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Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression
2012Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table.
Hervé, Abdi, Lynne J, Williams
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Weighted Least Squares Fitting Using Ordinary Least Squares Algorithms
Psychometrika, 1997A general approach for fitting a model to a data matrix by weighted least squares (WLS) is studied. This approach consists of iteratively performing (steps of) existing algorithms for ordinary least squares (OLS) fitting of the same model. The approach is based on minimizing a function that majorizes the WLS loss function.
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1998
This topic extends your study of least squares regression. You will examine the impact that a single observation can have on a regression analysis, learn how to use residual plots to indicate when the linear relationship is not appropriate, and discover transformation of variables as a way to use regression even when the relationship between the ...
Allan J. Rossman, Beth L. Chance
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This topic extends your study of least squares regression. You will examine the impact that a single observation can have on a regression analysis, learn how to use residual plots to indicate when the linear relationship is not appropriate, and discover transformation of variables as a way to use regression even when the relationship between the ...
Allan J. Rossman, Beth L. Chance
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Deterministic least squares filtering
Journal of Econometrics, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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SIAM Review, 1973
Summary: Certain elementary properties of the theory of least squares are presented from the point of view of complex stochastic processes. The development parallels the real case. We consider the least squares estimate (LSE), the best linear unbiased estimate (BLUE), the Markov estimate (ME), and the relationships among them.
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Summary: Certain elementary properties of the theory of least squares are presented from the point of view of complex stochastic processes. The development parallels the real case. We consider the least squares estimate (LSE), the best linear unbiased estimate (BLUE), the Markov estimate (ME), and the relationships among them.
openaire +1 more source