The influence of autoregressive relation strength and search strategy on directionality recovery in group iterative multiple model estimation.

2021 ◽  
Author(s):  
Alexander Weigard ◽  
Stephanie Lane ◽  
Kathleen Gates ◽  
Adriene Beltz
Keyword(s):  
Search Strategy ◽  
Model Estimation ◽  
Multiple Model

scholarly journals The Influence of Autoregressive Relation Strength and Search Strategy on Directionality Recovery in GIMME

2020 ◽  
Author(s):  
Alexander Samuel Weigard ◽  
Stephanie Lane ◽  
Kathleen Gates ◽  
Adriene M. Beltz
Keyword(s):  
Structural Equation ◽  
Search Strategy ◽  
Functional Neuroimaging ◽  
Indirect Evidence ◽  
Null Model ◽  
Resting State Fmri ◽  
Equation Modeling ◽  
Model Estimation ◽  
Multiple Model ◽  
Research Questions

Unified structural equation modeling (uSEM) implemented in the Group Iterative Multiple Model Estimation (GIMME) framework has recently been widely used for characterizing within-person network dynamics of behavioral and functional neuroimaging variables. Previous studies have established that GIMME accurately recovers the presence of relations between variables. However, recovery of relation directionality is less consistent, which is concerning given the importance of directionality estimates for many research questions. There is evidence that strong autoregressive relations may aid directionality recovery and indirect evidence that a novel version of GIMME allowing for multiple solutions could improve recovery when such relations are weak, but it remains unclear how these strategies perform under a range of study conditions. Using comprehensive simulations that varied the strength of autoregressive relations among other factors, this study evaluated the directionality recovery of two GIMME search strategies: 1) estimating autoregressive relations by default in the null model (GIMME-AR), and 2) generating multiple solution paths (GIMME-MS). Both strategies recovered directionality best – and were roughly equivalent in performance – when autoregressive relations were strong (e.g., β = .60). When they were weak (β <= .10), GIMME-MS displayed an advantage, although overall directionality recovery was modest. Analyses of empirical data in which autoregressive relations were characteristically strong (resting state fMRI) versus weak (daily diary) mirrored simulation results and confirmed that these strategies typically disagree on directionality when autoregressive relations are weak. Findings have important implications for psychological and neuroimaging applications of uSEM/GIMME and suggest specific contexts which allow researchers to place confidence in directionality results.

Estimate the Performance of Multi-Model Estimation Algorithms

2013 ◽  
Vol 427-429 ◽  
pp. 1506-1509
Author(s):  
Yong Yan Yu
Keyword(s):  
Estimation Procedure ◽  
Model Parameters ◽  
Data Sets ◽  
Model Estimation ◽  
Multiple Model ◽  
Estimation Algorithms ◽  
Performance Metric ◽  
Testing Dataset ◽  
Correctness Criterion ◽  
Line Fitting

A robust estimation procedure is necessary to estimate the true model parameters in computer vision. Evaluating the multiple-model in the presence of outliers-robust is a fundamentally different task than the single-model problem.Despite there are many diversity multi-model estimation algorithms, it is difficult to pick an effective and advisably approach.So we present a novel quantitative evaluation of multi-model estimation algorithms, efficiency may be evaluated by either examining the asymptotic efficiency of the algorithms or by running them for a series of data sets of increasing size.Thus we create a specifical testing dataset,and introduce a performance metric, Strongest-Intersection.and using the model-aware correctness criterion. Finally, well show the validity of estimation strategy by the Experimention of line-fitting.

scholarly journals Minimum error entropy based multiple model estimation for multisensor hybrid uncertain target tracking systems

2020 ◽  
Vol 14 (4) ◽  
pp. 199-213
Author(s):  
Shuhui Li ◽  
Xiaoxue Feng ◽  
Zhihong Deng ◽  
Feng Pan ◽  
Shengyang Ge
Keyword(s):  
Target Tracking ◽  
Model Estimation ◽  
Multiple Model ◽  
Tracking Systems ◽  
Minimum Error

scholarly journals Interacting Multiple Model Estimation for Helicopter Regime Recognition

2020 ◽  
Vol 57 (6) ◽  
pp. 1134-1147
Author(s):  
Dakota Musso ◽  
Jonathan Rogers
Keyword(s):  
Interacting Multiple Model ◽  
Model Estimation ◽  
Multiple Model
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