Geometry of Age– Period– Cohort (APC) Models and Constrained Estimation

2014 ◽  
pp. 72-105
Keyword(s):  
Constrained Estimation

Linearly constrained estimation by mathematical programming

1989 ◽  
Vol 42 (3) ◽  
pp. 254-267 ◽  
Author(s):  
Emil Klafszky ◽  
Jànos Mayer ◽  
Tamás Terlaky
Keyword(s):  
Mathematical Programming ◽  
Constrained Estimation ◽  
Linearly Constrained

scholarly journals Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments

2011 ◽  
Vol 15 (8) ◽  
pp. 2437-2457 ◽  
Author(s):  
S. Nie ◽  
J. Zhu ◽  
Y. Luo
Keyword(s):  
Parameter Estimation ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Estimation Method ◽  
State Parameter ◽  
Hydraulic Parameters ◽  
Constrained Estimation ◽  
Estimated Parameters

Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.

Constrained estimation and inference

2015 ◽  
pp. 321-342
Author(s):  
Daniel J. Henderson ◽  
Christopher F. Parmeter
Keyword(s):  
Constrained Estimation

On Rate-Constrained Estimation in Unreliable Sensor Networks

2003 ◽  
pp. 178-192 ◽  
Author(s):  
Prakash Ishwar ◽  
Rohit Puri ◽  
S. Sandeep Pradhan ◽  
Kannan Ramchandran
Keyword(s):  
Sensor Networks ◽  
Constrained Estimation

Improving Predictive Power of Conjoint Analysis by Constrained Parameter Estimation

1983 ◽  
Vol 20 (4) ◽  
pp. 433-438 ◽  
Author(s):  
V. Srinivasan ◽  
Arun K. Jain ◽  
Naresh K. Malhotra
Keyword(s):  
Parameter Estimation ◽  
Conjoint Analysis ◽  
Predictive Validity ◽  
Predictive Power ◽  
A Priori ◽  
A Priori Knowledge ◽  
Constrained Estimation ◽  
First Choice ◽  
Full Profile ◽  
Different Levels

The prediction of first choice preferences by the full-profile method of conjoint analysis can be improved significantly by imposing constraints on parameters based on a priori knowledge of the ordering of part worths for different levels of an attribute. Constrained estimation however, has little effect on the predictive validity of the tradeoff method because the preference judgments within rows (or columns) of tradeoff tables have largely the same role as the constraints.

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