Estimating Locations of Perennial Streams in Idaho Using a Generalized Least-Squares Regression Model of 7-Day, 2-Year Low Flows

2009 ◽  
Author(s):  
Molly S. Wood ◽  
Alan Rea ◽  
Kenneth D. Skinner ◽  
Jon E. Hortness
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Generalized Least Squares ◽  
Least Squares Regression ◽  
Low Flows

scholarly journals A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions

2021 ◽  
Author(s):  
Simone Persiano ◽  
Jose Luis Salinas ◽  
Jery Russell Stedinger ◽  
William H. Farmer ◽  
David Lun ◽  
...  
Keyword(s):  
Least Squares ◽  
Generalized Least Squares ◽  
Least Squares Regression

scholarly journals Correction: Accuracy improvement of boron by molecular emission with a genetic algorithm and partial least squares regression model in laser-induced breakdown spectroscopy

2020 ◽  
Vol 35 (7) ◽  
pp. 1498-1498
Author(s):  
Zhihao Zhu ◽  
Jiaming Li ◽  
Yangmin Guo ◽  
Xiao Cheng ◽  
Yun Tang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Regression Model ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Laser Induced Breakdown Spectroscopy ◽  
Least Squares Regression ◽  
Accuracy Improvement ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Correction for ‘Accuracy improvement of boron by molecular emission with a genetic algorithm and partial least squares regression model in laser-induced breakdown spectroscopy’ by Zhihao Zhu et al., J. Anal. At. Spectrom., 2018, 33, 205–209, DOI: 10.1039/C7JA00356K.

A Generalized Discriminative Least Squares Regression Model

2017 ◽  
Author(s):  
Haoliang Yuan ◽  
Junjie Zheng ◽  
Fangyuan Xu ◽  
Loi Lei Lai ◽  
Weiyang Li ◽  
...  
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Least Squares Regression

Confidence Sets for the Coefficients Vector of a Linear Regression Model with Nonspherical Disturbances

1997 ◽  
Vol 13 (3) ◽  
pp. 406-429 ◽  
Author(s):  
Anoop Chaturvedi ◽  
Hikaru Hasegawa ◽  
Ajit Chaturvedi ◽  
Govind Shukla
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Generalized Least Squares ◽  
Least Squares Estimator ◽  
Regression Coefficients ◽  
Confidence Sets ◽  
Generalized Least Squares Estimator ◽  
Coverage Probabilities

In this present paper, considering a linear regression model with nonspherical disturbances, improved confidence sets for the regression coefficients vector are developed using the Stein rule estimators. We derive the large-sample approximations for the coverage probabilities and the expected volumes of the confidence sets based on the feasible generalized least-squares estimator and the Stein rule estimator and discuss their ranking.

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