Industry and Time Specific Deviations from Fundamental Values in a Random Coefficient Model

2004 ◽  
Author(s):  
Leonardo Becchetti ◽  
Roberto Rocci ◽  
Giovanni Trovato
Keyword(s):  
Random Coefficient ◽  
Random Coefficient Model ◽  
Time Specific ◽  
Fundamental Values

Industry and time specific deviations from fundamental values in a random coefficient model

2006 ◽  
Vol 3 (2) ◽  
pp. 257-276 ◽  
Author(s):  
Leonardo Becchetti ◽  
Roberto Rocci ◽  
Giovanni Trovato
Keyword(s):  
Random Coefficient ◽  
Random Coefficient Model ◽  
Time Specific ◽  
Fundamental Values

Comparing MINQUE and IAUE estimates of variance components of a random coefficient model

2017 ◽  
Vol 11 ◽  
pp. 2727-2766
Author(s):  
Souha K. Badr ◽  
Ahmed H. Youssef ◽  
Hanaa H. Abu-zinadah
Keyword(s):  
Variance Components ◽  
Random Coefficient ◽  
Random Coefficient Model

scholarly journals Detecting and Forecasting Large Deviations and Bubbles in a Near-Explosive Random Coefficient Model

2013 ◽  
Author(s):  
Anurag Narayan Banerjee ◽  
Guillaume Chevillon ◽  
Marie Kratz
Keyword(s):  
Large Deviations ◽  
Random Coefficient ◽  
Random Coefficient Model

scholarly journals Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Chenfei Shao ◽  
Chongshi Gu ◽  
Zhenzhu Meng ◽  
Yating Hu
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Elastic Modulus ◽  
Random Coefficient ◽  
Arch Dam ◽  
Data Driven ◽  
The Finite Element Method ◽  
Random Coefficient Model ◽  
Proposed Model ◽  
Element Method

Both numerical simulations and data-driven methods have been applied in dam’s displacement modeling. For monitored displacement data-driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and temporal correlations among all monitoring points into account, we took the first step toward integrating the finite element method into a data-driven model. As the data-driven method, we selected the random coefficient model, which can make each explanatory variable coefficient of all monitoring points following one or several normal distributions. In this way, explanatory variables are constrained. Another contribution of the proposed model is that the actual elastic modulus at each monitoring point can be back-calculated. Moreover, with a Lagrange polynomial interpolation, we can obtain the distribution field of elastic modulus, rather than gaining one value for the whole dam in previous studies. The proposed model was validated by a case study of the concrete arch dam in Jinping-I hydropower station. It has a better prediction precision than the random coefficient model without the finite element method.

scholarly journals Tests of Hypotheses Arising in the Correlated Random Coefficient Model

2010 ◽  
Author(s):  
James Heckman ◽  
Daniel Schmierer
Keyword(s):  
Random Coefficient ◽  
Random Coefficient Model ◽  
Tests Of Hypotheses

The efficiency of estimating a random coefficient model

1980 ◽  
Vol 12 (3) ◽  
pp. 285-299 ◽  
Author(s):  
Baldev Raj ◽  
Virender Kumar Srivastava ◽  
Sushama Upadhyaya
Keyword(s):  
Random Coefficient ◽  
Random Coefficient Model

Identifying driver heterogeneity in car-following based on a random coefficient model

2013 ◽  
Vol 36 ◽  
pp. 35-44 ◽  
Author(s):  
Ikki Kim ◽  
Taewan Kim ◽  
Keemin Sohn
Keyword(s):  
Random Coefficient ◽  
Random Coefficient Model ◽  
Car Following

scholarly journals Tests of Hypotheses Arising in the Correlated Random Coefficient Model

2010 ◽  
Author(s):  
James J. Heckman ◽  
Daniel Schmierer
Keyword(s):  
Random Coefficient ◽  
Random Coefficient Model ◽  
Tests Of Hypotheses
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