Least-squares variance component estimation
By Peter Teunissen, Alireza Amiri-Simkooei. 2008
324 citations recorded by the source corpus.
Least-squares variance component estimation (LS-VCE) is a simple, flexible and attractive method for the estimation of unknown variance and covariance components. LS-VCE is simple because it is based on the well-known principle of LS; it is flexible because it works with a user-defined weight matrix; and it is attractive because it allows one to directly apply the existing body of knowledge of LS theory. In this contribution, we present the LS-VCE method for different scenarios and explore its various properties. The method is described for three classes of weight matrices: a general weight matrix, a weight matrix from the unit weight matrix class; and a weight matrix derived from the class of elliptically contoured distributions. We also compare the LS-VCE method with some of the existing VCE methods. Some of them are shown to be special cases of LS-VCE. We also show how the existing body of knowledge of LS theory can be used to one’s advantage for studying various aspects of VCE, such as the precision and estimability of VCE, the use of a-priori variance component information, and the problem of nonlinear VCE. Finally, we show how the mean and the variance of the fixed effect estimator of the linear model are affected by the results of LS-VCE. Various examples are given to illustrate the theory.
Subfields: Applied Mathematics, Environmental Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/2e96f28f2c98d5e59450c13819668d29be05809c
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In Applied Mathematics
In Environmental Engineering
In Statistics and Probability
Citations in the corpus
- Least-squares variance component estimation: theory and GPS applications (2007, background): “elliptical distributions) of the original observations is required (Teunissen and Amiri-Simkooei, 2006).”
- On the nature of GPS draconitic year periodic pattern in multivariate position time series (2013, methodology): “Significant correlation of individual components has already been reported by Williams et al. [2004]; Amiri-Simkooei [2009].”
- Assessing receiver noise using GPS short baseline time series (2006, methodology): “The reader is referred to Teunissen and Amiri-Simkooei (2006), in particular, for issues such as formulation of the model, iteration and precision of the (co)variance component estimators.”
- Noise in multivariate GPS position time-series (2009, methodology): “LS-VCE has many attractive features for which we refer to Teunissen and Amiri-Simkooei (2008, 2006) and Amiri-Simkooei (2007).”