Hybrid Least Squares for Collaborative Localization: Comparative Analysis and Integrated Outlier Rejection

2020 ◽  
Author(s):  
Ali Khalajmehrabadi ◽  
Nikolaos Gatsis ◽  
Daniel Pack ◽  
David Akopian
Keyword(s):  
Comparative Analysis ◽  
Least Squares ◽  
Outlier Rejection ◽  
Collaborative Localization

scholarly journals Pricing American Options Using a Nonparametric Entropy Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Xisheng Yu ◽  
Li Yang
Keyword(s):  
Monte Carlo ◽  
Comparative Analysis ◽  
Least Squares ◽  
American Options ◽  
Monte Carlo Algorithm ◽  
Underlying Asset ◽  
Asset Return ◽  
Pricing Errors ◽  
Risk Neutral ◽  
Pricing Measure

This paper studies the pricing problem of American options using a nonparametric entropy approach. First, we derive a general expression for recovering the risk-neutral moments of underlying asset return and then incorporate them into the maximum entropy framework as constraints. Second, by solving this constrained entropy problem, we obtain a discrete risk-neutral (martingale) distribution as the unique pricing measure. Third, the optimal exercise strategies are achieved via the least-squares Monte Carlo algorithm and consequently the pricing algorithm of American options is obtained. Finally, we conduct the comparative analysis based on simulations and IBM option contracts. The results demonstrate that this nonparametric entropy approach yields reasonably accurate prices for American options and produces smaller pricing errors compared to other competing methods.

ROBUST NEURAL APPROACH FOR THE ESTIMATION OF THE ESSENTIAL PARAMETERS IN COMPUTER VISION

1999 ◽  
Vol 08 (03) ◽  
pp. 255-274 ◽  
Author(s):  
GIANSALVO CIRRINCIONE ◽  
MAURIZIO CIRRINCIONE
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Image Segmentation ◽  
Least Squares ◽  
Total Least Squares ◽  
Outlier Rejection ◽  
Least Squares Problem ◽  
Case Deletion ◽  
Structured Total Least Squares ◽  
Deletion Diagnostics

The essential parameters approach is a well-known technique in computer vision for recovering the motion and scene parameters from a sequence of images. This approach has long been considered as suboptimal because of the underestimation of the effect of noise and outliers. This paper re-evaluates this method because it is correctly classified as a structured Total Least Squares problem and proposes very robust linear neurons for its solution. Then, a novel neural network, CASEDEL EXIN, which exploits these neurons together with the case deletion diagnostics, is introduced. It is not only very robust (outlier rejection), but is also able to identify the outliers. This fact can be exploited, for example, to refine the image segmentation.

scholarly journals Forensic Speaker Verification: Case Report at Brazilian Justice

2021 ◽  
Author(s):  
Thyago J. Machado ◽  
Jozue Vieira Filho ◽  
Mario A. de Oliveira
Keyword(s):  
Case Report ◽  
Comparative Analysis ◽  
Least Squares ◽  
Speaker Verification ◽  
Ordinary Least Squares ◽  
Penal Code ◽  
Mato Grosso

This case report investigates 5 real cases which followed legal channels and were judged by Mato Grosso Court in Brazil. Audio systems served as elements of key evidence on those lawsuits. The goal here is to analyze the cases by using a methodology based on the forensic speaker verification by using the Ordinary Least Squares (OLS) algorithm and to compare results with analyses obtained on real cases. The comparative analysis is assessed for time elapsed for obtaining results, as well as results quality. In Brazil, the lawsuit duration is very important, since the Penal Code foresees prescription after a given time, and it may lead to impunity. Results show that the analysis, by using OLS, generates immediate, effective results when compared to those obtained with traditional methodologies on the studied Brazilian lawsuits.

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