scholarly journals Measurement error correction in the least absolute shrinkage and selection operator model when validation data are available

2017 ◽  
Vol 28 (3) ◽  
pp. 670-680 ◽  
Author(s):  
Monica M Vasquez ◽  
Chengcheng Hu ◽  
Denise J Roe ◽  
Marilyn Halonen ◽  
Stefano Guerra

Measurement of serum biomarkers by multiplex assays may be more variable as compared to single biomarker assays. Measurement error in these data may bias parameter estimates in regression analysis, which could mask true associations of serum biomarkers with an outcome. The Least Absolute Shrinkage and Selection Operator (LASSO) can be used for variable selection in these high-dimensional data. Furthermore, when the distribution of measurement error is assumed to be known or estimated with replication data, a simple measurement error correction method can be applied to the LASSO method. However, in practice the distribution of the measurement error is unknown and is expensive to estimate through replication both in monetary cost and need for greater amount of sample which is often limited in quantity. We adapt an existing bias correction approach by estimating the measurement error using validation data in which a subset of serum biomarkers are re-measured on a random subset of the study sample. We evaluate this method using simulated data and data from the Tucson Epidemiological Study of Airway Obstructive Disease (TESAOD). We show that the bias in parameter estimation is reduced and variable selection is improved.

2000 ◽  
Vol 9 (5) ◽  
pp. 447-474 ◽  
Author(s):  
Dorothee Thürigen ◽  
Donna Spiegelman ◽  
Maria Blettner ◽  
Carsten Heuer ◽  
Hermann Brenner

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 39733-39745
Author(s):  
Shumpei Shimokawa ◽  
Yuzo Taenaka ◽  
Kazuya Tsukamoto ◽  
Myung Lee

Author(s):  
Y. Kang ◽  
C. Y. Zhao ◽  
Q. Zhang ◽  
C. S. Yang

Unwrapping error is a common error in the InSAR processing, which will seriously degrade the accuracy of the monitoring results. Based on a gross error correction method, Quasi-accurate detection (QUAD), the method for unwrapping errors automatic correction is established in this paper. This method identifies and corrects the unwrapping errors by establishing a functional model between the true errors and interferograms. The basic principle and processing steps are presented. Then this method is compared with the L1-norm method with simulated data. Results show that both methods can effectively suppress the unwrapping error when the ratio of the unwrapping errors is low, and the two methods can complement each other when the ratio of the unwrapping errors is relatively high. At last the real SAR data is tested for the phase unwrapping error correction. Results show that this new method can correct the phase unwrapping errors successfully in the practical application.


Biometrics ◽  
2019 ◽  
Vol 75 (3) ◽  
pp. 927-937 ◽  
Author(s):  
Juned Siddique ◽  
Michael J. Daniels ◽  
Raymond J. Carroll ◽  
Trivellore E. Raghunathan ◽  
Elizabeth A. Stuart ◽  
...  

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