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Статті в журналах з теми "Correlation analysis"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 9 червня 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 (February 28, 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 valuable insights for transportation engineers and policymakers to optimize traffic safety measures.

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2

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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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 (December 20, 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 (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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6

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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7

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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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 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

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 shaping programming skills, with performance in mathematical disciplines significantly correlating with outcomes in other fields. Both strong and weak correlations were identified between specific discipline groups. The use of CCA provided deeper insights into the relationships between subjects, offering new opportunities for optimizing the educational process. The findings of the article have both theoretical and practical significance, contributing to the improvement of educational approaches and methods for assessing academic performance.

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10

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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11

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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12

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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13

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 (September 15, 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 evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.

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14

Krzyśko, Mirosław, and Łukasz Waszak. "Canonical correlation analysis for functional data." Biometrical Letters 50, no. 2 (December 1, 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 basis functions.

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15

Teplova (Kosolapova), N., K. Itoh, S.-I. Itoh, E. Gusakov, S. Heuraux, S. Inagaki, M. Sasaki, et al. "On turbulence-correlation analysis based on correlation reflectometry." Physica Scripta 87, no. 4 (February 28, 2013): 045502. http://dx.doi.org/10.1088/0031-8949/87/04/045502.

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16

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 (December 2007): 434–50. http://dx.doi.org/10.1037/1082-989x.12.4.434.

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17

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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18

Lipovetsky, Stan. "Orthonormal Canonical Correlation Analysis." Open Statistics 2, no. 1 (January 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 of multivariate analysis is useful for solving various practical problems of applied statistics requiring operating with two data sets, and can be helpful in managerial estimations and decision making.

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19

Zhao, Hongmin, Dongting Sun, and Zhigang Luo. "Incremental Canonical Correlation Analysis." Applied Sciences 10, no. 21 (November 4, 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 matrices. More importantly, to avoid matrix inverse, we save overhead time by using sequential singular value decomposition (SVD), which is still efficient in case when the number of samples is sufficiently few. Driven by visual tracking, which tracks a specific target in a video sequence, we readily apply the proposed ICCA for this task through some essential modifications to evaluate its efficacy. Extensive experiments on several video sequences show the superiority of ICCA when compared to several classical trackers.

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20

Novak, A. I., and Y. O. Lyashchuk. "Biological risk correlation analysis." Proceedings of the Voronezh State University of Engineering Technologies 81, no. 4 (February 11, 2020): 40–45. http://dx.doi.org/10.20914/2310-1202-2019-4-40-45.

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21

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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22

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

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23

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

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24

Cocozzelli, Carmelo. "Understanding Canonical Correlation Analysis." Journal of Social Service Research 13, no. 4 (August 10, 1990): 19–42. http://dx.doi.org/10.1300/j079v13n04_02.

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25

Larner, Ken, and Valmore Celis. "Selective-correlation velocity analysis." GEOPHYSICS 72, no. 2 (March 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 reliability and resolution of velocity analysis. In selective-correlation velocity analysis, we include in the summation only crosscorrelations for those pairs of traces with relative differential moveout of reflections exceeding a chosen threshold value. Comparisons of performance on CMP gathers, both synthetic and field-data, show that selective-correlation velocity analysis considerably enhances the resolving power of velocity spectra over that of conventional crosscorrelation sum (normalized or unnormalized) in the presence of closely interfering reflections, statics distortions, and random noise, at no sacrifice in quality of results, and does so at computational cost comparable to that for conventional velocity analysis.

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26

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

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27

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

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28

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

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29

Zhang, Enhao, Xiaohong Chen, and Liping Wang. "Consistent Discriminant Correlation Analysis." Neural Processing Letters 52, no. 1 (June 27, 2020): 891–904. http://dx.doi.org/10.1007/s11063-020-10285-w.

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30

Sakar, C. Okan, Olcay Kursun, and Fikret Gurgen. "Ensemble canonical correlation analysis." Applied Intelligence 40, no. 2 (August 13, 2013): 291–304. http://dx.doi.org/10.1007/s10489-013-0464-2.

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31

Hardoon, David R., and John Shawe-Taylor. "Sparse canonical correlation analysis." Machine Learning 83, no. 3 (November 6, 2010): 331–53. http://dx.doi.org/10.1007/s10994-010-5222-7.

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32

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 (June 1, 2021): 012064. http://dx.doi.org/10.1088/1742-6596/1948/1/012064.

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33

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

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34

Grone, Robert, and Stephen Pierce. "Permanental Inequalities for Correlation Matrices." SIAM Journal on Matrix Analysis and Applications 9, no. 2 (April 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 (July 9, 2024): 1071–85. http://dx.doi.org/10.9734/jeai/2024/v46i72660.

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Анотація:

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 branches per plant and days to first flowering, while negatively correlating with nodes on main branches and pod diameter. Traits such as number of pods per plant and average pod weight showed strong positive correlations with pod yield, highlighting their importance in breeding programs. Path coefficient analysis revealed significant positive direct effects on pod yield per plant from traits including plant height, branches per plant, and average pod weight. Indirect effects through traits like days to first flowering and non-reducing sugars also contributed to pod yield. These insights into trait correlations and effects are crucial for developing superior cowpea genotypes with enhanced yield and agronomic traits. The findings emphasize the importance of genetic variability in breeding programs, enabling the selection of superior genotypes to improve cowpea productivity.

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36

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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37

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 (June 5, 2016): 99–106. http://dx.doi.org/10.17656/jzs.10538.

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38

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

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39

Qiu, Lin, and Vernon M. Chinchilli. "Probabilistic canonical correlation analysis for sparse count data." Journal of Statistical Research 56, no. 1 (February 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 features. We propose a model-based probabilistic approach for correlation and canonical correlation estimation for two sparse count data sets. Probabilistic sparse CCA (PSCCA) demonstrates that correlations and canonical correlations estimated at the natural parameter level are more appropriate than traditional estimation methods applied to the raw data. We demonstrate through simulation studies that PSCCA outperforms other standard correlation approaches and sparse CCA approaches in estimating the true correlations and canonical correlations at the natural parameter level. We further apply the PSCCA method to study the association of miRNA and mRNA expression data sets from a squamous cell lung cancer study, ﬁnding that PSCCA can uncover a large number of strongly correlated pairs than standard correlation and other sparse CCA approaches. Journal of Statistical Research 2022, Vol. 56, No. 1, pp. 73-98

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40

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 (September 30, 2012): 1878–83. http://dx.doi.org/10.6109/jkiice.2012.16.9.1878.

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41

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 (August 21, 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 concentrations by using multiple correlation analysis, the independent linear interpretation ratio between the six pollutant concentrations by using partial correlation analysis, at last, a pollutant independent emission index is defined to describe the independent emission level of one pollutant, then utilize the index to study the distribution characteristics of six main pollution concentrations in the study period in Zhengzhou. The results show that there is a significant correlation between the six pollutant concentrations. PM2.5, O3 and PM10 are the primary pollutants in Zhengzhou, the PM2.5 concentration is controlled by PM10 concentration and CO concentration, similarly, the PM10 concentration is controlled by PM2.5 concentration. In the polluted weather, O3 has the highest level of independent emissions. The main task of Zhengzhou in pollutant composition governance is to control the emission of PM2.5 and O3.

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42

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) (January 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 blocks of Kalman filters, including the augmented stacked measurement, sequential and data compression architectures. Next, convex combination architecture will be investigated and some theoretical results concerning its extension as well as in case of presence of correlation are investigated. Two special cases of correlation are highlighted. This concerns the case of presence of only two correlated tracks among all tracks and the case of weak correlation. In both cases some original theoretical results are put forward. Finally, links with related fusion architectures is investigated.

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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 (July 1990): 361–68. http://dx.doi.org/10.1137/0611024.

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44

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

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45

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 (September 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 AR(1) and the DFA correlation exponent. The higher the interaction constant the higher is the short-range correlation exponent. They are exponentially related. The investigation was extended to AR(2) processes. The presence of an interaction between distant terms with characteristic time constant in the series, in addition to a near by interaction will increase the correlation exponent and the range of correlation while the effect of a distant negative interaction will significantly decrease the range of interaction, only. This analysis demonstrate the possibility to identify an AR(1) model in an unknown DFA plot or to distinguish between AR(1) and AR(2) models.

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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 (January 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 feature correlations. Anomaly features that show direct, inverse, or no correlations between data sets may be separated by the application of correlation filters in the frequency domains of the data sets. The correlation filter passes or rejects wavenumbers between coregistered data sets based on the correlation coefficient between common wavenumbers as given by the cosine of their phase difference. Standardizing and summing the filtered outputs where directly correlative features have been enhanced yields local favorability indices that optimize the perception of these features. Differencing the standardized outputs where inversely correlative features have been enhanced, on the other hand, provides favorability indices that improve the perception of the inverse correlations. This study includes a generic example, as well as magnetic and gravity anomaly profile examples that illustrate the usefulness of these procedures for extracting correlative features between digital data sets.

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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 (May 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 correlations between the core flux density and the jet position angles on different scales, which validated the plausible predictions of the jet with precession characteristics. Meanwhile, there is a variation in the offset between the core electric vector position angle and the inner-jet position angle over time at 15 and 43 GHz.

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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 (September 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 to encode correlations between images and their symmetry samples. To verify effectiveness, four public image databases were tested. The result was that SRCCA was more robust than CCA and had good performance for image classification.

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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 (March 30, 2018): 195–206. http://dx.doi.org/10.22207/jpam.12.1.25.

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Dr.K, Uma Pavan Kumar, and Kalimuthu Dr.M. "Performance Analysis of Naïve Bayes Correlation Models in Machine Learning." International Journal of Psychosocial Rehabilitation 24, no. 04 (February 29, 2020): 1153–57. http://dx.doi.org/10.37200/ijpr/v24i4/pr201088.

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