Two-Stage Multiscale Adaptive Regression Methods for Twin Neuroimaging Data

2011 ◽  
pp. 102-109
Author(s):  
Yimei Li ◽  
John H. Gilmore ◽  
Jiaping Wang ◽  
Martin Styner ◽  
Weili Lin ◽  
...  
Keyword(s):  
Two Stage ◽  
Regression Methods ◽  
Neuroimaging Data ◽  
Adaptive Regression

scholarly journals TwinMARM: Two-Stage Multiscale Adaptive Regression Methods for Twin Neuroimaging Data

2012 ◽  
Vol 31 (5) ◽  
pp. 1100-1112 ◽  
Author(s):  
Yimei Li ◽  
John H. Gilmore ◽  
Jiaping Wang ◽  
Martin Styner ◽  
Weili Lin ◽  
...  
Keyword(s):  
Two Stage ◽  
Regression Methods ◽  
Neuroimaging Data ◽  
Adaptive Regression

scholarly journals A regression model for pooled data in a two-stage survey under informative sampling with application for detecting and estimating the presence of transgenic corn

2016 ◽  
Vol 26 (2) ◽  
pp. 182-197
Author(s):  
Osval A. Montesinos-López ◽  
Kent Eskridge ◽  
Abelardo Montesinos-López ◽  
José Crossa ◽  
Moises Cortés-Cruz ◽  
...  
Keyword(s):  
Likelihood Function ◽  
Group Testing ◽  
Transgenic Corn ◽  
Sampling Weights ◽  
Two Stage ◽  
Binary Responses ◽  
Regression Methods ◽  
Proposed Model ◽  
Informative Sampling ◽  
Selection Probabilities

AbstractGroup-testing regression methods are effective for estimating and classifying binary responses and can substantially reduce the number of required diagnostic tests. However, there is no appropriate methodology when the sampling process is complex and informative. In these cases, researchers often ignore stratification and weights that can severely bias the estimates of the population parameters. In this paper, we develop group-testing regression models for analysing two-stage surveys with unequal selection probabilities and informative sampling. Weights are incorporated into the likelihood function using the pseudo-likelihood approach. A simulation study demonstrates that the proposed model reduces the bias in estimation considerably compared to other methods that ignore the weights. Finally, we apply the model for estimating the presence of transgenic corn in Mexico and we give the SAS code used for the analysis.

scholarly journals Multiscale adaptive regression models for neuroimaging data

2011 ◽  
Vol 73 (4) ◽  
pp. 559-578 ◽  
Author(s):  
Yimei Li ◽  
Hongtu Zhu ◽  
Dinggang Shen ◽  
Weili Lin ◽  
John H. Gilmore ◽  
...  
Keyword(s):  
Regression Models ◽  
Neuroimaging Data ◽  
Adaptive Regression

Dynamic Parameters of the Geocenter Produced by GPS and DORIS Navigation Systems by Means of Adaptive Regression Methods

2021 ◽  
Author(s):  
Yury Nefedyev ◽  
Regina Mubarakshina ◽  
Alexey Andreev ◽  
Natalya Demina ◽  
Zoya Andreeva ◽  
...  
Keyword(s):  
Navigation Systems ◽  
Dynamic Parameters ◽  
Regression Methods ◽  
Adaptive Regression
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