Robust estimation via modified Cholesky decomposition for modal partially nonlinear models with longitudinal data

2021 ◽  
pp. 1-14
Author(s):  
Fei Lu
Keyword(s):  
Longitudinal Data ◽  
Robust Estimation ◽  
Nonlinear Models ◽  
Cholesky Decomposition ◽  
Modified Cholesky Decomposition

scholarly journals Nonlinear Models for Longitudinal Data

2009 ◽  
Vol 63 (4) ◽  
pp. 378-388 ◽  
Author(s):  
Jan Serroyen ◽  
Geert Molenberghs ◽  
Geert Verbeke ◽  
Marie Davidian
Keyword(s):  
Longitudinal Data ◽  
Nonlinear Models

Asymmetric Density Fitting with Modified Cholesky Decomposition Applied to Second-Order Electron Propagator

2020 ◽  
Vol 16 (3) ◽  
pp. 1597-1605
Author(s):  
Juan Felipe Huan Lew-Yee ◽  
Roberto Flores-Moreno ◽  
José Luis Morales ◽  
Jorge M. del Campo
Keyword(s):  
Cholesky Decomposition ◽  
Second Order ◽  
Electron Propagator ◽  
Modified Cholesky Decomposition ◽  
Density Fitting

Analysis of longitudinal data of beef cattle raised on pasture from northern Brazil using nonlinear models

2012 ◽  
Vol 44 (8) ◽  
pp. 1945-1951 ◽  
Author(s):  
Fernando B. Lopes ◽  
Marcelo C. da Silva ◽  
Ednira G. Marques ◽  
Concepta M. McManus
Keyword(s):  
Longitudinal Data ◽  
Beef Cattle ◽  
Nonlinear Models ◽  
Northern Brazil

scholarly journals A Maximum Likelihood Ensemble Filter via a Modified Cholesky Decomposition for Non-Gaussian Data Assimilation

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 877 ◽  
Author(s):  
Elias David Nino-Ruiz ◽  
Alfonso Mancilla-Herrera ◽  
Santiago Lopez-Restrepo ◽  
Olga Quintero-Montoya
Keyword(s):  
Maximum Likelihood ◽  
General Circulation ◽  
Circulation Model ◽  
Cholesky Decomposition ◽  
Practical Implementation ◽  
Square Root ◽  
Background Error ◽  
Modified Cholesky Decomposition ◽  
Highly Nonlinear ◽  
Maximum Likelihood Ensemble Filter

This paper proposes an efficient and practical implementation of the Maximum Likelihood Ensemble Filter via a Modified Cholesky decomposition (MLEF-MC). The method works as follows: via an ensemble of model realizations, a well-conditioned and full-rank square-root approximation of the background error covariance matrix is obtained. This square-root approximation serves as a control space onto which analysis increments can be computed. These are calculated via Line-Search (LS) optimization. We theoretically prove the convergence of the MLEF-MC. Experimental simulations were performed using an Atmospheric General Circulation Model (AT-GCM) and a highly nonlinear observation operator. The results reveal that the proposed method can obtain posterior error estimates within reasonable accuracies in terms of ℓ − 2 error norms. Furthermore, our analysis estimates are similar to those of the MLEF with large ensemble sizes and full observational networks.

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