A Comparison of the Internal Validity of Alternative Parameter Estimation Methods in Decompositional Multiattribute Preference Models

1979 ◽  
Vol 16 (3) ◽  
pp. 313-322 ◽  
Author(s):  
Arun K. Jain ◽  
Franklin Acito ◽  
Naresh K. Malhotra ◽  
Vijay Mahajan
Keyword(s):  
Parameter Estimation ◽  
Data Collection ◽  
Data Base ◽  
Internal Validity ◽  
Detailed Comparison ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Alternative Approaches ◽  
Preference Models ◽  
Parameter Estimation Methods

Since 1971, interest in the use of decompositional multiattribute preference models in marketing has been increasing. The applications have varied in terms of the type of data used, behavior predicted, and methods used for estimating parameters. The authors examine the effect of different data collection and estimation procedures on parameter estimates and their stability and validity. An actual data base is used. A detailed comparison is made of the alternative approaches of parameter estimation and suggestions are given for the potential users of decompositional multiattribute preference models.

scholarly journals An application of the ensemble Kalman filter in epidemiological modelling

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256227
Author(s):  
Rajnesh Lal ◽  
Weidong Huang ◽  
Zhenquan Li
Keyword(s):  
Parameter Estimation ◽  
Model Simulation ◽  
Synthetic Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Model Parameter ◽  
Reliable Model ◽  
Reported Data ◽  
Parameter Estimation Methods

Since the novel coronavirus (COVID-19) outbreak in China, and due to the open accessibility of COVID-19 data, several researchers and modellers revisited the classical epidemiological models to evaluate their practical applicability. While mathematical compartmental models can predict various contagious viruses’ dynamics, their efficiency depends on the model parameters. Recently, several parameter estimation methods have been proposed for different models. In this study, we evaluated the Ensemble Kalman filter’s performance (EnKF) in the estimation of time-varying model parameters with synthetic data and the real COVID-19 data of Hubei province, China. Contrary to the previous works, in the current study, the effect of damping factors on an augmented EnKF is studied. An augmented EnKF algorithm is provided, and we present how the filter performs in estimating models using uncertain observational (reported) data. Results obtained confirm that the augumented-EnKF approach can provide reliable model parameter estimates. Additionally, there was a good fit of profiles between model simulation and the reported COVID-19 data confirming the possibility of using the augmented-EnKF approach for reliable model parameter estimation.

Identification of Biodegradation models under model and data uncertainty

1996 ◽  
Vol 33 (2) ◽  
pp. 91-105 ◽  
Author(s):  
Peter A. Vanrolleghem ◽  
Karel J. Keesman
Keyword(s):  
Parameter Estimation ◽  
Real World ◽  
Data Uncertainty ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Local Minima ◽  
Real World Data ◽  
Correlation Matrices ◽  
Parameter Estimation Methods ◽  
Parameter Values

In this paper a number of nonlinear parameter estimation methods are evaluated with respect to their ability to identify biodegradation models from “real-world” data. Important aspects are then the sensitivity to local minima, rate of convergence, required prior knowledge and direct or indirect availability of parameter estimates uncertainty. Furthermore, it is important whether a method is robust against invalid assumptions. In addition to the final parameter values, covariance and correlation matrices, confidence intervals and residual sequences are presented to obtain information about the validity of the models and noise assumptions. Finally, recommendations on the method's applicability range are provided.

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