysical interpretation and theory. Procedures are presented that constitute an effective process for identifying correlative features between two or more digital data sets. The procedures include the development of normalization factors from the mean and variance properties of the data sets. Using these factors, the data sets may be transformed so that they have common amplitude ranges, means, and variances, thereby allowing a common graphical representation of the data sets that facilitates the visualization of feat

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Yuan, Q., M. Zhang, X. Liu, P. F. Jiang, and G. I. Kokhirova. "Correlation Analysis between OJ 287 Radio Jet Observables." Astrophysical Journal 949, no. 1 (2023): 20. http://dx.doi.org/10.3847/1538-4357/acc5ec.

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Abstract We collected the archival data of blazar OJ 287 from heterogeneous very long baseline interferometry monitoring programs at 2.3, 8.6, 15, and 43 GHz. The data reduction and observable extraction of those multiband, multiepoch observations are batch-processed consistently with our automated pipeline. We present the multivariate correlation analysis on the observables at each band. We employ the cross-correlation function to search the correlations and the Monte Carlo technique to verify the certainty of correlations. Several correlations are found. The foremost findings are the correla

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Wang, Wenjing, Yuwu Lu, and Zhihui Lai. "Symmetrical Robust Canonical Correlation Analysis for Image Classification." AATCC Journal of Research 8, no. 1_suppl (2021): 54–61. http://dx.doi.org/10.14504/ajr.8.s1.7.

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Canonical correlation analysis (CCA) is a useful technique for multivariate data analysis, which can find correlations between two sets of multidimensional data. CCA projects two sets of data into a low-dimensional space in which the correlations between them are maximized. However, CCA is sensitive to noise or outliers in the collected data of real-world applications, which will degrade its performance. To overcome this disadvantage, we propose symmetrical robust canonical correlation analysis (SRCCA) for image classification. By using low-rank learning, the noise is removed, and CCA is used

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YIN, YI, and PENGJIAN SHANG. "MULTISCALE DETRENDED CROSS-CORRELATION ANALYSIS OF STOCK MARKETS." Fractals 22, no. 04 (2014): 1450007. http://dx.doi.org/10.1142/s0218348x14500078.

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In this paper, we employ the detrended cross-correlation analysis (DCCA) to investigate the cross-correlations between different stock markets. We report the results of cross-correlated behaviors in US, Chinese and European stock markets in period 1997–2012 by using DCCA method. The DCCA shows the cross-correlated behaviors of intra-regional and inter-regional stock markets in the short and long term which display the similarities and differences of cross-correlated behaviors simply and roughly and the persistence of cross-correlated behaviors of fluctuations. Then, because of the limitation a

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Balakrishna, P., Rajasekhar Pinnamaneni, KV Pavani, and RK Mathur. "Correlation and Path Coefficient Analysis in Indian Oil Palm genotypes." Journal of Pure and Applied Microbiology 12, no. 1 (2018): 195–206. http://dx.doi.org/10.22207/jpam.12.1.25.

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