24 papers · ranked by Valyu relevance
M Harshvardhan, Pritam Ranjan
Pearson correlation is the default measure of association in most statistical software, yet it is only appropriate for pairs of continuous variables with a linear relationship. When variables are binary, ordinal, or categorical, specialized methods (e.g., point-biserial, polychoric, tetrachoric, and Cramér's~$V$) may…
Sunil Kumar, Ilyoung Chong
Correlation analysis is an extensively used technique that identifies interesting relationships in data. These relationships help us realize the relevance of attributes with respect to the target class to be predicted. This study has exploited correlation analysis and machine learning-based approaches to identify…
Milton Pividori, Marylyn D. Ritchie, Diego H. Milone, Casey S. Greene
Correlation coefficients are widely used to identify patterns in data that may be of particular interest. In transcriptomics, genes with correlated expression often share functions or are part of disease-relevant biological processes. Here we introduce the Clustermatch Correlation Coefficient (CCC), an efficient…
Alessandro Fontana
Methods to find correlation between variables are of interest to many disciplines, including statistics, machine learning, (big) data mining and neurosciences. Parameters that measure correlation between two variables are of limited utility when used with multiple variables. In this work, I propose a simple criterion…
Rami Mahdi
In this paper, a robust non-parametric measure of statistical dependence, or correlation, between two random variables is presented. The proposed coefficient is a permutation-like statistic that quantifies how much the observed sample Sn : {(Xi , Yi), i = 1 . . . n} is discriminable from the permutated sample Sˆ n×n …
Kirsten L. Peterson, Ruben Sanchez-Romero, Ravi D. Mill, Michael W. Cole
Functional connectivity (FC) has been invaluable for understanding the brain’s communication network, with strong potential for enhanced FC approaches to yield additional insights. Unlike with the fMRI field-standard method of pairwise correlation, theory suggests that partial correlation can estimate FC without…
Adam Lehavi, Seong‐Tae Kim
—In the realm of cybersecurity, intrusion detection systems (IDS) detect and prevent attacks based on collected computer and network data. In recent research, IDS models have been constructed using machine learning (ML) and deep learning (DL) methods such as Random Forest (RF) and deep neural networks (DNN). Feature…
A. Nicholls
The calculation of error bars for quantities of interest in computational chemistry comes in two forms: (1) Determining the confidence of a prediction, for instance of the property of a molecule; (2) Assessing uncertainty in measuring the difference between properties, for instance between performance metrics of two or…
Authors not listed
Predicting which molecular systems require post-Hartree-Fock treatment remains a fundamental challenge in quantum chemistry. We introduce Fbond, a universal descriptor that quantifies electron correlation strength through the product of HOMO-LUMO gap and maximum single-orbital entanglement entropy. Validation across…
Fatih Dikbaş
Correlation remains to be one of the most widely used statistical tools for assessing the strength of relationships between data series. This paper presents a novel compositional correlation method for detecting linear and nonlinear relationships by considering the averages of all parts of all possible compositions of…
Kenneth D. Harris
Many neurophysiological signals exhibit slow continuous trends over time. Because standard correlation analyses assume that all samples are independent, they can yield apparently significant “nonsense correlations” even for signals that are completely unrelated. Here we compare the performance of several methods for…
Dean A. Pospisil, Wyeth Bair
The Pearson correlation coefficient squared, r^2^, is often used in the analysis of neural data to estimate the relationship between neural tuning curves. Yet this metric is biased by trial-to-trial variability: as trial-to-trial variability increases, measured correlation decreases. Major lines of research are…
Mohammad Tabatabai, Stephanie Bailey, Zoran Bursac, Habib Tabatabai + 2 more
'Derek Wilus' 'Karan P. Singh'] Background The most common measure of association between two continuous variables is the Pearson correlation (Maronna et al. in Safari an OMC. Robust statistics, 2019.…
Victoria Savalei, Douglas G. Bonett, Peter M. Bentler
Asymptotically optimal correlation structure methods with binary data can break down in small samples. A new correlation structure methodology based on a recently developed odds-ratio (OR) approximation to the tetrachoric correlation coefficient is proposed as an alternative to the LPB approach proposed by Lee et al.…
Edoardo Saccenti, Margriet H. W. B. Hendriks, Age K. Smilde
Correlation coefficients are abundantly used in the life sciences. Their use can be limited to simple exploratory analysis or to construct association networks for visualization but they are also basic ingredients for sophisticated multivariate data analysis methods. It is therefore important to have reliable estimates…
Ruben Sanchez-Romero, Michael W. Cole
Cognition and behavior emerge from brain network interactions, suggesting that causal interactions should be central to the study of brain function. Yet approaches that characterize relationships among neural time series—functional connectivity (FC) methods—are dominated by methods that assess bivariate statistical…
Gabriel Kennedy, Alejandro Ochoa
In studies of assortative mating, similarity between variables measured in parents is often quantified using correlation. The order of the parents within any given pair can be arbitrary in these applications, but common correlation estimators are not robust to reordering within pairs. These unordered variable pairs are…
Edoardo Saccenti, Margriet H. W. B. Hendriks, Age K. Smilde
Correlation coefficients are abundantly used in the life sciences. Their use can be limited to simple exploratory analysis or to construct association networks for visualization but they are also basic ingredients for sophisticated multivariate data analysis methods. It is therefore important to have reliable estimates…
Arnaud Chiolero, Gilles Paradis, Benjamin Rich, James A. Hanley
Analyzing the relationship between the baseline value and subsequent change of a continuous variable is a frequent matter of inquiry in cohort studies. These analyses are surprisingly complex, particularly if only two waves of data are available. It is unclear for non-biostatisticians where the complexity of this…
Xiatong Cai, Guangpeng Pei, Yuen Zhu, Donggang Guo + 1 more
With the establishment of global biological monitor network and development of remote sensing technology, data won't be a limitation, but the variance brought by spatial heterogeneous and fractal will influence correlation coefficient significantly with the enlarged sample scale. Those impede us to find more intrinsic…
Zenon Gniazdowski
The article investigates the possibility of measuring the strength of a linear correlation relationship between nominal data and numerical data. Correlation coefficients for variables coded with real numbers as well as for variables coded with complex numbers were studied. For variables coded with real numbers…
Yu-Kang Tu
Testing the relation between percentage change and baseline value has been controversial, but it is not clear why this practice may yield spurious results. In this paper, we first explained why the usual testing of the relation between percentage change and baseline value is inappropriate and then demonstrated how the…
Authors not listed
High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
Authors not listed
Material properties calculated using density functional theory (DFT) are often corrected to more closely match experimental values, but the most common correction method has flaws that lead to unphysical results and false positives in the material discovery process. In this work, we show that these flaws stem from the…