scholarly journals Finding Relevant Variables in Sparse Bayesian Factor Models: Economic Applications and Simulation Results

2012 ◽  
Author(s):  
Sylvia Kaufmann ◽  
Christian Schumacher
Keyword(s):  
Factor Models ◽  
Economic Applications ◽  
Relevant Variables ◽  
Bayesian Factor ◽  
Simulation Results

scholarly journals Quantifying the strength of general factors in psychopathology: A comparison of CFA with maximum likelihood estimation, BSEM and ESEM/EFA bi-factor approaches

2018 ◽  
Author(s):  
Aja Louise Murray ◽  
Tom Booth ◽  
Manuel Eisner ◽  
Ingrid Obsuth ◽  
Denis Ribeaud
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Factor Models ◽  
General Factor ◽  
Normative Sample ◽  
Ml Estimation ◽  
Co Morbidity ◽  
Simulation Results ◽  
P Factor

Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or p-factor) in classifying, researching, diagnosing and treating psychiatric disorders depends (amongst other issues) on the extent to which co-morbidity is symptom-general rather than staying largely within the confines of narrower trans-diagnostic factors such as internalising and externalising. In this study we compared three methods of estimating p-factor strength. We compared omega hierarchical and ECV calculated from CFA bi-factor models with maximum likelihood (ML) estimation, from ESEM/EFA models with a bifactor rotation, and from BSEM bi-factor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings may be the preferred option. However, CFA with ML also performed well provided secondary loadings were modelled We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, n=1286) and University counselling sample (n= 359).

scholarly journals Bayesian factor models in characterizing molecular adaptation

2013 ◽  
Vol 40 (7) ◽  
pp. 1402-1424
Author(s):  
Saheli Datta ◽  
Raquel Prado ◽  
Abel Rodríguez
Keyword(s):  
Factor Models ◽  
Molecular Adaptation ◽  
Bayesian Factor

scholarly journals Hybrid synchronisation of vallis chaotic systems using nonlinear active control

2018 ◽  
Vol 7 (2.21) ◽  
pp. 50 ◽  
Author(s):  
Piyush Pratap Singh ◽  
Vikash Kumar ◽  
Eshan Tiwari ◽  
Vinay K. Chauhan
Keyword(s):  
Numerical Simulation ◽  
Active Control ◽  
Lyapunov Stability ◽  
Chaotic Systems ◽  
Southern Oscillation ◽  
Lyapunov Stability Theory ◽  
Control Technique ◽  
Control Laws ◽  
Relevant Variables ◽  
Simulation Results

In this paper, hybrid synchronisation of Vallis chaotic systems using a nonlinear control technique is proposed. Vallis system represents the principal quantitative features of the El-Nino Southern Oscillation (ENSO) phenomenon. A nonlinear active control technique is used for hybrid synchronisation. Control laws are designed by using the sum of the relevant variables of the both mater and slave systems. Required Lyapunov stability condition is devised using Lyapunov stability theory. Numerical simulation results reflect the successful achievement of the proposed objectives. MATLAB is used for simulation.  

scholarly journals Bayesian factor models for probabilistic cause of death assessment with verbal autopsies

2020 ◽  
Vol 14 (1) ◽  
pp. 241-256
Author(s):  
Tsuyoshi Kunihama ◽  
Zehang Richard Li ◽  
Samuel J. Clark ◽  
Tyler H. McCormick
Keyword(s):  
Cause Of Death ◽  
Factor Models ◽  
Bayesian Factor

scholarly journals A Bayesian Approach for Large Asset Allocation

2020 ◽  
Vol 10 (1) ◽  
pp. 58
Author(s):  
Mihnea S. Andrei ◽  
John S. J. Hsu
Keyword(s):  
Sensitivity Analysis ◽  
Bayesian Approach ◽  
Asset Allocation ◽  
Historical Data ◽  
Factor Models ◽  
Memory Allocation ◽  
Multivariate Normal ◽  
Confidence Levels ◽  
Conduct Sensitivity Analysis ◽  
Bayesian Factor

The Black-Litterman model combines investor’s personal views with historical data and gives optimal portfolio weights. In (Andrei & Hsu, 2020), they reviewed the original Black-Litterman model and modified it in order to fit it into a Bayesian framework, when a certain number of assets is considered. They used the idea by (Leonard & Hsu, 1992) for a multivariate normal prior on the logarithm of the covariance matrix. When implemented and applied to a large number of assets such as all the S&P500 companies, they ran into memory allocation and running time issues. In this paper, we reduce the dimensions by considering Bayesian factor models, which solve the asset allocation problems for a large number of assets. In addition, we will conduct sensitivity analysis for the confidence levels that the investors have to input.

scholarly journals Posterior contraction in sparse Bayesian factor models for massive covariance matrices

2014 ◽  
Vol 42 (3) ◽  
pp. 1102-1130 ◽  
Author(s):  
Debdeep Pati ◽  
Anirban Bhattacharya ◽  
Natesh S. Pillai ◽  
David Dunson
Keyword(s):  
Factor Models ◽  
Covariance Matrices ◽  
Bayesian Factor
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