Semiparametric efficient estimators in heteroscedastic error models

2017 ◽  
Vol 71 (1) ◽  
pp. 1-28
Author(s):  
Mijeong Kim ◽  
Yanyuan Ma
Keyword(s):  
Error Models ◽  
Heteroscedastic Error

Identification in Dynamic Shock-Error Models

1979 ◽  
Author(s):  
Agustin Maravall ◽  
Klaus Neumann ◽  
Ulrich Steinhardt
Keyword(s):  
Error Models

Study on Optimization of the Number of DF Stations

2014 ◽  
Vol 709 ◽  
pp. 485-490
Author(s):  
Xiang Wu ◽  
Jun Jun Zong ◽  
Xun Xue Cui ◽  
Chuan Xu Liu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Direction Finding ◽  
Location Error ◽  
Location Accuracy ◽  
Error Models ◽  
Reasonable Number ◽  
Simulation Results

Reasonable number of direction finding station is examined in multi-station bearing-crossing location. Though it is believed that increasing the number of station is helpful to improve the location accuracy, In the paper, the maximum likelihood estimation (MLE) as an example. The algorithms and the location error models are given. The simulation results show that the location accuracy will be improved quickly with the increase of the number of the measuring participants, but the improvement will be sharply slowed down if too many station involved, which also boost the complexity of location.

Optimization of Signal Processing Applications Using Parameterized Error Models for Approximate Adders

2021 ◽  
Vol 20 (2) ◽  
pp. 1-25
Author(s):  
Celia Dharmaraj ◽  
Vinita Vasudevan ◽  
Nitin Chandrachoodan
Keyword(s):  
Signal Processing ◽  
Circuit Design ◽  
Computational Efficiency ◽  
Noise Power ◽  
Mean Square ◽  
Error Statistics ◽  
Error Models ◽  
Optimization Framework ◽  
Accuracy Constraint ◽  
Multi Level

Approximate circuit design has gained significance in recent years targeting error-tolerant applications. In the literature, there have been several attempts at optimizing the number of approximate bits of each approximate adder in a system for a given accuracy constraint. For computational efficiency, the error models used in these routines are simple expressions obtained using regression or by assuming inputs or the error is uniformly distributed. In this article, we first demonstrate that for many approximate adders, these assumptions lead to an inaccurate prediction of error statistics for multi-level circuits. We show that mean error and mean square error can be computed accurately if static probabilities of adders at all stages are taken into account. Therefore, in a system with a certain type of approximate adder, any optimization framework needs to take into account not just the functionality of the adder but also its position in the circuit, functionality of its parents, and the number of approximate bits in the parent blocks. We propose a method to derive parameterized error models for various types of approximate adders. We incorporate these models within an optimization framework and demonstrate that the noise power is computed accurately.

Three new residual error models for population PK/PD analyses

1995 ◽  
Vol 23 (6) ◽  
pp. 651-672 ◽  
Author(s):  
Mats O. Karlsson ◽  
Stuart L. Beal ◽  
Lewis B. Sheiner
Keyword(s):  
Residual Error ◽  
Error Models ◽  
Population Pk

Improvement of photogrammetric JRC data distributions based on parabolic error models

2015 ◽  
Vol 80 ◽  
pp. 19-30 ◽  
Author(s):  
Dong Hyun Kim ◽  
George V. Poropat ◽  
Ivan Gratchev ◽  
Arumugam Balasubramaniam
Keyword(s):  
Error Models
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