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Efficiency comparisons show that the IRLS method does quite well in comparison to least squares or the traditional rank estimates in cases of moderate-tailed ...
Rank estimation of regression coefficients using iterated reweighted least squares · Influence Functions of Iteratively Reweighted Least Squares Estimators.
The finite sample performance of the rank estimator of regression coefficients obtained using the iteratively reweighted least squares (IRLS) of Sievers and ...
Efficiency comparisons show that the IRLS method does quite well in comparison to least squares or the traditional rank estimates in cases of moderate-tailed ...
Feb 15, 2024 · A novel iteratively reweighted accurate sparse low-rank (IRASLR) matrix estimation algorithm is proposed.
The method of iteratively reweighted least squares (IRLS) is used to solve certain optimization problems with objective functions of the form of a p-norm.
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On the Iteratively Reweighted Rank Regression Estimator ; Taylor & Francis, Colchester. Publication country: United Kingdom ; Article. Language: English ; Inist- ...
An iterated reweighted least squares algorithm is presented for the computation of the rank estimator. The method is simple in concept and can be carried out ...
I extend the concept of partial least squares (PLS) into the framework of generalized linear models. A spectroscopy example in a logistic regression ...
A diagnostic plot of the residuals r against the predicted values y ^ might show that as values of y ^ increases, the variance of r increases as well.