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GLS is employed to improve statistical efficiency and reduce the risk of drawing erroneous inferences, as compared to conventional least squares and weighted least squares methods. It was first described by Alexander Aitken in 1935. [1] It requires knowledge of the covariance matrix for the residuals. If this is unknown, estimating the ...
Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals.
In 2017, GLS acquired the California-based parcel delivery company Golden State Overnight (GSO) Delivery Service, as well as Postal Express. [4] [5] GSO was renamed GLS-US in December 2019. [6] In September 2018 GLS acquired the Canadian parcel delivery company Dicom, expanding operations in North America and Europe. [7]
By sitting GLS on top of genetic algorithm, Tung-leng Lau introduced the guided genetic programming (GGA) algorithm. It was successfully applied to the general assignment problem (in scheduling), processors configuration problem (in electronic design) and a set of radio-link frequency assignment problems (an abstracted military application).
Other estimation techniques besides FGLS were suggested for SUR model: [7] the maximum likelihood (ML) method under the assumption that the errors are normally distributed; the iterative generalized least squares (IGLS), where the residuals from the second step of FGLS are used to recalculate the matrix ^, then estimate ^ again using GLS, and ...
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In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression.The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
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