Results 21 to 30 of about 2,491,525 (260)
Data reduction algorithm for correlated data in the smart grid
IET Smart Grid, 2021 Smart grids are intelligent electrical networks that incorporate information and communication technology (ICT) to provide data services for the power grid.
Zoya Pourmirza, Sara Walker, John Brooke
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A Bayesian Joint Modeling Using Gaussian Linear Latent Variables for Mixed Correlated Outcomes with Possibility of Missing Values [PDF]
Journal of Statistical Theory and Applications (JSTA), 2016 This paper proposes a Bayesian approach for the analysis of mixed correlated nominal, ordinal and continuous outcomes with possibility of missing values using a variation of Markov Chain Monte Carlo (MCMC) method named Parameter Expanded and ...
Sayed Jamal Mirkamali, Mojtaba Ganjali
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Data-Driven Method to Quantify Correlated Uncertainties
IEEE Access, 2023 Polynomial chaos (PC) has been proven to be an efficient method for uncertainty quantification, but its applicability is limited by two strong assumptions: the mutual independence of random variables and the requirement of exact knowledge about the ...
Jeahan Jung, Minseok Choi
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Missing data correlation computations [PDF]
Mathematics of Computation, 1962 1. A. L. DULMAGE & N. S. MENDELSOHN, "A structure theory of bipartite graphs of finite exterior dimension," Trans. Roy. Soc. Canada, Sect. III 53, 1959, p. 1-13. 2. D. M. JOHNSON, A. L. DULMAGE, & N. S. MENDELSOHN, "Connectivity and reducibility of graphs", Canad. J. Math., 14,1962, p. 529-539. 3. A. L. DULMAGE & N. S.
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BMC Medical Research Methodology, 2022 Background As the cost of RNA-sequencing decreases, complex study designs, including paired, longitudinal, and other correlated designs, become increasingly feasible.
Elizabeth A. Wynn +3 more
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A Flexible Multivariate Distribution for Correlated Count Data
Stats, 2021 Multivariate count data are often modeled via a multivariate Poisson distribution, but it contains an underlying, constraining assumption of data equi-dispersion (where its variance equals its mean).
Kimberly F. Sellers +3 more
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Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso
BMC Bioinformatics, 2023 In clinical trials, identification of prognostic and predictive biomarkers has became essential to precision medicine. Prognostic biomarkers can be useful for the prevention of the occurrence of the disease, and predictive biomarkers can be used to ...
Wencan Zhu +2 more
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BMC Medical Research Methodology, 2020 Background Elderly population’s health is a major concern for most industrial nations. National health surveys provide a measure of the state of elderly health. One such survey is the Chinese Longitudinal Healthy Longevity Survey.
Elsa Vazquez-Arreola +2 more
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lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models
BMC Bioinformatics, 2022 Background Studies that utilize RNA Sequencing (RNA-Seq) in conjunction with designs that introduce dependence between observations (e.g. longitudinal sampling) require specialized analysis tools to accommodate this additional complexity.
Brian E. Vestal +2 more
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Physical Review D, 1994 We discuss fitting correlated data - with the example of hadron mass spectroscopy in mind. The main conclusion is that the method of minimising correlated $ ^2$ is unreliable if the data sample is too small.
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