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Journal articles on the topic 'Correlations'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

Szapudi, I., A. S. Szalay, and P. Boschan. "Cluster correlations from N-point correlation amplitudes." Astrophysical Journal 390 (May 1992): 350. http://dx.doi.org/10.1086/171286.

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3

Anand, Vijayakumar, Tomas Katkus, Soon Hock Ng, and Saulius Juodkazis. "Review of Fresnel incoherent correlation holography with linear and non-linear correlations [Invited]." Chinese Optics Letters 19, no. 2 (2021): 020501. http://dx.doi.org/10.3788/col202119.020501.

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4

Auletta, Gennaro. "Correlations and Hyper-Correlations." Journal of Modern Physics 02, no. 09 (2011): 958–61. http://dx.doi.org/10.4236/jmp.2011.29114.

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5

Hu, Zheng-Da, Jicheng Wang, Yixin Zhang, and Ye-Qi Zhang. "Dynamics of Nonclassical Correlations with an Initial Correlation." Journal of the Physical Society of Japan 83, no. 11 (2014): 114004. http://dx.doi.org/10.7566/jpsj.83.114004.

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6

Walther, Andreas, and Christof Faller. "Interaural correlation discrimination from diffuse field reference correlations." Journal of the Acoustical Society of America 133, no. 3 (2013): 1496–502. http://dx.doi.org/10.1121/1.4790473.

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7

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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8

Cope, Leslie, Daniel Q. Naiman, and Giovanni Parmigiani. "Integrative correlation: Properties and relation to canonical correlations." Journal of Multivariate Analysis 123 (January 2014): 270–80. http://dx.doi.org/10.1016/j.jmva.2013.09.011.

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9

Neff, T., and H. Feldmeier. "Tensor correlations in the unitary correlation operator method." Nuclear Physics A 713, no. 3-4 (2003): 311–71. http://dx.doi.org/10.1016/s0375-9474(02)01307-6.

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10

Kermarrec, Gaël, and Steffen Schön. "Taking correlations into account: a diagonal correlation model." GPS Solutions 21, no. 4 (2017): 1895–906. http://dx.doi.org/10.1007/s10291-017-0665-y.

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11

Andreev, I. "Correlation length versus radius in Bose-Einstein correlations." Nuclear Physics A 525 (April 1991): 527–30. http://dx.doi.org/10.1016/0375-9474(91)90377-i.

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12

Lipa, P., P. Carruthers, H. C. Eggers, and B. Buschbeck. "The correlation integral as probe of multiparticle correlations." Physics Letters B 285, no. 3 (1992): 300–308. http://dx.doi.org/10.1016/0370-2693(92)91468-o.

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13

Ehling, Paul, and Christian Heyerdahl-Larsen. "Correlations." Management Science 63, no. 6 (2017): 1919–37. http://dx.doi.org/10.1287/mnsc.2015.2413.

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14

Guo, Zhihua, Huaixin Cao, and Zhengli Chen. "Distinguishing classical correlations from quantum correlations." Journal of Physics A: Mathematical and Theoretical 45, no. 14 (2012): 145301. http://dx.doi.org/10.1088/1751-8113/45/14/145301.

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15

Khrennikov, Andrei. "Quantum correlations from classical Gaussian correlations." Journal of Russian Laser Research 30, no. 5 (2009): 472–79. http://dx.doi.org/10.1007/s10946-009-9095-9.

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16

Eliazar, Iddo. "From micro-correlations to macro-correlations." Annals of Physics 374 (November 2016): 138–61. http://dx.doi.org/10.1016/j.aop.2016.07.027.

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17

Hamilton, A. J. S., and J. R. ,. III Gott. "Cluster-cluster correlations and constraints on the correlation hierarchy." Astrophysical Journal 331 (August 1988): 641. http://dx.doi.org/10.1086/166587.

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18

Arias de Saavedra, F., та E. Buendía. "σz-dependent correlations with other correlation mechanisms in liquidHe3". Physical Review B 46, № 21 (1992): 13934–41. http://dx.doi.org/10.1103/physrevb.46.13934.

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19

Høstmark, Arne Torbjørn. "DISTRIBUTION DEPENDENT CORRELATIONS: A MATHEMATICAL PRINCIPLE UTILIZED IN PHYSIOLOGY, OR CORRELATION BIAS?" International Journal of Research -GRANTHAALAYAH 8, no. 11 (2020): 63–75. http://dx.doi.org/10.29121/granthaalayah.v8.i11.2020.1470.

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In many studies, we may raise the question of whether relative amounts of particular variables are positively or negatively associated, but investigations specifically focusing upon this issue seem hard to find. Previously, we reported some general rules for associations between relative amounts of positive scale variables. The main research question of the present work was: How are correlations between percentages of the same sum brought about? One particular feature of such correlations seemed to be that distributions (ranges) of the variables were crucial for obtaining either positive or ne

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20

Keating, J. P., and D. J. Smith. "Twin prime correlations from the pair correlation of Riemann zeros." Journal of Physics A: Mathematical and Theoretical 52, no. 36 (2019): 365201. http://dx.doi.org/10.1088/1751-8121/ab3521.

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21

Ariunbold, Gombojav O., Yuri V. Rostovtsev, Vladimir A. Sautenkov, and Marlan O. Scully. "Intensity correlation and anti-correlations in coherently driven atomic vapor." Journal of Modern Optics 57, no. 14-15 (2010): 1417–27. http://dx.doi.org/10.1080/09500341003777905.

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22

Iskhakov, R. S., V. A. Ignatchenko, S. V. Komogortsev, and A. D. Balaev. "Study of magnetic correlations in nanostructured ferromagnets by correlation magnetometry." Journal of Experimental and Theoretical Physics Letters 78, no. 10 (2003): 646–50. http://dx.doi.org/10.1134/1.1644310.

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23

Fataftah, Hiba, and Wael Karain. "Detecting protein atom correlations using correlation of probability of recurrence." Proteins: Structure, Function, and Bioinformatics 82, no. 9 (2014): 2180–89. http://dx.doi.org/10.1002/prot.24574.

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24

Ryu, J., Y. i. Jo, and S. H. Lee. "Correlation between Signal Correlations and Noise Correlations among Local Cortical Populations Reveals the Functional Architecture of Early Visual Cortex." Journal of Vision 12, no. 9 (2012): 1306. http://dx.doi.org/10.1167/12.9.1306.

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25

Nuzum, C. Thomas. "Morphological Correlations." Science 229, no. 4712 (1985): 428. http://dx.doi.org/10.1126/science.229.4712.428.b.

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26

Nicolae, Moroianu, and Moroianu Daniela. "Inflations Correlations." Annales Universitatis Apulensis Series Oeconomica 3, no. 8 (2006): 109–12. http://dx.doi.org/10.29302/oeconomica.2006.8.3.19.

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27

Latham, Peter E. "Correlations demystified." Nature Neuroscience 20, no. 1 (2016): 6–8. http://dx.doi.org/10.1038/nn.4455.

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28

Li, Wentian, and Kunihiko Kaneko. "DNA correlations." Nature 360, no. 6405 (1992): 635–36. http://dx.doi.org/10.1038/360635b0.

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29

Munson, Peter J., Ronald C. Taylor, and George S. Michaels. "DNA correlations." Nature 360, no. 6405 (1992): 636. http://dx.doi.org/10.1038/360636a0.

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30

Olkin, Ingram, and Jeremy D. Finn. "Correlations redux." Psychological Bulletin 118, no. 1 (1995): 155–64. http://dx.doi.org/10.1037/0033-2909.118.1.155.

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31

Wesson, John. "Tricky correlations." Physics World 29, no. 5 (2016): 22. http://dx.doi.org/10.1088/2058-7058/29/5/34.

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32

Aspden, H. "Constant correlations." American Journal of Physics 54, no. 11 (1986): 967. http://dx.doi.org/10.1119/1.14829.

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33

Zahn, Rainer. "Core correlations." Nature 371, no. 6495 (1994): 289–90. http://dx.doi.org/10.1038/371289a0.

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34

Balucani, Umberto, M. Howard Lee, and Valerio Tognetti. "Dynamical correlations." Physics Reports 373, no. 6 (2003): 409–92. http://dx.doi.org/10.1016/s0370-1573(02)00430-1.

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35

NUZUM, C. T. "Morphological Correlations." Science 229, no. 4712 (1985): 428. http://dx.doi.org/10.1126/science.229.4712.428-a.

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36

Baba, Kunihiro, and Ritei Shibata. "Multiplicative Correlations." Annals of the Institute of Statistical Mathematics 58, no. 2 (2006): 311–26. http://dx.doi.org/10.1007/s10463-006-0036-x.

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37

Luo, Shun-Long, and Nan Li. "Quantum Correlations Reduce Classical Correlations with Ancillary Systems." Chinese Physics Letters 27, no. 12 (2010): 120304. http://dx.doi.org/10.1088/0256-307x/27/12/120304.

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38

Wang, Min, Fang Chen, Tao Lu, and Jianping Dong. "Bayesian t-tests for correlations and partial correlations." Journal of Applied Statistics 47, no. 10 (2019): 1820–32. http://dx.doi.org/10.1080/02664763.2019.1695760.

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39

Smith, Jr. Daniel M. "City-City Correlations to Introduce Galaxy-Galaxy Correlations." Communicating Astronomy with the Public Journal 8, no. 2 (2014): 31–34. https://doi.org/10.5281/zenodo.14928982.

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The large-scale structure of the Universe, vividly displayed by the spatial distribution of galaxies, is characterised quantitatively by the two-point galaxy–galaxy correlation function. But the meaning of the correlation function is somewhat abstract because it does not have a ready analogy. This work computes the two-dimensional, two-point city–city correlation function for three populous regions of the United States, demonstrating that the city–city correlation function is analogous to the galaxy–galaxy correlation function determined from Sloan Digital Sky Survey da

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40

He, Yan, Chao Tang, and Dongsheng Chen. "Evaluation of heat transfer correlations for two-phase flow boiling in twisted tapes inserted tubes." Journal of Physics: Conference Series 2758, no. 1 (2024): 012016. http://dx.doi.org/10.1088/1742-6596/2758/1/012016.

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Abstract Many experimental researches were conducted in the twisted tapes inserted tubes, and many correlations were proposed and evaluated. However, most of the evaluations were based on specific experimental data, and the number of correlations evaluated was limited in each paper. This paper conducted a review of correlations and experimental investigations of the two-phase flow boiling heat transfer coefficient for twisted tapes inserted tubes, the key forms of the 5 correlations were reviewed, and the prediction accuracy of the 5 correlations was evaluated against the 508 published experim

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41

Joe, George W., and Jorge L. Mendoza. "The Internal Correlation: Its Applications in Statistics and Psychometrics." Journal of Educational Statistics 14, no. 3 (1989): 211–26. http://dx.doi.org/10.3102/10769986014003211.

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The internal correlation, a measure of dependency in a set of variables, is discussed and generalized. This coefficient is an upper bound to the product moment correlations, multiple correlations, and canonical correlations that can be defined in a set of variables. Applications of the internal correlation coefficient and its generalizations are given for a number of data-analytic situations. Where appropriate, we discuss tests of significance. We illustrate the internal correlation and expand the concept to a series of additional indices: local internal, up-internal, and down-internal correla

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42

Liang, Ke, Sihang Zhou, Meng Liu, et al. "Hawkes-Enhanced Spatial-Temporal Hypergraph Contrastive Learning Based on Criminal Correlations." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8733–41. http://dx.doi.org/10.1609/aaai.v38i8.28719.

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Crime prediction is a crucial yet challenging task within urban computing, which benefits public safety and resource optimization. Over the years, various models have been proposed, and spatial-temporal hypergraph learning models have recently shown outstanding performances. However, three correlations underlying crime are ignored, thus hindering the performance of previous models. Specifically, there are two spatial correlations and one temporal correlation, i.e., (1) co-occurrence of different types of crimes (type spatial correlation), (2) the closer to the crime center, the more dangerous

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43

Shah, Mirza M. "Further Study and Development of Correlations for Heat Transfer during Subcooled Boiling in Plain Channels." Fluids 8, no. 9 (2023): 245. http://dx.doi.org/10.3390/fluids8090245.

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The author’s published correlations for subcooled boiling in channels are further studied and developed in this work. The areas explored include choice of equivalent diameters for annuli and partially heated channels, effects of flow direction, micro-gravity, and orientation of heated surface. A new correlation is developed, which is a modification of the author’s earlier correlation. The author’s previous correlations and the new correlation are compared with a very wide range of test data for round tubes, rectangular channels, and annuli. Several other correlations are also compared with the

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44

Copi, Craig J., James Gurian, Arthur Kosowsky, Glenn D. Starkman, and Hezi Zhang. "Exploring suppressed long-distance correlations as the cause of suppressed large-angle correlations." Monthly Notices of the Royal Astronomical Society 490, no. 4 (2019): 5174–81. http://dx.doi.org/10.1093/mnras/stz2962.

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ABSTRACT The absence of large-angle correlations in the map of cosmic microwave background temperature fluctuations is among the well-established anomalies identified in full-sky and cut-sky maps over the past three decades. Suppressed large-angle correlations are rare statistical flukes in standard inflationary cosmological models. One natural explanation could be that the underlying primordial density perturbations lack correlations on large distance scales. To test this idea, we replace Fourier modes by a wavelet basis with compact spatial support. While the angular correlation function of

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45

Elghannay, Husam A., and Yousef M. F. El Hasadi. "DEVELOPMENT OF DRAG COEFFICIENT CORRELATIONS FOR CIRCULAR CYLINDER USING SYMBOLIC REGRESSION." Al-Mukhtar Journal of Engineering Research 7, no. 1 (2024): 20–28. http://dx.doi.org/10.54172/wadd2q70.

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The current paper provides a symbolic regression-based correlation for the drag coefficient for circular cylinder. The correlation is intended to be applicable over a wide range of flow regimes namely that range from the creeping flow regime up to the turbulent flow regime. Demo version of TuringBot symbolic regression software was used to develop different correlations using different sets of data. Experimental set of data was used in one run whereas steady numerical results for Reynolds number up to ~ 25 were used in generating a second set of formulas. In a different run data generated usin

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46

She, Yangzi. "Evaluation and Research on Correlation for Spray Cooling." Journal of Physics: Conference Series 2860, no. 1 (2024): 012050. http://dx.doi.org/10.1088/1742-6596/2860/1/012050.

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Abstract Spray cooling is influenced by many related parameters, making analysis of heat transfer mechanisms and prediction of cooling performance challenging. After summarizing and analyzing fifteen different forms of correlation in spray cooling literature, it appears that there are four expressions for spray cooling correlation. 2989 experimental data under different experimental conditions in twelve studies were collected to form an experimental database to evaluate the prediction of correlations. The prediction accuracy of Nusselt number correlations based on droplet diameter is considere

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47

GALKA, ANDREAS, and GERD PFISTER. "DYNAMICAL CORRELATIONS ON RECONSTRUCTED INVARIANT DENSITIES AND THEIR EFFECT ON CORRELATION DIMENSION ESTIMATION." International Journal of Bifurcation and Chaos 13, no. 03 (2003): 723–32. http://dx.doi.org/10.1142/s0218127403006881.

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We investigate the structure of dynamical correlations on reconstructed attractors which were obtained by time-delay embedding of periodic, quasi-periodic and chaotic time series. Within the specific sampling of the invariant density by a finite number of vectors which results from embedding, we identify two separate levels of sampling, corresponding to two different types of dynamical correlations, each of which produces characteristic artifacts in correlation dimension estimation: the well-known trajectory bias and a characteristic oscillation due to periodic sampling. For the second artifac

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48

McFarland, Dennis. "The Effects of Using Partial or Uncorrected Correlation Matrices When Comparing Network and Latent Variable Models." Journal of Intelligence 8, no. 1 (2020): 7. http://dx.doi.org/10.3390/jintelligence8010007.

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Network models of the WAIS-IV based on regularized partial correlation matrices have been reported to outperform latent variable models based on uncorrected correlation matrices. The present study sought to compare network and latent variable models using both partial and uncorrected correlation matrices with both types of models. The results show that a network model provided better fit to matrices of partial correlations but latent variable models provided better fit to matrices of full correlations. This result is due to the fact that the use of partial correlations removes most of the cova

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49

Al-Shammasi, A. A. "A Review of Bubblepoint Pressure and Oil Formation Volume Factor Correlations." SPE Reservoir Evaluation & Engineering 4, no. 02 (2001): 146–60. http://dx.doi.org/10.2118/71302-pa.

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Summary This paper evaluates published correlations and neural-network models for bubblepoint pressure (pb) and oil formation volume factor (Bo) for their accuracy and flexibility in representing hydrocarbon mixtures from different locations worldwide. The study presents a new, improved correlation for pb based on global data. It also presents new neural-network models and compares their performances to numerical correlations. The evaluation examines the performance of correlations with their original published coefficients and with new coefficients calculated based on global data, data from s

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50

Nyberg, Nils T., Jens Ø. Duus, and Ole W. Sørensen. "Heteronuclear Two-Bond Correlation: Suppressing Heteronuclear Three-Bond or Higher NMR Correlations while Enhancing Two-Bond Correlations Even for Vanishing2JCH." Journal of the American Chemical Society 127, no. 17 (2005): 6154–55. http://dx.doi.org/10.1021/ja050878w.

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