Topic
Partial correlation
About: Partial correlation is a research topic. Over the lifetime, 917 publications have been published within this topic receiving 59517 citations.
Papers published on a yearly basis
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Papers
TL;DR: In this article, an asymptotic theory for computing volumes of regions in the parameter space of a directed Gaussian graphical model that are obtained by bounding partial correlations is developed.
Abstract: An asymptotic theory is developed for computing volumes of regions in the parameter space of a directed Gaussian graphical model that are obtained by bounding partial correlations. We study these volumes using the method of real log canonical thresholds from algebraic geometry. Our analysis involves the computation of the singular loci of correlation hypersurfaces. Statistical applications include the strong-faithfulness assumption for the PC-algorithm, and the quantification of confounder bias in causal inference. A detailed analysis is presented for trees, bow-ties, tripartite graphs, and complete graphs.
15 citations
TL;DR: The partial correlation coefficient is extended in the presence of missing data using the expectation-maximization (EM) algorithm, and it is compared with a multiple imputation method and complete case analysis using simulation studies to find the best approach.
Abstract: In the dementia area it is often of interest to study relationships among regional brain measures; however, it is often necessary to adjust for covariates. Partial correlations are frequently used to correlate two variables while adjusting for other variables. Complete case analysis is typically the analysis of choice for partial correlations with missing data. However, complete case analysis will lead to biased and inefficient results when the data are missing at random. We have extended the partial correlation coefficient in the presence of missing data using the expectation-maximization (EM) algorithm, and compared it with a multiple imputation method and complete case analysis using simulation studies. The EM approach performed the best of all methods with multiple imputation performing almost as well. These methods were illustrated with regional imaging data from an Alzheimer’s disease study.
15 citations
TL;DR: In this article, a critical evaluation of the procedure that Naroll has suggested for using partial correlation to control for the effects of diffusion on estimates of functional relationships is provided, and Naroll's formula does not provide good estimates of the true functional relationships.
Abstract: This paper provides a critical evaluation of the procedure that Naroll has suggested for using partial correlation to control for the effects of diffusion on estimates of functional relationships An examination of par tial correlation and Naroll's formula shows that they are not consistent Hypothetical data are used to demonstrate that Naroll's formula does not provide good estimates of the true functional relationships
15 citations
TL;DR: This work proposes, for the first time, the explicit use of partial correlations in process monitoring and introduces the use of sensitivity enhancing data transformations (SET) with the ability to maximize the detection ability of all monitoring procedures based on (partial or marginal) correlation.
15 citations
TL;DR: In this article, the author obtained the large deviation principles for the empirical correlation coefficient of two Gaussian random variables X and Y, when considering two independent Gaussian Random Variants X, Y with the means X, Y (both known).
15 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 7 |
| 2024 | 11 |
| 2023 | 20 |
| 2022 | 48 |
| 2021 | 39 |
| 2020 | 43 |