Consistent estimator in multivariate errors-in-variables model in the case of unknown error covariance structure

2007 ◽  
Vol 59 (8) ◽  
pp. 1137-1147 ◽  
Author(s):  
O. H. Kukush ◽  
M. Ya. Polekha
Keyword(s):  
Covariance Structure ◽  
Consistent Estimator ◽  
Errors In Variables ◽  
Error Covariance ◽  
Error Covariance Structure

scholarly journals Effects of error covariance structure on estimation of model averaging weights and predictive performance

2013 ◽  
Vol 49 (9) ◽  
pp. 6029-6047 ◽  
Author(s):  
Dan Lu ◽  
Ming Ye ◽  
Philip D. Meyer ◽  
Gary P. Curtis ◽  
Xiaoqing Shi ◽  
...  
Keyword(s):  
Model Averaging ◽  
Covariance Structure ◽  
Predictive Performance ◽  
Error Covariance ◽  
Error Covariance Structure

A systematic approach for identifying level-1 error covariance structures in latent growth modeling

2016 ◽  
Vol 41 (3) ◽  
pp. 444-455 ◽  
Author(s):  
Cherng G. Ding ◽  
Ten-Der Jane ◽  
Chiu-Hui Wu ◽  
Hang-Rung Lin ◽  
Chih-Kang Shen
Keyword(s):  
Systematic Approach ◽  
Growth Modeling ◽  
Covariance Structure ◽  
Latent Growth Modeling ◽  
Covariance Structures ◽  
Latent Growth ◽  
Error Covariance ◽  
Nested Relations ◽  
Level 1 ◽  
Error Covariance Structure

It has been pointed out in the literature that misspecification of the level-1 error covariance structure in latent growth modeling (LGM) has detrimental impacts on the inferences about growth parameters. Since correct covariance structure is difficult to specify by theory, the identification needs to rely on a specification search, which, however, is not systematically addressed in the literature. In this study, we first discuss characteristics of various covariance structures and their nested relations, based on which we then propose a systematic approach to facilitate identifying a plausible covariance structure. A test for stationarity of an error process and the sequential chi-square difference test are conducted in the approach. Preliminary simulation results indicate that the approach performs well when sample size is large enough. The approach is illustrated with empirical data. We recommend that the approach be used in LGM empirical studies to improve the quality of the specification of the error covariance structure.

scholarly journals Robustness of Stein-type estimators under a non-scalar error covariance structure

2009 ◽  
Vol 100 (10) ◽  
pp. 2376-2388 ◽  
Author(s):  
Xinyu Zhang ◽  
Ti Chen ◽  
Alan T.K. Wan ◽  
Guohua Zou
Keyword(s):  
Covariance Structure ◽  
Error Covariance ◽  
Error Covariance Structure

scholarly journals Controlling Noise in Ensemble Data Assimilation Schemes

2010 ◽  
Vol 138 (5) ◽  
pp. 1502-1512 ◽  
Author(s):  
Malaquias Peña ◽  
Zoltan Toth ◽  
Mozheng Wei
Keyword(s):  
Data Assimilation ◽  
Ad Hoc ◽  
Forecast Accuracy ◽  
Covariance Structure ◽  
Sampling Errors ◽  
Background Error ◽  
Forecast Performance ◽  
Error Covariance ◽  
Additional Noise ◽  
Filter Divergence

Abstract A variety of ad hoc procedures have been developed to prevent filter divergence in ensemble-based data assimilation schemes. These procedures are necessary to reduce the impacts of sampling errors in the background error covariance matrix derived from a limited-size ensemble. The procedures amount to the introduction of additional noise into the assimilation process, possibly reducing the accuracy of the resulting analyses. The effects of this noise on analysis and forecast performance are investigated in a perfect model scenario. Alternative schemes aimed at controlling the unintended injection of noise are proposed and compared. Improved analysis and forecast accuracy is observed in schemes with minimal alteration to the evolving ensemble-based covariance structure.

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