Covariance eigenpairs neighbour distance for fault detection in chemical processes
Sign Up to like & getrecommendations! Published in 2018 at "Canadian Journal of Chemical Engineering"
DOI: 10.1002/cjce.22920
Abstract: This paper presents a new data-driven fault detection method called covariance eigenpairs neighbor distance (CEND) for monitoring chemical processes. It processes measured data in one-step sliding windows to estimate covariance, and the eigenpairs of each… read more here.
Keywords: fault; detection; covariance eigenpairs; distance ... See more keywords
Incorporating covariate information in the covariance structure of misaligned spatial data
Sign Up to like & getrecommendations! Published in 2020 at "Environmetrics"
DOI: 10.1002/env.2623
Abstract: Incorporating covariates in the second‐ structure of spatial processes is an effective way of building flexible nonstationary covariance models. Fitting these covariances requires covariates to already exist at locations where there is response data. However,… read more here.
Keywords: covariance structure; response data; structure; covariate ... See more keywords
Random fields on the hypertorus: Covariance modeling and applications
Sign Up to like & getrecommendations! Published in 2021 at "Environmetrics"
DOI: 10.1002/env.2701
Abstract: This article gives a comprehensive theoretical framework to the modeling, inference, and applications of Gaussian random fields using what we term the hypertorus as an index set. The hypertorus is obtained through a product of… read more here.
Keywords: hypertorus covariance; random fields; seasonality; covariance ... See more keywords
Associations of white matter hyperintensities with networks of gray matter blood flow and volume in midlife adults: A coronary artery risk development in young adults magnetic resonance imaging substudy.
Sign Up to like & getrecommendations! Published in 2022 at "Human brain mapping"
DOI: 10.1002/hbm.25876
Abstract: White matter hyperintensities (WMHs) are emblematic of cerebral small vessel disease, yet effects on the brain have not been well characterized at midlife. Here, we investigated whether WMH volume is associated with brain network alterations… read more here.
Keywords: cbf gmv; gray matter; covariance; volume ... See more keywords
Matching, induction, and covariance with mixed response-contingent food and noncontingent food.
Sign Up to like & getrecommendations! Published in 2021 at "Journal of the experimental analysis of behavior"
DOI: 10.1002/jeab.689
Abstract: The multiscale molar view of behavior is based on three basic laws of behavior: the Law of Allocation, the Law of Induction, and the Law of Covariance. Experiments that mix response-contingent food with noncontingent food… read more here.
Keywords: response contingent; noncontingent food; food; covariance ... See more keywords
The impact of using reconditioned correlated observation‐error covariance matrices in the Met Office 1D‐Var system
Sign Up to like & getrecommendations! Published in 2020 at "Quarterly Journal of the Royal Meteorological Society"
DOI: 10.1002/qj.3741
Abstract: Recent developments in numerical weather prediction have led to the use of correlated observation‐error covariance (OEC) information in data assimilation and forecasting systems. However, diagnosed OEC matrices are often ill‐conditioned and may cause convergence problems… read more here.
Keywords: system; convergence; oec matrix; correlated observation ... See more keywords
Background error statistics in the Tropics: Structures and impact in a convective‐scale numerical weather prediction system
Sign Up to like & getrecommendations! Published in 2020 at "Quarterly Journal of the Royal Meteorological Society"
DOI: 10.1002/qj.3785
Abstract: The background error covariance matrix plays a vital role in any data assimilation system. Proper specification, which is determined by the forecast system set‐up, is often required. Previous studies have investigated its relevance in various… read more here.
Keywords: system; background error; convective scale; covariance ... See more keywords
Why does EnKF suffer from analysis overconfidence? An insight into exploiting the ever‐increasing volume of observations
Sign Up to like & getrecommendations! Published in 2021 at "Quarterly Journal of the Royal Meteorological Society"
DOI: 10.1002/qj.3970
Abstract: Ensemble Kalman filters (EnKF) are empirically known to suffer from insufficient posterior spread and this issue is aggravated when assimilating a large volume of observations. This problem, commonly called analysis underdispersion or analysis overconfidence, has… read more here.
Keywords: analysis overconfidence; analysis; covariance; volume observations ... See more keywords
Multiple multi-sample testing under arbitrary covariance dependency.
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in medicine"
DOI: 10.1002/sim.9761
Abstract: Modern high-throughput biomedical devices routinely produce data on a large scale, and the analysis of high-dimensional datasets has become commonplace in biomedical studies. However, given thousands or tens of thousands of measured variables in these… read more here.
Keywords: multiple multi; testing arbitrary; dependency; covariance ... See more keywords
Estimation of covariance and precision matrix, network structure, and a view toward systems biology
Sign Up to like & getrecommendations! Published in 2017 at "Wiley Interdisciplinary Reviews: Computational Statistics"
DOI: 10.1002/wics.1415
Abstract: Covariance matrix and its inverse, known as the precision matrix, have many applications in multivariate analysis because their elements can exhibit the variance, correlation, covariance, and conditional independence between variables. The practice of estimating the… read more here.
Keywords: biology; network; precision matrix; covariance ... See more keywords
High‐dimensional covariance estimation for Gaussian directed acyclic graph models with given order
Sign Up to like & getrecommendations! Published in 2019 at "Wiley Interdisciplinary Reviews: Computational Statistics"
DOI: 10.1002/wics.1468
Abstract: The covariance matrix is a fundamental quantity that helps us understand the nature of relationships among variables in a multivariate data set. Estimating the covariance matrix can be challenging in modern applications where the number… read more here.
Keywords: estimation gaussian; dimensional covariance; covariance; covariance matrix ... See more keywords