Robust estimator for correlated observations based on bifactor equivalent weights

335 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Spatiotemporal filtering for regional GPS network in China using independent component analysis (2017, methodology): “However, intersite correlations that existwithin aGPSnetwork could be considerably reduced using a postprocessing approach (Yang et al. 2002).”
Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion (2014, methodology): “Two heuristic methods can be employed to make the filter robust, one of which inflates the covariance of the observation noise, see, e.g., Hajiyev and Soken (2012), Soken and Hajiyev (2010), Yang et al. (2002b), while the other re-scale the filter gain, see, e.g., Kim et al. (2008, 2009).”
Hodges–Lehmann estimates in deformation analyses (2013, methodology): “…was improved and developed (see, e.g., Andrews 1974; Huber 1981; Hampel et al. 1986) and it also became an important method of robust estimation employed in geodetic applications (see, Xu 1989, 2005; Yang et al. 2002; Wiśniewski 2009, 2010; Erenoglu and Hekimoglu 2010; Guo et al. 2011).”
GPS/BDS short-term ISB modelling and prediction (2016, methodology): “However, it is recommended to apply robust least-squares (Zhou 1989; Yang et al. 2002), on account of its outstanding capacity for data quality control (outliers detecting and repairing).”