Characterization of Consumers’ Behavior in Medical Insurance Market with Agent Parameters’ Estimation Process Using Bayesian Network

2018 ◽  
pp. 112-128
Author(s):  
Ren Suzuki ◽  
Yoko Ishino ◽  
Shingo Takahashi
Keyword(s):  
Bayesian Network ◽  
Medical Insurance ◽  
Parameters Estimation ◽  
Insurance Market ◽  
Estimation Process

Characterization of a PMU-based method for transmission line parameters estimation with systematic measurement error modeling

2021 ◽  
Author(s):  
Carlo Muscas ◽  
Paolo Attilio Pegoraro ◽  
Carlo Sitzia ◽  
Antonio Vincenzo Solinas ◽  
Sara Sulis ◽  
...  
Keyword(s):  
Measurement Error ◽  
Transmission Line ◽  
Error Modeling ◽  
Parameters Estimation ◽  
Systematic Measurement ◽  
Systematic Measurement Error

Characterization of semiconductor laser frequency chirp based on signal distortion in dispersive optical fiber

2006 ◽  
Vol 14 (2) ◽  
Author(s):  
P. Krehlik
Keyword(s):  
Single Mode ◽  
Laser Frequency ◽  
Single Mode Fiber ◽  
Distributed Feedback ◽  
Parameters Estimation ◽  
Signal Distortion ◽  
Measuring Time ◽  
Simple Method ◽  
Laser Chirp

AbstractIn the paper, the simple method of laser chirp parameters estimation is presented. It is based on measuring time-domain distortions of chirped signal transmitted through dispersive fiber and finding laser chirp parameters by matching measured distortions to calculated ones. Experiments undertaken with 1.55 μm telecommunication grade distributed feedback (DFB) lasers and standard single-mode fiber are described, together with some practical remarks on measurement setup and main conclusions.

A Linear Approach for Parameters Estimation of Multivariable Models in a Parameter Matrix Form

2014 ◽  
Vol 11 (3) ◽  
pp. 36-44
Author(s):  
A. Efremov
Keyword(s):  
Parameters Estimation ◽  
Matrix Form ◽  
Model Parameters ◽  
Point Selection ◽  
Estimation Process ◽  
Linear Approach ◽  
Parameter Matrix ◽  
Starting Point ◽  
Non Linear ◽  
Parameterized Model

Abstract When a model output is a linear function of the model parameters, the estimation process is significantly simplified, since the optimal estimates can be determined without the usage of a numerical optimization method. Moreover, some types of nonlinear models w.r.t. their parameters can be interpreted as linear (obviously introducing a discrepancy). This is the main premise behind the linear approach for parameter estimation, where the Least Squares (LS) method is used for parameters estimation. As this assumption contradicts with the non-linear parameterized model structure, the estimation process becomes iterative. In spite of this, the linear approach is frequently preferable due to the reduced number of computations, compared with the non-linear approach, where the model is correctly considered as non-linear. Moreover, some issues with the starting point selection, stuck at a local minima, etc., natural for the non-linear approach, are avoided. In this paper estimators are presented, based on the linear approach, for both MIMO linear and non-linear parameterized models in a parameter matrix form. The representatives of the first group are LS and Weighted LS (WLS). For non-linear models, this approach is presented in terms of Extended LS (ELS). The topic regarding the efficient realizations of the estimators is also discussed

scholarly journals Issues of the Private Medical Insurance Market

2010 ◽  
Vol 2010 (611) ◽  
pp. 611_141-611_156
Author(s):  
Tomoka Miyachi
Keyword(s):  
Medical Insurance ◽  
Insurance Market
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