Outliers in process modeling and identification
By R. Pearson. IEEE Transactions on Control Systems Technology, 2002
506 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/818f580d15305d78c314036c7c19cc99a79bc3d3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Ken Goldberg
- Jeffrey Mahler
- Pieter Abbeel
- Sergey Levine
- Michael Laskey
- Sanjay Krishnan
- Adithyavairavan Murali
- Rui Alexandre de Matos Araújo
In Statistics and Probability
- Hancong Liu
- M. Franklin
- Ning Zhou
- Ken Goldberg
- Rui Alexandre de Matos Araújo
- Francisco Alexandre A. Souza
- John W. Pierre
- Bogdan Gabrys
Citations in the corpus
- Mathematical Models for Natural Gas Forecasting (2009, methodology): “We can use preliminary demand forecast models to detect anomalous data, which can be confirmed, and corrected or discarded before final models are developed [26, 28, 39, 40].”
- Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, methodology): “tion in statistical data [147]–[150] and has been introduced as an offline and online outlier detection procedure in [67].”
- Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression (2014, methodology): “Details of our specific data collection procedure can be found in Section V.”
- Data-driven Soft Sensors in the process industry (2009, methodology): “The most simple approach is the 3σ outlier detection algorithm (e.g. Lin et al., 2007; Pearson, 2002), which is based on univariate observations of the variable distributions.”
- Electromechanical Mode Online Estimation Using Regularized Robust RLS Methods (2008, background): “Outliers are values that significantly deviate from normal values [9].”
- Review of soft sensor methods for regression applications (2016, methodology): “liers, and it has shown to be practically effective in real applications [21; 19].”
- Robust RLS Methods for Online Estimation of Power System Electromechanical Modes (2007, background): “These issues include the following. 1) Outliers and missing data [ 9 ].”
- On-line outlier detection and data cleaning (2004, methodology): “The Hampel identifier is often considered extremely effective in practice (Perarson, 2002).”