A supplement on CLT for LSS under a large dimensional generalized spiked covariance model

2018 ◽  
Vol 138 ◽  
pp. 57-65 ◽  
Author(s):  
Jiaqi Chen ◽  
Yangchun Zhang ◽  
Weiming Li ◽  
Boping Tian
Keyword(s):  
Covariance Model

scholarly journals Discussion on Competition for Spatial Statistics for Large Datasets

2021 ◽  
Author(s):  
Roman Flury ◽  
Reinhard Furrer
Keyword(s):  
Spatial Statistics ◽  
Covariance Function ◽  
Likelihood Function ◽  
Large Datasets ◽  
Missing Observations ◽  
Covariance Model ◽  
Compactly Supported ◽  
Covariance Tapering ◽  
Simple Kriging ◽  
Estimated Parameters

AbstractWe discuss the experiences and results of the AppStatUZH team’s participation in the comprehensive and unbiased comparison of different spatial approximations conducted in the Competition for Spatial Statistics for Large Datasets. In each of the different sub-competitions, we estimated parameters of the covariance model based on a likelihood function and predicted missing observations with simple kriging. We approximated the covariance model either with covariance tapering or a compactly supported Wendland covariance function.

Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model

2002 ◽  
Vol 49 (6) ◽  
pp. 533-539 ◽  
Author(s):  
H.M. Huizenga ◽  
J.C. de Munck ◽  
L.J. Waldorp ◽  
R.P.P.P. Grasman
Keyword(s):  
Source Analysis ◽  
Covariance Model ◽  
Noise Covariance ◽  
Parametric Noise

The Matrix-Logarithmic Covariance Model

1996 ◽  
Vol 91 (433) ◽  
pp. 198-210 ◽  
Author(s):  
Tom Y. M. Chiu ◽  
Tom Leonard ◽  
Kam-Wah Tsui
Keyword(s):  
Covariance Model ◽  
The Matrix

scholarly journals Rfam 14: expanded coverage of metagenomic, viral and microRNA families

2020 ◽  
Vol 49 (D1) ◽  
pp. D192-D200 ◽  
Author(s):  
Ioanna Kalvari ◽  
Eric P Nawrocki ◽  
Nancy Ontiveros-Palacios ◽  
Joanna Argasinska ◽  
Kevin Lamkiewicz ◽  
...  
Keyword(s):  
Similarity Search ◽  
Sequence Similarity ◽  
Sequence Similarity Search ◽  
Covariance Model ◽  
Rna Sequences ◽  
Multiple Sequence ◽  
The Family ◽  
Recent Developments ◽  
Community Contribution ◽  
Website Features

Abstract Rfam is a database of RNA families where each of the 3444 families is represented by a multiple sequence alignment of known RNA sequences and a covariance model that can be used to search for additional members of the family. Recent developments have involved expert collaborations to improve the quality and coverage of Rfam data, focusing on microRNAs, viral and bacterial RNAs. We have completed the first phase of synchronising microRNA families in Rfam and miRBase, creating 356 new Rfam families and updating 40. We established a procedure for comprehensive annotation of viral RNA families starting with Flavivirus and Coronaviridae RNAs. We have also increased the coverage of bacterial and metagenome-based RNA families from the ZWD database. These developments have enabled a significant growth of the database, with the addition of 759 new families in Rfam 14. To facilitate further community contribution to Rfam, expert users are now able to build and submit new families using the newly developed Rfam Cloud family curation system. New Rfam website features include a new sequence similarity search powered by RNAcentral, as well as search and visualisation of families with pseudoknots. Rfam is freely available at https://rfam.org.

scholarly journals Modelling spatial covariances for terrestrial water storage variations verified with synthetic GRACE-FO data

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Eva Boergens ◽  
Henryk Dobslaw ◽  
Robert Dill ◽  
Maik Thomas ◽  
Christoph Dahle ◽  
...  
Keyword(s):  
Water Storage ◽  
Longitudinal Direction ◽  
Correlation Model ◽  
Simulation Environment ◽  
Terrestrial Water Storage ◽  
Covariance Model ◽  
Spatial Correlation Structure ◽  
Terrestrial Water ◽  
Covariance Models ◽  
Standard Deviations

AbstractGridded terrestrial water storage (TWS) variations observed by GRACE or GRACE-FO typically show a spatial correlation structure that is both anisotropic (direction-dependent) and non-homogeneous (latitude-dependent). We introduce a new correlation model to represent this structure. This correlation model allows GRACE and GRACE-FO data users to get realistic correlations of the TWS grids without the need to derive them from the formal spherical harmonic uncertainties. Further, we found that the modelled correlations fit the spatial structure of uncertainties to a greater extent in a simulation environment. The model is based on a direction-dependent Bessel function of the first kind which allows to model the longer correlation lengths in the longitudinal direction via a shape parameter, and also to account for residual GRACE striping errors that might remain after spatial filtering. The global scale and shape parameters vary with latitude by means of even Legendre polynomials. The correlation between two points transformed to covariance by scaling with the standard deviations of each point. The covariance model is valid on the sphere which is empirically verified with a Monte-Carlo approach. The covariance model is subsequently applied to 5 years of simulated GRACE-FO data which allow for immediate validation with true uncertainties from the differences between the input mass signal and the recovered gravity fields. Four different realisations of the point standard deviations were tested: two based on the formal errors provided with the simulated Stokes coefficients, and two based on empirical standard deviations, where the first is spatially variant and temporally invariant, and the second spatially invariant and temporally variant. These four different covariance models are applied to compute TWS time series uncertainties for both the fifty largest discharge basins and regular grid cells over the continents. These four models are compared with the true uncertainties available in the simulations. The two empirically-based covariance models provide more realistic TWS uncertainties than the ones based on the formal errors. Especially, the empirically-based covariance models are better in reflecting the spatial pattern of the uncertainties of the simulated GRACE-FO data including their latitude dependence. However, these modelled uncertainties are in general too large. But with only one global scaling factor, a statistical test confirms the equivalence between the empirically-based covariance model with temporally variable point standard deviations and the true uncertainties. Thus at the end, this covariance model represents the closest fit in the simulation environment. The simulated GRACE-FO data are assumed to be very realistic which is why we recommend the new covariance model to be further investigated for the characterisation of real GRACE and GRACE-FO terrestrial water storage data.

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