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Partial covariance based functional connectivity computation using Ledoit–Wolf covariance regularization

Matthew R. Brier, Anish Mitra, John E. McCarthy, Beau M. Ances, Abraham Z. Snyder
<span title="">2015</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sa477uo7lveh7hchpikpixop5u" style="color: black;">NeuroImage</a> </i> &nbsp;
A: BOLD correlation matrices (̂) calculated in the 36 and 264 ROI set with and without global signal regression (GSR).  ...  E: mean shrinkage coefficient () in each ROI set with and without global signal regression. Partial covariance matrices do not depend on global signal regression.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2015.07.039">doi:10.1016/j.neuroimage.2015.07.039</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26208872">pmid:26208872</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4604032/">pmcid:PMC4604032</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ktk4nnaynreklkbvp5cynlp6s4">fatcat:ktk4nnaynreklkbvp5cynlp6s4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190925021437/https://openscholarship.wustl.edu/cgi/viewcontent.cgi?article=1034&amp;context=math_facpubs" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6a/a9/6aa9861e0174b0498d0ccf1dc9899e3a5815769f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2015.07.039"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4604032" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Analysis of Covariance [chapter]

<span title="2005-01-28">2005</span> <i title="John Wiley &amp; Sons, Inc."> Statistics for Research </i> &nbsp;
The correlation between weights of brother-sister pairs may be the same for obese siblings as for those of conventional weight.  ...  Care should be taken in the interpretation of regression and correlation.  ...  With that assumption the test statistic has a t distribution regardless of the sample size. In logistic regression the test statistic has an approximate chisquare distribution.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/0471477435.ch13">doi:10.1002/0471477435.ch13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xobll62wyzg2dizclp4o5ijmze">fatcat:xobll62wyzg2dizclp4o5ijmze</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160221145547/http://www.rupp.edu.kh:80/cec/downloads/Statistics-for-Research.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/0471477435.ch13"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> wiley.com </button> </a>

Nonparametric Multivariate L1-median Regression Estimation with Functional Covariates [article]

Mohamed Chaouch, Naâmane Laïb
<span title="2013-04-16">2013</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Some numerical study in chemiometrical real data are carried out to compare the multivariate L1-median regression with the vector of marginal median regression when the covariate X is a curve as well as  ...  fidence ellipsoids for the multivariate L1-median regression in practice.  ...  Notice that the minimum and the maximum of the electricity power demand are strongly correlated.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1304.4300v1">arXiv:1304.4300v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6jcfw4edzjfflgm6vcovlscixe">fatcat:6jcfw4edzjfflgm6vcovlscixe</a> </span>
<a target="_blank" rel="noopener" href="https://archive.org/download/arxiv-1304.4300/1304.4300.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> File Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1304.4300v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Streaming Covariance Selection with Applications to Adaptive Querying in Sensor Networks

C. Anagnostopoulos, N. M. Adams, D. J. Hand
<span title="2010-01-08">2010</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bf4qaipmsrghrjyxonrsmqevie" style="color: black;">Computer journal</a> </i> &nbsp;
Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors.  ...  We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator.  ...  Our scheme cannot currently accommodate this, so we used linear interpolation to obtain a dataset with regular measurements, every 15 minutes.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/comjnl/bxp123">doi:10.1093/comjnl/bxp123</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d65wm65eh5gbbjxtxjghuhhmmy">fatcat:d65wm65eh5gbbjxtxjghuhhmmy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20110523012814/http://www.statslab.cam.ac.uk:80/~christof/papers/anagnostopoulos2009sgm.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e7/d9/e7d9a7a014fee9943257546000f85d9a8e577a5a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/comjnl/bxp123"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> oup.com </button> </a>

Cross-Covariance Models [chapter]

<span title="">2017</span> <i title="Springer International Publishing"> Encyclopedia of GIS </i> &nbsp;
The application of correlation queries used in Earth science is introduced in details as follows.  ...  Measures of spatial correlation include the cross-K function with Monte Carlo simulation, mean nearest-neighbor distance, and spatial regression models.  ...  More complex techniques were developed since then with the improvement of processing capacities but also with the development of new theoretical approaches.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-17885-1_100240">doi:10.1007/978-3-319-17885-1_100240</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2ojzb7es7rhofinw4abol6dgc4">fatcat:2ojzb7es7rhofinw4abol6dgc4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180728221740/https://link.springer.com/content/pdf/10.1007%2F978-3-319-17885-1_100240.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-17885-1_100240"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Multiple Output Regression with Latent Noise [article]

Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Mehreen Ali, Aki S. Havulinna, Marjo-Riitta Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski
<span title="2016-02-03">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Additionally, (2) assumptions about the correlation structure of the regression weights are needed.  ...  unidentifiability in reduced-rank regression models.  ...  covariates as a prior for the regression weights to enforce correlated covariates to have correlated weights.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1410.7365v2">arXiv:1410.7365v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bn4gvhiiifgbtchz4w5kgtnelq">fatcat:bn4gvhiiifgbtchz4w5kgtnelq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191016084058/https://arxiv.org/pdf/1410.7365v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a3/24/a324d83dfaa46ff4f8773a222db868c4a62c7e10.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1410.7365v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures [article]

Lea Waller, Anastasia Brovkin, Lena Dorfschmidt, Danilo Bzdok, Henrik Walter, Johann Daniel Kruschwitz
<span title="2018-07-06">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Results: In addition to previously integrated functionalities, such as network construction and graph-theoretical analyses of brain connectivity with a high-speed general linear model (GLM), users can  ...  Comparison with existing methods: Compared to other existing toolboxes, GraphVar 2.0 offers (1) comprehensive customization, (2) an all-in-one user friendly interface, (3) customizable model design and  ...  When dealing with correlated features, L1 regularization will select only one out of a set of correlated features in a winner-takes-all approach.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.00082v2">arXiv:1803.00082v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/slgntlansng73nb5c2ciofcpte">fatcat:slgntlansng73nb5c2ciofcpte</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200929162615/https://arxiv.org/ftp/arxiv/papers/1803/1803.00082.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/57/86/5786b9170536d852cf85289017e7acb294549776.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.00082v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures

L. Waller, A. Brovkin, L. Dorfschmidt, D. Bzdok, H. Walter, J.D. Kruschwitz
<span title="">2018</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/huhco7lwxvct3fbbxk44mmpflu" style="color: black;">Journal of Neuroscience Methods</a> </i> &nbsp;
Results: In addition to previously integrated functionalities, such as network construction and graph-theoretical analyses of brain connectivity with a high-speed general linear model (GLM), users can  ...  Comparison with existing methods: Compared to other existing toolboxes, GraphVar 2.0 offers (1) comprehensive customization, (2) an all-in-one user friendly interface, (3) customizable model design and  ...  When dealing with correlated features, L1 regularization will select only one out of a set of correlated features in a winner-takes-all approach.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jneumeth.2018.07.001">doi:10.1016/j.jneumeth.2018.07.001</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30026069">pmid:30026069</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/g3dv3kimuzha5lw2ufuhf4djna">fatcat:g3dv3kimuzha5lw2ufuhf4djna</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190504114302/https://hal.archives-ouvertes.fr/hal-01828991/document" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c6/9f/c69fd22a8e8875924e003b26babf694bc3d2b958.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jneumeth.2018.07.001"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Tree Ensembles with Rule Structured Horseshoe Regularization [article]

Malte Nalenz, Mattias Villani
<span title="2018-02-15">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The model is based on the RuleFit approach in Friedman and Popescu (2008) where rules from decision trees and linear terms are used in a L1-regularized regression.  ...  We modify RuleFit by replacing the L1-regularization by a horseshoe prior, which is well known to give aggressive shrinkage of noise predictor while leaving the important signal essentially untouched.  ...  The second regularizaton step learns the weights α l for the generated rules via L1-regularized (Lasso) regression, along with weights on linear terms included in the model.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1702.05008v2">arXiv:1702.05008v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aczafxrbcfatbfodvgvf6ulasi">fatcat:aczafxrbcfatbfodvgvf6ulasi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200909224737/https://arxiv.org/pdf/1702.05008v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/66/56/6656171753d1d89e58a46482b7e821b4ee2f13fa.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1702.05008v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Predicting the intensity of partially observed data from a revisited kriging for point processes [article]

Edith Gabriel and Florent Bonneu and Pascal Monestiez and Joel Chadoeuf
<span title="2015-12-15">2015</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In order to predict its local intensity, we propose to define the first- and second-order characteristics of a random field, defined as the regularized counting process, from the ones of the point process  ...  and to interpolate the intensity by using a revisited kriging of the regularized process.  ...  So, once the pair correlation function is estimated from the point data subset, one can apply our method to interpolate the intensity.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1409.6441v2">arXiv:1409.6441v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3x7fulvzkfbejg44z7zkxqsyzy">fatcat:3x7fulvzkfbejg44z7zkxqsyzy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200823022431/https://arxiv.org/pdf/1409.6441v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7b/53/7b530aec71ccf588bd42618c9b6afc4c2efb6db0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1409.6441v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

eddy4R 0.2.0: a DevOps model for community-extensible processing and analysis of eddy-covariance data based on R, Git, Docker, and HDF5

Stefan Metzger, David Durden, Cove Sturtevant, Hongyan Luo, Natchaya Pingintha-Durden, Torsten Sachs, Andrei Serafimovich, Jörg Hartmann, Jiahong Li, Ke Xu, Ankur R. Desai
<span title="2017-08-31">2017</span> <i title="Copernicus GmbH"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rqk7miszmbcxtcrxk7hjwolghq" style="color: black;">Geoscientific Model Development</a> </i> &nbsp;
</strong> Large differences in instrumentation, site setup, data format, and operating system stymie the adoption of a universal computational environment for processing and analyzing eddy-covariance (  ...  In addition, modularity permits meeting project milestones while retaining extensibility with time.</p>  ...  The resulting higher-level data products (level 1-level 4, or L1-L4) are collected from the compute nodes and, together with all contextual information, are combined into a daily L1-L4 HDF5 data file that  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/gmd-10-3189-2017">doi:10.5194/gmd-10-3189-2017</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ubcqfcmcczg5tfpg2dedco3ohq">fatcat:ubcqfcmcczg5tfpg2dedco3ohq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171202221346/https://www.geosci-model-dev.net/10/3189/2017/gmd-10-3189-2017.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/74/fc/74fc53b72fd2121136ef6a6aba48d51e516e01f6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/gmd-10-3189-2017"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Functional Regression [article]

Jeffrey S. Morris
<span title="2014-06-16">2014</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
First will be an introduction to basis functions, key building blocks for regularization in functional regression methods, followed by an overview of functional regression methods, split into three types  ...  : [1] functional predictor regression (scalar-on-function), [2] functional response regression (function-on-scalar) and [3] function-on-function regression.  ...  to a weighted ridge regression in the PC space.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1406.4068v1">arXiv:1406.4068v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uvbzr45s75etpl7faj6y7wkz6i">fatcat:uvbzr45s75etpl7faj6y7wkz6i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200825112239/https://arxiv.org/pdf/1406.4068v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e0/55/e055e408dcdb4ded38d3e92d656e4e01933f1d03.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1406.4068v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

The generalized equivalence of regularization and min–max robustification in linear mixed models

Jan Pablo Burgard, Joscha Krause, Dennis Kreber, Domingo Morales
<span title="2021-01-11">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pp7wkecwgvedrot2jx6ynvspzy" style="color: black;">Statistical Papers</a> </i> &nbsp;
AbstractThe connection between regularization and min–max robustification in the presence of unobservable covariate measurement errors in linear mixed models is addressed.  ...  The theoretical findings are supported by a Monte Carlo simulation study under multiple measurement error scenarios.  ...  symmetric measurement errors d i j iid ∼ N (0 3 , [2] D ). • Scenario 3: strongly correlated symmetric measurement errors d i j iid ∼ N (0 3 , [3] D ). • Scenario 4: weakly correlated asymmetric measurement  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00362-020-01214-z">doi:10.1007/s00362-020-01214-z</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mzrllii2trcddgwvpmibgg7eli">fatcat:mzrllii2trcddgwvpmibgg7eli</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429105353/https://link.springer.com/content/pdf/10.1007/s00362-020-01214-z.pdf?error=cookies_not_supported&amp;code=2418f73d-1966-417e-a942-13d2ab4f9096" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/35/ae/35ae8e190649795666053519e7ef129c6100fe7e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00362-020-01214-z"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a>

Predicting the intensity of partially observed data from a revisited kriging for point processes [article]

Edith Gabriel, Florent Bonneu, Pascal Monestiez, Joel Chadoeuf
<span title="2016-04-20">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In order to predict the local intensity of the point process, λ (x|U), we propose to define the first- and second-order characteristics of a random field, defined as the regularized counting process, from  ...  the ones of the point process and to interpolate the local intensity by using a kriging adapted to the regularized process.  ...  So, once the pair correlation function is estimated from the point data subset, one can apply our method to interpolate the intensity.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1604.05868v1">arXiv:1604.05868v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/y3eysr5iyvbftjqp46x3t76fwe">fatcat:y3eysr5iyvbftjqp46x3t76fwe</a> </span>
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Two-Stage Sparse Regression Screening to Detect Biomarker-Treatment Interactions in Randomized Clinical Trials [article]

Jixiong Wang, Ashish Patel, James M.S. Wason, Paul J. Newcombe
<span title="2020-04-25">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We also propose a new stage 1 multivariate screening strategy using ridge regression to account for correlations among biomarkers.  ...  correlated data.  ...  It is known that ridge regression has a tendency to average effects across strongly correlated covariates.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.12028v1">arXiv:2004.12028v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kwenxe2x5vdhdjdqeslz3lkdpi">fatcat:kwenxe2x5vdhdjdqeslz3lkdpi</a> </span>
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