scholarly journals A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5407
Author(s):  
Wenbo Wang ◽  
Ying Xu
Keyword(s):  
Least Squares ◽  
Robust Estimation ◽  
Subset Selection ◽  
Selection Method ◽  
Practical Application ◽  
Robust Performance ◽  
M Estimator ◽  
Ls Estimator ◽  
Receiver Autonomous Integrity Monitoring ◽  
Mm Estimation

The residual-based (RB) receiver autonomous integrity monitoring (RAIM) detector is a widely used receiver integrity enhancement technology that has the ability to rapidly respond to outliers. However, the sensitivity and vulnerability of the residuals to the outliers are the weaknesses of the method especially in the case of multi-outlier modes. It is an effective method for enhancing the validity of residuals by robust estimation instead of least squares (LS) estimation. In this paper, a modified RB RAIM detector based on a robust MM estimation with a higher detection performance under multi-outlier modes is presented. A fast subset selection method based on the characteristic slope that could reduce the number of subsets to be calculated is also presented. The experimental results show that the proposed algorithm maintains a more robust performance than the RB RAIM detector based on the LS estimator and M estimator with an IGG III function especially with the increase in the number of outliers. The proposed fast subset selection method can reduce the calculation time by at least 80%, demonstrating the practical application value of the algorithm.

scholarly journals ROBUST ESTIMATION AND INFERENCE FOR THRESHOLD MODELS WITH INTEGRATED REGRESSORS

2014 ◽  
Vol 31 (4) ◽  
pp. 778-810 ◽  
Author(s):  
Haiqiang Chen
Keyword(s):  
Least Squares ◽  
Robust Estimation ◽  
Model Identification ◽  
Selection Procedure ◽  
Generalized Least Squares ◽  
Threshold Effect ◽  
Threshold Models ◽  
Asymptotic Size ◽  
Ls Estimator ◽  
Error Terms

This paper studies the robust estimation and inference of threshold models with integrated regressors. We derive the asymptotic distribution of the profiled least squares (LS) estimator under the diminishing threshold effect assumption that the size of the threshold effect converges to zero. Depending on how rapidly this sequence converges, the model may be identified or only weakly identified and asymptotic theorems are developed for both cases. As the convergence rate is unknown in practice, a model-selection procedure is applied to determine the model identification strength and to construct robust confidence intervals, which have the correct asymptotic size irrespective of the magnitude of the threshold effect. The model is then generalized to incorporate endogeneity and serial correlation in error terms, under which, we design a Cochrane–Orcutt feasible generalized least squares (FGLS) estimator which enjoys efficiency gains and robustness against different error specifications, including both I(0) and I(1) errors. Based on this FGLS estimator, we further develop a sup-Wald statistic to test for the existence of the threshold effect. Monte Carlo simulations show that our estimators and test statistics perform well.

BDS/LEO ARAIM Subset Selection Method

2021 ◽  
Author(s):  
Ziyi Yang ◽  
Kun Fang ◽  
Zhiqiang Dan ◽  
Qiang Li ◽  
Zhipeng Wang ◽  
...  
Keyword(s):  
Subset Selection ◽  
Selection Method

scholarly journals Unsupervised Band Selection Method Based on Importance-Assisted Column Subset Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 517-527 ◽  
Author(s):  
Xiaoyan Luo ◽  
Zhiqi Shen ◽  
Rui Xue ◽  
Han Wan
Keyword(s):  
Subset Selection ◽  
Selection Method ◽  
Band Selection ◽  
Column Subset Selection

scholarly journals Least-squares method and deep learning in the identification and analysis of name-plates of power equipment

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yerong Zhong ◽  
Guoheng Ruan ◽  
Ehab Abozinadah ◽  
Jiaming Jiang
Keyword(s):  
Deep Learning ◽  
Least Squares ◽  
Least Squares Method ◽  
Feature Fusion ◽  
Learning Algorithm ◽  
Power Equipment ◽  
Practical Application ◽  
Deep Learning Algorithm ◽  
Character Feature ◽  
Histogram Feature

Abstract This article proposes a nameplate recognition method based on the least-squares method and deep learning algorithm character feature fusion. This method extracts the histogram of the edge direction of the character and constructs the histogram feature vector based on the wavelet transform deep learning algorithm. We use classifier training for the text recognition of the nameplate to segment the text into individual characters. Then, we extract the character features to build a template. Experiments prove that the algorithm meets the practical application needs of nameplate identification of power equipment and achieves the design goals.

scholarly journals Prediction of Number of Cases of 2019 Novel Coronavirus (COVID-19) Using Social Media Search Index

2020 ◽  
Vol 17 (7) ◽  
pp. 2365 ◽  
Author(s):  
Lei Qin ◽  
Qiang Sun ◽  
Yidan Wang ◽  
Ke-Fei Wu ◽  
Mingchih Chen ◽  
...  
Keyword(s):  
Social Media ◽  
Estimation Error ◽  
Subset Selection ◽  
Selection Method ◽  
Lasso Regression ◽  
Forward Selection ◽  
Optimal Method ◽  
Outbreak Areas ◽  
Novel Coronavirus ◽  
And Control

Predicting the number of new suspected or confirmed cases of novel coronavirus disease 2019 (COVID-19) is crucial in the prevention and control of the COVID-19 outbreak. Social media search indexes (SMSI) for dry cough, fever, chest distress, coronavirus, and pneumonia were collected from 31 December 2019 to 9 February 2020. The new suspected cases of COVID-19 data were collected from 20 January 2020 to 9 February 2020. We used the lagged series of SMSI to predict new suspected COVID-19 case numbers during this period. To avoid overfitting, five methods, namely subset selection, forward selection, lasso regression, ridge regression, and elastic net, were used to estimate coefficients. We selected the optimal method to predict new suspected COVID-19 case numbers from 20 January 2020 to 9 February 2020. We further validated the optimal method for new confirmed cases of COVID-19 from 31 December 2019 to 17 February 2020. The new suspected COVID-19 case numbers correlated significantly with the lagged series of SMSI. SMSI could be detected 6–9 days earlier than new suspected cases of COVID-19. The optimal method was the subset selection method, which had the lowest estimation error and a moderate number of predictors. The subset selection method also significantly correlated with the new confirmed COVID-19 cases after validation. SMSI findings on lag day 10 were significantly correlated with new confirmed COVID-19 cases. SMSI could be a significant predictor of the number of COVID-19 infections. SMSI could be an effective early predictor, which would enable governments’ health departments to locate potential and high-risk outbreak areas.

Recursive weighted partial least squares (rPLS): an efficient variable selection method using PLS

2013 ◽  
Vol 28 (5) ◽  
pp. 439-447 ◽  
Author(s):  
Åsmund Rinnan ◽  
Martin Andersson ◽  
Carsten Ridder ◽  
Søren Balling Engelsen
Keyword(s):  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Selection Method ◽  
Variable Selection Method

A Steganalysis framework based on CNN using the filter subset selection method

2020 ◽  
Vol 79 (27-28) ◽  
pp. 19875-19892
Author(s):  
Lan Wu ◽  
Xiaolei Han ◽  
Chenglin Wen ◽  
Binquan Li
Keyword(s):  
Subset Selection ◽  
Selection Method
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