Iteratively reweighted least squares and slime mold dynamics: connection and convergence

2021 ◽  
Author(s):  
Damian Straszak ◽  
Nisheeth K. Vishnoi
Keyword(s):  
Least Squares ◽  
Slime Mold ◽  
Iteratively Reweighted Least Squares

scholarly journals Analytical quality assessment of iteratively reweighted least-squares (IRLS) method

2014 ◽  
Vol 20 (1) ◽  
pp. 132-141 ◽  
Author(s):  
Jianfeng Guo
Keyword(s):  
Least Squares ◽  
Order Approximation ◽  
Diagnostic Tools ◽  
Iteratively Reweighted Least Squares ◽  
First Order ◽  
Iterative Reweighting ◽  
First Order Approximation ◽  
Detectable Bias ◽  
Theoretical Analyses ◽  
Exact Analytical Method

The iteratively reweighted least-squares (IRLS) technique has been widely employed in geodetic and geophysical literature. The reliability measures are important diagnostic tools for inferring the strength of the model validation. An exact analytical method is adopted to obtain insights on how much iterative reweighting can affect the quality indicators. Theoretical analyses and numerical results show that, when the downweighting procedure is performed, (1) the precision, all kinds of dilution of precision (DOP) metrics and the minimal detectable bias (MDB) will become larger; (2) the variations of the bias-to-noise ratio (BNR) are involved, and (3) all these results coincide with those obtained by the first-order approximation method.

Iteratively reweighted least squares method for camera pose estimation based on coplanar line correspondences

2016 ◽  
Vol 24 (5) ◽  
pp. 1168-1175
Author(s):  
张振杰 ZHANG Zhen-jie ◽  
郝向阳 HAO Xiang-yang ◽  
程传奇 CHENG Chuan-qi ◽  
黄忠义 HUANG Zhong-yi
Keyword(s):  
Least Squares ◽  
Pose Estimation ◽  
Least Squares Method ◽  
Iteratively Reweighted Least Squares ◽  
Coplanar Line ◽  
Camera Pose Estimation ◽  
Camera Pose

scholarly journals Fast Poisson estimation with high-dimensional fixed effects

2020 ◽  
Vol 20 (1) ◽  
pp. 95-115 ◽  
Author(s):  
Sergio Correia ◽  
Paulo Guimarães ◽  
Tom Zylkin
Keyword(s):  
Least Squares ◽  
Fixed Effects ◽  
Maximum Likelihood Estimates ◽  
High Dimensional ◽  
Software Components ◽  
Iteratively Reweighted Least Squares ◽  
Convergence Properties ◽  
New Techniques ◽  
Boston College ◽  
Pseudo Maximum Likelihood

In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). Estimation is implemented using a modified version of the iteratively reweighted least-squares algorithm that allows for fast estimation in the presence of HDFE. Because the code is built around the reghdfe package ( Correia, 2014 , Statistical Software Components S457874, Department of Economics, Boston College), it has similar syntax, supports many of the same functionalities, and benefits from reghdfe‘s fast convergence properties for computing high-dimensional leastsquares problems. Performance is further enhanced by some new techniques we introduce for accelerating HDFE iteratively reweighted least-squares estimation specifically. ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo)maximum likelihood estimates.

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